Econometrics and economic statistics Books

Book Synopsis1. Optimization in Energy Markets 1.1 Classification of optimization problems1.1.1 Linear versus Nonlinear Problems 1.1.2 Deterministic versus Stochastic Problems 1.1.3 Static versus Dynamic Problems1.2 Optimal portfolio selection among different investment alternatives1.3 Energy Asset Optimization 1.3.1 Generation Asset Investment Valuation with Real Option Methodology 1.3.2 Generation, Transportation and Storage Asset Operational Optimization and Valuation 1.4 Energy Trading and Optimization 1.4.1 Asset allocation with Capital Constraints 1.4.2 Intraday trading 2. Optimization Methods2.1 Linear Optimization2.1.1 LP problems2.2 Nonlinear Optimization2.2.1 Unconstrained problem2.2.2 Constrained Problems with Equality Constraints2.2.3 Constrained Problems with Inequalities Constraints2.3 Pricing financial assets2.3.1 Pricing in energy markets2.3.2 Pricing in incomplete markets2.3.3 A motivating exampTrade ReviewEnergy markets are extremely competitive markets. Optimization of business decisions is fundamental for performance maximization. This book represents an excellent synthesis of optimization theory and practice applied to a wide and significant range of cutting-edge business problems characterizing power and natural gas markets.'- Domenico De Luca, CEO, Axpo Trading and Member of Executive Board Axpo Group'Optimization methods play an important role when making decisions and managing risk in today's liberalized energy markets. When planning a power plant or entering a structured gas contract, stochastic control is the key mathematical tool to assess the inherent risk. The authors of this book present an excellent account of the problems and methods for optimization in energy and power markets. The scope ranges from a rigorous theoretical analysis of the control problems, through numerical methods and to in-depth discussions of relevant practical case studies. This book is unique in providing a solid mathematical analysis of various optimization problems, yet never losing the market practice out of sight. It will be an invaluable reference for both academics and practitioners in power and gas markets.' - Fred Espen Benth, Professor of Mathematical Finance at the University of Oslo, Department of Mathematics and Deputy ManagerTable of Contents1. Optimization in Energy Markets 1.1 Classification of optimization problems1.1.1 Linear versus Nonlinear Problems 1.1.2 Deterministic versus Stochastic Problems 1.1.3 Static versus Dynamic Problems1.2 Optimal portfolio selection among different investment alternatives1.3 Energy Asset Optimization 1.3.1 Generation Asset Investment Valuation with Real Option Methodology 1.3.2 Generation, Transportation and Storage Asset Operational Optimization and Valuation 1.4 Energy Trading and Optimization 1.4.1 Asset allocation with Capital Constraints 1.4.2 Intraday trading 2. Optimization Methods2.1 Linear Optimization2.1.1 LP problems2.2 Nonlinear Optimization2.2.1 Unconstrained problem2.2.2 Constrained Problems with Equality Constraints2.2.3 Constrained Problems with Inequalities Constraints2.3 Pricing financial assets2.3.1 Pricing in energy markets2.3.2 Pricing in incomplete markets2.3.3 A motivating example: utility indifference pricing2.4 Deterministic Dynamic Programming2.5 Stochastic Dynamic Programming, discrete time2.5.1 A motivating example2.5.2 The general case2.5.3 Tree methods2.5.4 Least Square Monte Carlo methods2.5.5 Naïve Monte Carlo with Linear Programming2.6 Stochastic Dynamic Programming, continuous time2.6.1 The Hamilton-Jacobi-Bellman equation2.7 Deterministic numerical methods2.7.1 Finite Difference Method for HJB equation2.7.2 Boundary conditions2.8 Probabilistic numerical methods2.8.1 Tree methods, continuous time2.8.2 Computationally simple trees in dimension 12.8.3 Lattice of trees2.8.4 Monte Carlo methods3. Cases on Static Optimization3.1 Case A: investment alternatives3.2 Case B: Optimal generation mix for an electricity producer: a mean-variance approach3.3 Conclusions 4. Valuing project's exibilities using the diagrammatic approach4.1 Introduction4.2 Description of the Investment Problem4.3 Traditional evaluation Methods4.4 Modelling Electricity Price Dynamics4.5 Valuing Investment Flexibilities By Means Of The Lattice Approach4.5.1 Investment alternative A4.5.2 Investment alternative B4.5.3 Investment alternative C4.6 Conclusions5. Virtual Power Plant Contracts5.1 Introduction5.2 Valuation Problem5.2.1 Example6. Algorithms comparisonThe Swing Case6.1 Introduction6.2 Swing contracts6.2.1 Indexed strike price modelling for gas swing contracts6.2.2 The stochastic control problem6.2.3 Dynamic Programming6.3 Finite difference algorithm6.3.1 Boundary conditions6.3.2 The algorithm6.4 Least Square Monte Carlo algorithm6.4.1 The algorithm, and a reduction to one dimension6.5 Naïve Monte Carlo with Linear Programming6.6 Numerical Experiments6.6.1 Finite differences6.6.2 Least Square Monte Carlo6.6.3 One year contract6.7 Conclusions7. Storage contracts7.1 The contract7.2 The evaluation problem7.3 The optimal strategy (in the case of a physical gas storage)7.4 The implementation7.4.1 The gas cave7.4.2 The gas spot price7.4.3 The boundary conditions7.4.4 Numerical experiment, no-penalty case7.4.5 Numerical experiment, penalty case8. Optimal Trading Strategies in Intraday Power Markets8.1 Intraday power markets8.1.1 Intraday power price features8.1.2 Conclusions8.2 Optimal Algorithmic Trading in Auction-Based Intraday Power Markets8.2.1 The optimization problem8.2.2 Example: Italian intra-day market8.3 Optimal Algorithmic Trading in Continuous Time Power Markets8.3.1 The optimization problem8.3.2 Example: EPEX Spot market

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Book SynopsisThis book demonstrates how quantitative country-level investment strategies can be successfully employed to manage money in international markets.Table of ContentsPART I1. Value versus Growth: Is Buying Cheap Always a Bargain?2. Trend is your Friend: Momentum Investing3. Is Small Beautiful? Size Effect in Stock Markets4. Is Risk Always Rewarded? Low-Volatility Anomalies5. Is a Good Company a Good Investment? Quality InvestingPART II6. Testing Country Allocation Strategies7. A Short Primer on International Equity Investing8. Value-Oriented Country Selection9. Momentum Effect across Countries10. Small-Country Effect11. Risk-Based Country Asset Allocation12. Country Selection Based on Quality13. What Next? Combining and Improving Country Selection Strategies

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Book Synopsis* Presents five state--of--the--art survey papers on time series econometrics. * Presents a modern financial econometrics software package. * Surveys recent developments in the field. * Discusses the theoretical properties of the GARCH family of models.Table of Contents1. The Econometrics of Financial Time Series: Michael McAleer and Les Oxley. 2. Recent Theoretical Results for Time Series Models with GARCH Errors: W. K. Li, Shiqing Ling and Michael McAleer. 3. Bootstrapping Financial Time Series: Esther Ruiz and Lorenzo Pascual. 4. Measures of Fit for Rational Expectations Models: Tom Engsted. 5. Some Recent Developments in Futures Hedging: Donald Lien and Y. K. Tse. 6. Asset Pricing with Observable Stochastic Discount Factors: Peter Smith and Michael Wickens. 7. G@RCH 2.2: An Ox Package for Estimating and Forecasting Various ARCH Models: Sébastien Laurent and Jean-Philippe Peters.

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Book SynopsisIn this new and expanding area, Tony Lancaster's text is the first comprehensive introduction to the Bayesian way of doing applied economics. Uses clear explanations and practical illustrations and problems to present innovative, computer-intensive ways for applied economists to use the Bayesian method; Emphasizes computation and the study of probability distributions by computer sampling; Covers all the standard econometric models, including linear and non-linear regression using cross-sectional, time series, and panel data; Details causal inference and inference about structural econometric models; Includes numerical and graphical examples in each chapter, demonstrating their solutions using the S programming language and Bugs software Supported by online supplements, including Data Sets and Solutions to Problems, at Trade Review“This book conveys the revolution in Bayesian statistics brought about by modern computing and simulation methods from a perspective that econometricians will find familiar. It works through the implications for econometric practice using practical examples and accessible computer software. Graduate students in economics will find it highly accessible. Practitioners steeped in classical econometric methods will find much that is new, exciting, and useful here as well.” John Geweke, University of Iowa “Lancaster's text gives an impressive overview of the Bayesian point of view, and should prove a valuable resource to econometricians of all persuasions.” Werner Ploberger, University of Rochester Table of ContentsIntroduction. 1. The Bayesian Algorithm. 2. Prediction and Model Checking. 3. Linear Regression. 4. Bayesian Calculations. 5. Nonlinear Regression Models. 6. Randomized, Controlled and Observational Data. 7. Models for Panel Data. 8. Instrumental Variables. 9. Some Time Series Models. Appendix 1: A Conversion Manual. Appendix 2: Programming. Appendix 3: BUGS. Index

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Book SynopsisThis is the perfect (and essential) supplement for all econometrics classes--from a rigorous first undergraduate course, to a first master''s, to a PhD course. Explains what is going on in textbooks full of proofs and formulas Offers intuition, skepticism, insights, humor, and practical advice (dos and don'ts) Contains new chapters that cover instrumental variables and computational considerations Includes additional information on GMM, nonparametrics, and an introduction to wavelets Trade Review"The first edition of this book was a slim, non-technical introduction that would commonly be recommended to students struggling with their main course text. Over the years it has metamorphosed into a substantial volume in its own right, but the basic idea remains; light on technicalities but strong on insights, tips and quirky asides." (Times Higher Educations Supplement, February 2009)Table of ContentsPreface. Dedication. 1. Introduction. 1.1 What is Econometrics?. 1.2 The Disturbance Term. 1.3 Estimates and Estimators. 1.4 Good and Preferred Estimators. General Notes. Technical Notes. 2. Criteria for Estimators. 2.1 Introduction. 2.2 Computational Cost. 2.3 Least Squares. 2.4 Highest R2. 2.5 Unbiasedness. 2.6 Efficiency. 2.7 Mean Square Error (MSE). 2.8 Asymptotic Properties. 2.9 Maximum Likelihood. 2.10 Monte Carlo Studies. 2.11 Adding Up. General Notes. Technical Notes. 3. The Classical Linear Regression Model. 3.1 Textbooks as Catalogs. 3.2 The Five Assumptions. 3.3 The OLS Estimator in the CLR Model. General Notes. Technical Notes. 4. Interval Estimation and Hypothesis Testing. 4.1 Introduction. 4.2 Testing a Single Hypothesis: the t Test. 4.3 Testing a Joint Hypothesis: the F Test. 4.4 Interval Estimation for a Parameter Vector. 4.5 LR, W, and LM Statistics. 4.6 Bootstrapping. General Notes. Technical Notes. 5. Specification. 5.1 Introduction. 5.2 Three Methodologies. 5.3 General Principles for Specification. 5.4 Misspecification Tests/Diagnostics. 5.5 R2 Again. General Notes. Technical Notes. 6. Violating Assumption One: Wrong Regressors, Nonlinearities, and Parameter Inconstancy. 6.1 Introduction. 6.2 Incorrect Set of Independent Variables. 6.3 Nonlinearity. 6.4 Changing Parameter Values. General Notes. Technical Notes. 7. Violating Assumption Two: Nonzero Expected Disturbance. General Notes. 8. Violating Assumption Three: Nonspherical Disturbances. 8.1 Introduction. 8.2 Consequences of Violation. 8.3 Heteroskedasticity. 8.4 Autocorrelated Disturbances. 8.5 Generalized Method of Moments. General Notes. Technical Notes. 9. Violating Assumption Four: Instrumental Variable Estimation. 9.1 Introduction. 9.2 The IV Estimator. 9.3 IV Issues. General Notes. Technical Notes. 10. Violating Assumption Four: Measurement Errors and Autoregression. 10.1 Errors in Variables. 10.2 Autoregression. General Notes. Technical Notes. 11. Violating Assumption Four: Simultaneous Equations. 11.1 Introduction. 11.2 Identification. 11.3 Single-equation Methods. 11.4 Systems Methods. General Notes. Technical Notes. 12. Violating Assumption Five: Multicollinearity. 12.1 Introduction. 12.2 Consequences. 12.3 Detecting Multicollinearity. 12.4 What to Do. General Notes. Technical Notes. 13. Incorporating Extraneous Information. 13.1 Introduction. 13.2 Exact Restrictions. 13.3 Stochastic Restrictions. 13.4 Pre-test Estimators. 13.5 Extraneous Information and MSE. General Notes. Technical Notes. 14. The Bayesian Approach. 14.1 Introduction. 14.2 What Is a Bayesian Analysis?. 14.3 Advantages of the Bayesian Approach. 14.4 Overcoming Practitioners’ Complaints. General Notes. Technical Notes. 15. Dummy Variables. 15.1 Introduction. 15.2 Interpretation. 15.3 Adding Another Qualitative Variable. 15.4 Interacting with Quantitative Variables. 15.5 Observation-specific Dummies. General Notes. Technical Notes. 16. Qualitative Dependent Variables. 16.1 Dichotomous Dependent Variables. 16.2 Polychotomous Dependent Variables. 16.3 Ordered Logit/Probit. 16.4 Count Data. General Notes. Technical Notes. 17. Limited Dependent Variables. 17.1 Introduction. 17.2 The Tobit Model. 17.3 Sample Selection. 17.4 Duration Models. General Notes. Technical Notes. 18. Panel Data. 18.1 Introduction. 18.2 Allowing for Different Intercepts. 18.3 Fixed versus Random Effects. 18.4 Short Run versus Long Run. 18.5 Long, Narrow Panels. General Notes. Technical Notes. 19. Time Series Econometrics. 19.1 Introduction. 19.2 ARIMA Models. 19.3 VARs. 19.4 Error-correction Models. 19.5 Testing for Unit Roots. 19.6 Cointegration. General Notes. Technical Notes. 20. Forecasting. 20.1 Introduction. 20.2 Causal Forecasting/Econometric Models. 20.3 Time Series Analysis. 20.4 Forecasting Accuracy. General Notes. Technical Notes. 21. Robust Estimation. 21.1 Introduction. 21.2 Outliers and Influential Observations. 21.3 Guarding Against Influential Observations. 21.4 Artificial Neural Networks. 21.5 Non-parametric Estimation. General Notes. Technical Notes. 22. Applied Econometrics. 22.1 Introduction. 22.2 The Ten Commandments of Applied. Econometrics. 22.3 Getting the Wrong Sign. 22.4 Common Mistakes. 22.5 What Do Practitioners Need to Know?. General Notes. Technical Notes. 23. Computational Considerations. 23.1 Introduction. 23.2 Optimizing via a Computer Search. 23.3 Estimating Integrals via Simulation. 23.4 Drawing Observations from Awkward Distributions. General Notes. Technical Notes. Appendix A: Sampling Distributions, the. Foundation of Statistics. Appendix B: All about Variance. Appendix C: A Primer on Asymptotics. Appendix D: Exercises. Appendix E: Answers to Even-numbered Questions. Glossary. Bibliography. Name Index. Subject Index

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Book SynopsisThis book presents models and statistical methods for the analysis of recurrent event data. More general intensity-based models are also considered, as well as simpler models that focus on rate or mean functions.Trade ReviewFrom the Reviews: "The book provides many good real life examples to demonstrate application of the methods discussed....[it] is excellent for teaching an advanced class in statistics on this topic as it also contains many good exercises at the end of each chapter, some being extensions of the discussions." (Journal of Biopharmaceutical Statistics (JBS), Issue #5, 2008) "This book provides a timely and comprehensive review of methodologies for recurrent event data analysis and should be beneficial to Biometrics readers who are interested in recurrent events." "The strength of this book is its scope. It covers most of the methodology that is readily available for general use. ...Overall, we think this is a very good reference for recurrent event data analysis, especially because no other books provide a similar degree of coverage, and it would provide a nice textbook for a graduate-level course on the topic." (Biometrics, September 2008) "This book deals with processes generating multiple events over time. … The book comprises eight chapters, four appendices and a useful notational glossary. … it is directed to a much broader target readership, like social scientists, economists and industrial statisticians as well. … Many examples are used to illustrate and discuss the models and statistical methods in great detail. Techniques for estimation, testing and model checking are lucidly described … for a graduate course." (Harald Heinzl, Zentralblatt MATH, Vol. 1159, 2009) “…Every aspiring statistical researcher interested in recurrent events should have this book on his/her shelf as a great guide for learning the state-of-the-art stochastic models, frequentist (mostly estimating equation and asymptotic based) methods, and computational tools (including popular programs and routines). This is a very well-organized and comprehensive book on a very rapidly expanding area of research. As a mentor of PhD students, I myself will definitely recommend every graduate student interested in mastering recurrent events to read this book thoroughly to understand the current state of the literature as well as areas of future research and further development.” ( Journal of the American Statistical Association, Dec. 2009, Vol. 104, No. 488)Table of ContentsModels and Frameworks for Analysis of Recurrent Events.- Methods Based on Counts and Rate Functions.- Analysis of Gap Times.- General Intensity-Based Models.- Multitype Recurrent Events.- Observation Schemes Giving Incomplete or Selective Data.- OtherTopics.

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Book SynopsisTwo decades after its original publication, The Analysis of Household Surveys is being reissued with a new preface by its author, Sir Angus Deaton, recipient of the 2015 Nobel Prize in Economic Sciences.

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Book SynopsisA comprehensive guide to the statistical methods used in economics and quantitative economics. Acknowledged experts cover topics such as: * Semiparametic and non-parametic interference * Time series behaviour of commodity prices * Applications of Edgeworth expansions and quantitative methods in development economics.Table of Contents1. Specification Errors in Limited Dependent Variable Models: G. S. Maddala (Ohio State University). 2. The Optimality of Extended Score Tests With Applications to Testing for a Moving Average Unit Root: K. Tanaka (Hitotsubashi University). 3. Score Diagnostics for Linear Models Estimated by Two Stage Least Squares: J. M. Woolridge (Michigan State University). 4. Asymptotic Expansions in Statisics: A Review of Methods and Applications: R. N. Bhattacharya and M. L. Puri (Both Indiana University). 5. An Asymptotic Expansion for the Distribution of Test Criteria Which Are Asymptotically Distributed as Chi-Squared Under Contiguous Alternatives: A. Holly and L. Gardiol (Both Université de Lausanne). 6. Estimation in Semiparametric Models: O. Linton (Yale University). 7. Pooling Nonparametric Estimates of Regression Functions with a Similar Shape: C. A. P. Pinkse and P. M. Robinson (University of British Columbia and London School of Economics). 8. On the Theory of Testing Covariance Stationarity Under Moment Condition Failure: Peter C. B. Phillips and Mico Lorentan (Yale University and University of Wisconsin). 9. Pattern Identification of ARMA Models: T. W. Anderson (Stanford University). 10. Convergence Rates for Series Estimators: W. K. Newey (Massachusetts Institute of Technology). 11. Generalized Least Squares with Nonnormal Errors: C. L. Cavanagh and T. J. Rotherberg (Columbia University and University of California at Berkeley). 12. Factor Analysis Under More General Conditions with Reference to Heteroskedasticity of Unknown Form: John G. Cragg and Stephen G. Donald (University of British Columbia and Boston University). 13. Inference in Factor Models: Christian Gourieroux, A. Monfort and E. Renault (CRES, CREST, and Université des Sciences Sociales). 14. Expectations: Are They Rational, Adaptive or Naive?: Marc Nerlove and T. Schuerman (University of Maryland and AT & T Bell Laboratories). 15. Some Hypotheses About the Time Series Behaviour of Commodity Prices: P. K. Trivedi (Indiana University). 16. A Review of the Derivation and Calculation of Rao Distances with an Application to Portfolio Theory: U. Jensen (Christian-Albrechts Universitat).

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Book SynopsisIn Capital Theory and Equilibrium Analysis and Recursive Utility, Robert Becker and John Boyd have synthesized their previously unpublished work on recursive models.Table of ContentsList of Examples. Preface. Part I: The Recursive Utility Approach:. 1. Introduction. 2. What is a Recursive Utility Function?. 3. Why Study Recursive Utility?. 3.1. The Long Run Incidence of Capital Taxation. The Tax Model. Tax Incidence with the TAS Specification. Tax Incidence With the Epstein-Hynes Utility Specification. 3.2. The Impatience Problem. The Impatience Problem with an Epstein-Hynes Utility Function. 4. Recursive Utility and Commodity Spaces. 4.1 Diminishing Returns and Bounded Growth. 4.2 Nondecreasing Returns and Sustained Growth. Growth and Exogenous Technical Progress. Endogenous Growth Models. 4.3. Order Structures. Weak Separability of the Future from the Present. Partial Orders on the Commodity Space. 5. Conclusion. Part II: Commodity and Price Spaces:. 1. Introduction. 2. Commodity Spaces. 2.1. Order Properties. Free Disposal. 2.2 Topological Properties. Metric Spaces. Continuity. Compactness, Product Spaces, and the Tychonoff. Theorem. Connectedness. 2.3. Linear Topologies. Order Convergence. Contraction Mapping Theorems. 3. Commodity Price Dualities. 3.1. Duals and Hyperplanes. 3.2. Hahn-Banach Theorems. 3.3. Dual Pairs and Weak Topologies. 3.4. Order Duals. 4. Conclusion. Part III: Representation of Recursive Preferences:. 1. Introduction. 2. Preference Orders and Utility Theory. 3. Recursive Utility: The Koopmans Axioms. 3.1. The Axioms. 3.2. Biconvergence. 3.3. Recursive Preferences and Myopia. 4. Impatience, Discounting and Myopia. 4.1. Impatience and Time Perspective. 4.2. Myopia and the Continuity Axiom. 4.3. The Norm of Marginal Impatience Conditions. 5. Recursive Utility: The Aggregator. 5.1. Basic Properties of the Aggregator. 5.2. The Existence of Recursive Utility. 5.3. Aggregators Bounded From Below. 5.4. Unbounded Aggregators. 6. Conclusion. Part IV: Existence and Characterization of Optimal Paths:. 1. Introduction. 2. Fundamentals of Existence Theory. 2.1. A Simple Capital Accumulation Model. 2.2. The Weierstrass Theorem. 2.3. One-Sector TAS Existence Theory. 2.4. Extended Utilitarianism. 3. Multisector Capital Accumulation Model. 3.1. The von Neumann and Malinvaud Models. 3.2. The Feasible Correspondence. 4. The Existence and Sensitivity of Optimal Paths. 4.1. The Maximum Theorem. 4.2. Optimal Paths. 5. Recursive Dynamic Programming. 5.1. Dynamic Programming with TAS Utility. 5.2. Recursive Utility and Multisector Models. 5.3. Dynamic Programming and Extended Utilitarianism. 6. Characterization of Optimal Paths. 6.1. No-Arbitrage Conditions. 6.2. Complete Characterization of Optimal Paths. 7. Conclusion. Part V:. 1. Introduction. 2. One-Sector Models. 2.1. Stationary States in One-Sector Models. 2.2. Monotonicity and Turnpikes in TAS Models. 2.3. Monotonocity and Turnpikes in Recursive Models. 2.4. Growing Economies. 3. Steady States in Multisectoral Models. 3.1. Stationary Optimal Programs for Additive Utility. 3.2. Stationary Optimal Programs for Recursive Utility. 4. Stability of Multisectoral Models. 4.1. The Undiscounted Model. 4.2. The Visit Lemma. 4.3. Uniqueness of Steady States. 4.4. Local and Global Stability. 5. Cycles and Chaos in Optimal Growth. 5.1. The Existence of Cycles. 5.2. Chaotic Dynamics. 6. Conclusion. Part VI: Equivalence Principles and Dynamic Equilibria:. 1. Introduction. 2. Equivalence Principles for One-Sector Models. 2.1. The Perfect Foresight Equivalence Theorem. Perfect Foresight Competitive Equilibrium. The PFCE Equivalence Principle. 2.2. The Fisher Equivalence Theorem. 2.3. The Equivalence Theorem and the Transversality Condition. 2.4. Recursive Competitive Equilibrium and Recursive Utility. 3. Multisector Equivalence Principles. 3.1. The Portfolio Equilibrium Condition. 3.2. The Two-Sector Model's Equivalence Theorem. The Household Sector. The Production Sector. The Transformation Function. Perfect Foresight Equilibrium. The Optimal Growth Problem. The Equivalence Theorem. 3.3. Dynamics and The Two-Sector Model's Equivalence Theorem. 4. The Transversality Condition and the Hahn Problem. 5. Conclusion. Part VII: Comparative Dynamics:. 1. Introduction. 2. The Reduced-Form TAS Model. 2.1. Comparative Dynamics. 2.2. Comparative Dynamics for Oscillating Programs. 2.3. Comparative Dynamics and Capital Income Tax Reform. 3. A Primer of Lattice Programming. 3.1. More About Lattices. 3.2. An Introduction to Monotone Comparative Statistics. 3.3. Topkis's Theorems. 4. Lattice Programming and the Reduced-Form TAS Model. 4.1. The Monotonicity of Optimal Capital Policy Function. 4.2. The Capital Deepening Theorem. 5. Recursive Utility Models. 5.1. Recursive Utility, Monotonicity and Lattice Programming. 5.2. Increasing Impatience and Recursive Utility. 5.3. Capital Deepening and Recursive Utility. 6. Conclusion. Part VIII: Dynamic Competitive Equilibrium:. 1. Introduction. 2. Dynamic Economies. 2.1. Existence of Pareto Optima. 3. The Core. 3.1. Existence of Core Allocations. 3.2. Edgeworth Equilibria. 4. The Core and Competitive Equilibrium. 4.1. Core Equivalence. 4.2. The Welfare Theorems. 4.3. Representation of Equilibrium as Welfare Maximum. 5. Models with Very Heterogeneous Discounting. 5.1. Core Equivalence with Heterogeneous Discounting. 5.2. Specialization to Recursive Utility. 6. Conclusion. References. Index.

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Book SynopsisMany empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employ—a methodology that will yield a model that is both more informative and is a better representation of the data. This book outlines simple, practical procedures that can be used to specify a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book demonstrates how to test for model misspecification and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the researcher's confidence in the output generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results.

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Book SynopsisThis book deals with Business Analytics (BA) – an emerging area in modern business decision making. Business analytics is a data driven decision making approach that uses statistical and quantitative analysis along with data mining, management science, and fact-based data to measure past business performance to guide an organization in business planning and effective decision making. Business Analytics tools are also used to predict future business outcomes with the help of forecasting and predictive modeling.In this age of technology, massive amount of data are collected by companies. Successful companies use their data as an asset and use them for competitive advantage. Business Analytics is helping businesses in making informed business decisions and automating and optimizing business processes.Successful business analytics depends on the quality of data. Skilled analysts, who understand the technologies and their business, use business analytics tools as an organizational commitment to data-driven decision making.

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Book SynopsisThis book mainly focuses on why data analytics fails in business. It provides an objective analysis and root causes of the phenomenon, instead of abstract criticism of utility of data analytics. The author, then, explains in detail on how companies can survive and win the global big data competition, based on actual cases of companies. Having established the execution and performance-oriented big data methodology based on over 10 years of experience in the field as an authority in big data strategy, the author identifies core principles of data analytics using case analysis of failures and successes of actual companies. Moreover, he endeavors to share with readers the principles regarding how innovative global companies became successful through utilization of big data. This book is a quintessential big data analytics, in which the author's knowhow from direct and indirect experiences is condensed. How do we survive at this big data war in which Facebook in SNS, Amazon in e-commerce, Google in search, expand their platforms to other areas based on their respective distinct markets? The answer can be found in this book.

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Book SynopsisThe most comprehensive and most up-to-date one-volume reference work available on U.S. states.Salem Press's popular The Fifty States is designed to serve the needs of students, researchers, and the general public seeking historical and current statistical information on individual American states.

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Book SynopsisAsk not what AI can do for a company, rather what artificial intelligence may do to a company. How does a company successfully integrate artificial intelligence into its operations? What are the problems in doing so? And how does the introduction of AI into society change the answer to the first question? As companies delay or even cancel initiatives in artificial intelligence, Four Laws for the Artificially Intelligent redefines possibilities and offers leverage to turn AI visions into reality. It is a story of transformation: of people, of companies, and of artificial intelligence itself.The Four Laws is unique in its combination of stories and science illustrating how a technology competing with human consciousness is introduced and assimilated within a company. A work of creative nonfiction stretched on a frame of research, it is an essential trail guide for navigating the Industry Version 4.0 jungle in a search of the fruits of innovation.

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Book Synopsis

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Book Synopsis

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Book SynopsisVolume 27 of "Advances in Econometrics", entitled "Missing Data Methods", contains 16 chapters authored by specialists in the field, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; Consistent Estimation and Orthogonality; and Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling.Table of ContentsList of Contributors. Introduction. The Elephant in the Corner: A Cautionary Tale about Measurement Error in Treatment Effects Models. Recent Developments in Semiparametric and Nonparametric Estimation of Panel Data Models with Incomplete Information: A Selected Review. Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling. Efficient Estimation of the Dose–Response Function Under Ignorability Using Subclassification on the Covariates. Average Derivative Estimation with Missing Responses. Consistent Estimation and Orthogonality. On the Estimation of Selection Models when Participation is Endogenous and Misclassified. Efficient Probit Estimation with Partially Missing Covariates. Nonlinear Difference-in-Difference Treatment Effect Estimation: A Distributional Analysis. Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas. Estimating the Average Treatment Effect Based on Direct Estimation of the Conditional Treatment Effect. A Missing Variable Imputation Methodology with an Empirical Application. Missing Data Methods: Cross-sectional Methods and Applications. Advances in Econometrics. Advances in Econometrics. Copyright page.

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Book SynopsisVolume 27 of "Advances in Econometrics", entitled "Missing Data Methods", contains 16 chapters authored by specialists in the field, covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; Consistent Estimation and Orthogonality; and Likelihood-Based Estimators for Endogenous or Truncated Samples in Standard Stratified Sampling.Table of ContentsList of Contributors. Introduction. Markov Switching Models in Empirical Finance. Markov Switching in Portfolio Choice and Asset Pricing Models: A Survey. Volatility in Discrete and Continuous-Time Models: A Survey with New Evidence on Large and Small Jumps. Missing-Data Imputation in Nonstationary Panel Data Models. Missing Data Methods: Time-Series Methods and Applications. Advances in Econometrics. Advances in Econometrics. Copyright page.

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Book SynopsisThe study of sport in the economy presents a rich arena for the application of sharply focused microeconomics, macroeconomics and econometrics to both team and individual outcomes. This unique book offers a survey of recent research that follows the tradition of empirical and theoretical analysis of sport economics and econometrics.Including contributions by many of the leading experts in the field, the authors address four central branches, namely: competitive balance, labor relations, attendance and demand, and the economic impact of sport in communities.A wide range of topics is explored within these themes, including: the effect of uncertainty of outcome on attendance players' labor markets, wages and team performance variations in fan loyalty between teams and through time the determinants of soccer match attendance. Case studies of Major League Baseball, the National Football League (NFL) and college athletics in the US, the English Premier League, the Spanish football league and other (major and minor) European football leagues underpin the discussion.This important book will prove to be a fascinating and stimulating read for academics, researchers and students interested in the econometric analysis of sport.Contributors: G.M. Ahlfeldt, J. Baños, R. Baumann, D.J. Berri, R. Fort, B. Frick, J. García, W. Greene, B.R. Humphreys, L. Kahane, G. Kavetsos, S. Késenne, Y.H. Lee, N. Longley, V.A. Matheson, R.G. Noll, P. Rodríguez, R. Simmons, S. Szymanski, J. VroomanTable of ContentsContents: Foreword William Greene Preface PART I: COMPETITIVE BALANCE 1. Two to Tango: Optimum Competitive Balance in Professional Sports Leagues John Vrooman 2. Major League Baseball Attendance Time Series: League Policy Lessons Rodney Fort and Young Hoon Lee PART II: PLAYER’S LABOUR MARKETS 3. Wages, Transfers and the Variation of Team Performance in the English Premier League Stefan Szymanski 4. Team Wage Bills and Sporting Performance: Evidence from (Major and Minor) European Football Leagues Bernd Frick 5. Returns to Thuggery in the National Hockey League: The Effects of Increased Enforcement Leo Kahane, Neil Longley and Robert Simmons 6. Valuing the Blind Side: Pay and Performance of Offensive Linemen in the National Football League David J. Berri, Brad R. Humphreys and Robert Simmons PART III: ATTENDANCE 7. Endogeneity in Attendance Demand Models Roger G. Noll 8. Estimation of Temporal Variations in Fan Loyalty: Application of Multi-factor Models Young Hoon Lee 9. The Determinants of Football Match Attendance in Spanish Football: An Empirical Analysis Jaume García and Plácido Rodríguez PART IV: ECONOMIC IMPACT 10. Estimating Economic Impact Using Ex Post Econometric Analysis: Cautionary Tales Robert Baumann and Victor A. Matheson 11. Should I Wish on a Stadium? Measuring the Average Effect on the Treated Gabriel M. Ahlfeldt and Georgios Kavetsos 12. Spain and the FIFA World Cup 2018/2022: A Qualitative and Quantitative Analysis José Baños and Plácido Rodríguez Epilogue Plácido Rodríguez, Stefan Késenne and Jaume García Index

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Book SynopsisThis research collection offers a 34-article tour of recent advances and the current state of 5 important and booming areas of empirical methodology: Bayesian methods; modelling of temporal duration, dependence, and dynamics; network-analytic methodology; text, classification, and big-data analytic methods; methods for nonparametric and design-based causal inference. These prominent articles, written by leading scholars, break new ground and provide definitive statements of the current best practices in those respective areas. Together they describe the cutting-edge profile of modern empirical methodology for applied empirical analysis in political science. This is an essential resource for those studying and researching political methodology.Trade Review‘Few books have “political methodology” in their titles because the discipline is not yet well organized. This collection offers a concise picture of the field and puts landmark articles into perspective. It also covers very recent developments of statistical analysis in political science.’Table of ContentsContents: Research Review Robert J. Franzese Jr. PART I ADVANCES IN BAYESIAN METHODS 1. Simon Jackman (2000), ‘Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo’, American Journal of Political Science, 44 (2), April, 375–404 2. Joshua Clinton, Simon Jackman and Douglas Rivers (2004), ‘The Statistical Analysis of Roll Call Data’, American Political Science Review, 98 (2), May, 355–70 3. Richard Traunmüller, Andreas Murr and Jeff Gill (2015), ‘Modeling Latent Information in Voting Data with Dirichlet Process Priors’, Political Analysis, 23 (1), Winter, 1–20 4. Yair Ghitza and Andrew Gelman (2013), ‘Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups’, American Journal of Political Science, 57 (3), July, 762–76 5. Devin Caughey and Christopher Warshaw (2015), ‘Dynamic Estimation of Latent Opinion Using a Hierarchical Group-Level IRT Model’, Political Analysis, 23 (2), Spring, 197–211 PART II ADVANCES IN TIME-SERIES, TIME-SERIES-CROSS-SECTION/PANEL, AND EVENT-HISTORY/DURATION MODELLING 6. Janet M. Box–Steffensmeier and Bradford S. Jones (1997), ‘Time Is of the Essence: Event History Models in Political Science’, American Journal of Political Science, 41 (4), October, 1414–61 7. Frederick J. Boehmke, Daniel S. Morey and Megan Shannon (2006), ‘Selection Bias and Continuous-Time Duration Models: Consequences and a Proposed Solution’, American Journal of Political Science, 50 (1), January, 192–207 8. Jude C. Hays, Emily U. Schilling and Frederick J. Boehmke (2015), ‘Accounting for Right Censoring in Interdependent Duration Analysis’, Political Analysis, 23 (3) Summer, 400–14 9. Jude C. Hays and Robert J. Franzese, Jr. (2009), ‘A Comparison of the Small-Sample Properties of Several Estimators for Spatial-Lag Count Models’, paper submitted at the 2009 Summer Meeting of The Society of Political Methodology, New Haven, CT, USA, July 23–5, i, 1–27 10. Patrick T. Brandt, Michael Colaresi and John R. Freeman (2008), ‘The Dynamics of Reciprocity, Accountability, and Credibility’, Journal of Conflict Resolution, 52 (3), June, 343–74 11. Patrick T. Brandt, John R. Freeman and Philip A. Schrodt (2011), ‘Real Time, Time Series Forecasting of Inter- and Intra-State Political Conflict’, Conflict Management and Peace Science, 28 (1), February, 41–64 12. Daniel Stegmueller (2013), ‘Modeling Dynamic Preferences: A Bayesian Robust Dynamic Latent Ordered Probit Model’, Political Analysis, 21 (3), Summer, 314–33 13. Xun Pang (2014), ‘Varying Responses to Common Shocks and Complex Cross-Sectional Dependence: Dynamic Multilevel Modeling with Multifactor Error Structures for Time–Series Cross–Sectional Data’, Political Analysis, 22 (4), Autumn, 464–96 14. Robert J. Franzese, Jr. and Jude C. Hays (2008), ‘Empirical Models of Spatial Interdependence’ in Janet M. Box-Steffensmeier, Henry E. Brady and David Collier (eds), Oxford Handbook of Political Methodology, Oxford, UK: Oxford University Press, Part VII, Chapter 25, 570–604 15. Robert J. Franzese, Jr., Jude C. Hays and Scott J. Cook (2016), ‘Spatial- and Spatiotemporal-Autoregressive Probit Models of Interdependent Binary Outcomes’, Political Science Research and Methods, 4 (1), January, 151–73 PART III ADVANCES IN NETWORK ANALYSIS 16. B. A. Desmarais and S. J. Cranmer (2012), ‘Statistical Mechanics of Networks: Estimation and Uncertainty’, Physica A: Statistical Mechanics and it’s Applications, 391 (4), February, 1865–76 17. Bruce A. Desmarais and Skyler J. Cranmer (2012), ‘Micro-Level Interpretation of Exponential Random Graph Models with Application to Estuary Networks’, Policy Studies Journal, 40 (3), August, 402–34 18. Bruce A. Desmarais, Jeffrey J. Harden and Frederick J. Boehmke (2015), ‘Persistent Policy Pathways: Inferring Diffusion Networks in the American States’, American Political Science Review, 109 (2), May, 392–406 19. Jeff Gill and John R. Freeman (2013), ‘Dynamic Elicited Priors for Updating Covert Networks’, Network Science, 1 (1), April, 68–94 20. Jude C. Hays, Aya Kachi and Robert J. Franzese, Jr. (2010), ‘A Spatial Model Incorporating Dynamic, Endogenous Network Interdependence: A Political Science Application’, Statistical Methodology, 7 (3), May, 406–28 21. Robert J Franzese, Jr., Jude C. Hays and Aya Kachi (2012), ‘Modeling History Dependence in Network-Behavior Coevolution’, Political Analysis, 20 (2), Spring, 175–90 PART IV ADVANCES IN TEXT-ANALYTIC, CLASSIFICATION AND BIG-DATA METHODS 22. Phillip A Schrodt and David Van Brackle (2013) ‘Automated Coding of Political Event Data’ in V.S. Subrahmanian (ed.), Handbook of Computational Approaches to Counterterrorism, Chapter 2, New York, USA: Springer, 23–49 23. Justin Grimmer and Gary King (2011), ‘General Purpose Computer-Assisted Clustering and Conceptualization’, Proceedings of the National Academy of Sciences, 108 (7), February, 2643–50 24. Vito D’Orazio, Steven T. Landis, Glenn Palmer and Philip Schrodt (2014), ‘Separating the Wheat from the Chaff: Applications of Automated Document Classification Using Support Vector Machines’, Political Analysis, 22 (2), Spring, 224–42 25. Justin Grimmer and Brandon M. Stewart (2013), ‘Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts’, Political Analysis, 21 (3), Summer, 267–97 26. Martin Elff (2013), ‘A Dynamic State-Space Model of Coded Political Texts’, Political Analysis, 21 (2), Spring, 217–32 27. Christopher Lucas, Richard A. Nielson, Margaret E. Roberts, Brandon M. Stewart, Alex Storer and Dustin Tingley (2015), ‘Computer Assisted Text Analysis for Comparative Politics’, Political Analysis, 23 (2), Spring, 254–77 PART V ADVANCES IN NONPARAMETRIC & DESIGN-BASED INFERENCE METHODS 28. Jasjeet S. Sekhon (2008), ‘The Neyman-Rubin Model of Causal Inference and Estimation via Matching Methods’, in Janet M. Box-Steffensmeier, Henry E. Brady and David Collier (eds), Oxford Handbook of Political Methodology, Part VI, Chapter 11, Oxford, UK: Oxford University Press, 271–99 29. Jasjeet Sekhon and Rocío Titiunik (2012), ‘When Natural Experiments Are Neither Natural Nor Experiments’, American Political Science Review, 106 (1), February, 35–57 30. Peter M. Aronow and Allison Carnegie (2013), ‘Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable’, Political Analysis, 21 (4), Autumn, 492–506 31. Kosuke Imai, Luke Keele, Dustin Tingley and Teppai Yamamoto (2011), ‘Unpacking the Black Box of Causality: Learning about Casual Mechanisms from Experimental and Observational Studies’, American Political Science Review, 105 (4), November, 765–89 32. Kosuke Imai and Marc Ratkovic (2013), ‘Estimating Treatment Effect Heterogeneity in Randomized Program Evaluation’, Annals of Applied Statistics, 7 (1), 443–70 33. Luke Keele and Rocío Titiunik (2016), ‘Natural Experiments Based on Geography’, Political Science Research and Methods, 4 (1), January, 65–95 34. Luke Keele, Rocío Titiunik and Jose Zubizarreta (2015), ‘Enhancing a Geographic Regression Discontinuity Design Through Matching To Estimate the Effect of Ballot Initiatives on Voter Turnout’, Journal of the Royal Statistical Society: Statistics in Society, Series A, 178 (1), 223–39 [17] Index

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Book SynopsisThe Handbook of Experimental Game Theory offers a comprehensive analysis of the field, discussing foundational topics that are at the core of applied game theory. It highlights the nuances that scientific experiments have delivered to our understanding of strategic interactions among decision makers. Leading experts explore methodological considerations and games of complete and incomplete information to offer new directions for research in experimental game theory. Chapters demonstrate transformative behavioral research focused on classic topics in game theory such as cooperation and coordination games. Taking a scientific approach to the study of game theory, this innovative Handbook provides an insight into laboratory and field experiments that test game theoretic propositions and suggests new ways of modeling strategic behavior. It takes a forward-thinking position, addressing the challenges inherent in innovations surrounding the measurement of strategic behavior using experimental methods. This Handbook will prove to be a valuable resource for scholars and students who are looking to gain a broader understanding of experimental game theory and how to contribute to its advancement. It will also be of particular interest to researchers in experimental and behavioral economics.Trade Review'This Handbook is a must-have resource for experimental economists and game theorists. It consists of authoritative contributions from top researchers in the areas it covers. The topics range from methodology to surveys of important and active research in experimental game theory. The Handbook is both rigorous in its treatment of the topics as well as accessible to readers not familiar with the areas of coverage.' --Charles Noussair, University of Arizona, US'Every game theorist and experimental economist should have this book and use it to analyze data, design experiments, and understand their results.' --Elizabeth Hoffman, Iowa State University, USTable of ContentsContents: Introduction to the Handbook of Experimental Game Theory 1 C. Mónica Capra, Rachel T.A. Croson, Mary L. Rigdon and Tanya S. Rosenblat PART I A SAMPLING OF METHODOLOGICAL INNOVATIONS 1 Stochastic game theory for social science: a primer on quantal response equilibrium 8 Jacob K. Goeree, Charles A. Holt and Thomas R. Palfrey 2 The experimetrics of depth-of-reasoning models 48 Peter G. Moffatt 3 The process of choice in games 69 Giorgio Coricelli, Luca Polonio and Alexander Vostroknutov 4 Games with continuous-time experimental protocols 95 Alexander L. Brown and Daniel G. Stephenson 5 Bargaining in the field 125 Marco Castillo and Ragan Petrie PART II EXPERIMENTS ON STATIC AND DYNAMIC GAMES OF COMPLETE INFORMATION 6 Recent advances in experimental coordination games 149 David J. Cooper and Roberto A. Weber 7 Public goods, norms and cooperation 184 Marie Claire Villeval 8 Cooperation among strangers with and without a monetary system 213 Maria Bigoni, Gabriele Camera and Marco Casari 9 Game-theoretic accounts of social norms: the role of normative expectations 241 Cristina Bicchieri and Alessandro Sontuoso 10 Strategies used by non-human primates in dynamic games 256 Mackenzie F. Webster, Julia Watzek and Sarah F. Brosnan 11 Reciprocity in games with unknown types 271 Garret Ridinger and Michael McBride 12 Behavioral rules 289 Carlos Alós-Ferrer and Johannes Buckenmaier PART III EXPERIMENTS ON STATIC AND DYNAMIC GAMES OF INCOMPLETE INFORMATION 13 Strategic information transmission: a survey of experiments and theoretical foundations 311 Andreas Blume, Ernest K. Lai and Wooyoung Lim 14 Communication and information in games of collective decision: a survey of experimental results 348 César Martinelli and Thomas R. Palfrey 15 Voting game experiments with incomplete information: a survey 376 Jens Großer 16 Experiments in market design 399 Siqi Pan Index

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Book SynopsisImad Moosa challenges convention with this comprehensive and compelling critique of the limitations and abuses of econometrics, condemning the common practices of misapplied statistical methods in both economics and finance. After reviewing the Keynesian, Austrian and mainstream criticisms of econometrics, it is demonstrated that by using standard econometric techniques, methods and models can be manipulated to produce any desired result. These hazardous analyses may then be relied upon to support flawed policy recommendations, ideological beliefs and private interests. Moosa proposes that the way forward should instead be to rely on clear thinking, intuition and common sense rather than continue with the reliance upon econometrics. The mathematization of economics has limited the accessibility and participation in economic discussion by making the area into a complex `science' when it should not be. Appealing to both academics and practitioners of economics and finance, this book serves to challenge the acceptance of econometrics as offering trustworthy analysis. Any individual interested in this sort of empirical work will find this book a captivating read on the limitations of econometrics.Trade Review'Professor Moosa argues that the dominance of econometrics has damaged economics as a discipline. I entirely agree. Moosa is refreshingly blunt: ''(econometrics) is a con art that can be used to prove almost anything''. Any applied economist concerned about the low regard in which our discipline is held should read this lively and hard-hitting critique.' --Peter Swann, Nottingham University Business School, UK'Econometrics as a Con Art is the best book I have read for a long time. Economists are fond of hailing econometrics as a major success, but it has achieved nothing of value. The truth is that beneath its ''sciency'' veneer economists regularly use econometrics to produce stir-fry regressions that can prove any nonsense. They can prove that eating margarine leads to more divorces or that more guns lead to fewer homicides. They can use it to prove or debunk any proposition, they can prove the obvious, they can prove what cannot be true and they can test the untestable. In this wonderful book Imad Moosa brilliantly debunks this industry for the junk science scam that it is. Every economist should read it but please not as a how-to manual. It is high time economists took the con out of econometrics; we have all suffered long-enough.' --Kevin Dowd, Durham University, UKTable of ContentsContents: 1. The Nature and Evolution of Econometrics 2. Components, Functions and Related Disciplines 3. Econometrics as a Science 4. The Laws of Economics and Science 5. Econometric Analysis: Loopholes and Shortcomings 6. Criticism of Econometrics: Keynes, Leamer, Lucas and the Austrians 7. Stir-Fry Regressions as a Con Job 8. Cointegration Analysis: Principles and Fallacies 9. Cointegration Analysis: Applications and Illustrations 10. Sensitivity and Insensitivity of Empirical Results 11. The Forecasting Fiasco 12. Concluding Thoughts Index

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Book SynopsisThis book offers a unique and insightful econometric evaluation of the policies used to fight transnational terrorism between 1990 and 2014. It uses the tools of modern economics, game theory and structural econometrics to analyze the roles of foreign aid, educational capital, and military intervention. Jean-Paul Azam and Veronique Thelen analyze panel data over 25 years across 124 countries. They prove that foreign aid plays a key role in inducing recipient governments to protect the donors' political and economic interests within their sphere of influence. Demonstrating that countries endowed with better educational capital export fewer terrorist attacks, they also illustrate that, in contrast, military intervention is counter-productive in abating terrorism. Recognizing the strides taken by the Obama administration to increase the role of foreign aid and reduce the use of military interventions, this book shows the significant impact this has had in reducing the number of transnational terrorist attacks per source country, and suggests further developments in this vein. Practical and timely, this book will be of particular interest to students and scholars of economics and political science, as well as those working on the wider issue of terrorism. Presenting a series of new findings, the book will also appeal to international policy makers and government officials.Table of ContentsContents: 1. Introduction and Overview Part 1: Getting the Questions Right 2: Targets and Perpetrators of Transnational Terrorist Attacks 3: Why Suicide Terrorists Get Educated 4: Aid and Military Intervention in a Model of Delegated Protection Part 2: Empirical Answers 5: Testing the Impacts of Foreign Aid and Military Interventions 6: Estimating the Speed of Terrorist Responses. 7: The Problem of Imported Attacks 8. General Conclusion Bibliography Index

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Book SynopsisVolatility ranks among the most active and successful areas of research in econometrics and empirical asset pricing finance over the past three decades. This research review studies and analyses some of the most influential published works from this burgeoning literature, both classic and contemporary. Topics covered include GARCH, stochastic and multivariate volatility models as well as forecasting, evaluation and high-frequency data. This insightful review presents and discusses the most important milestones and contributions that helped pave the way to today's understanding of volatility.Trade Review‘This anthology of classical and recent articles will be very useful to all researchers and students interested in the various econometric aspects of volatility measurement, modeling, forecasting, and their applications in finance. The introductory chapter by Andersen and Bollerslev - well-known top experts in the field - offers the needed guidance to fully benefit from the collected papers.’Table of ContentsContents: Acknowledgements Introduction Torben G. Andersen and Tim Bollerslev PART I PROLOGUE 1. Fischer Black (1976), ‘Studies of Stock Price Volatility Changes’, Proceedings of the 1976 Meetings of the American Statistical Association, Business and Economic Statistics Section, 177–81 PART II GARCH MODELS 2. Robert F. Engle (1982), ’Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation’, Econometrica, 50 (4), July, 987–1007 3. Tim Bollerslev (1986), ‘Generalized Autoregressive Conditional Heteroskedasticity’, Journal of Econometrics, 31 (3), April, 307–27 4. Robert F. Engle, David M. Lilien and Russell P. Robins (1987), ‘Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M Model’, Econometrica, 55 (2), March, 391–407 5. Kenneth R. French, G. William Schwert and Robert F. Stambaugh (1987), ‘Expected Stock Returns and Volatility’, Journal of Financial Economics, 19 (1), September, 3–29 6. G. William Schwert (1989), ‘Why Does Stock Market Volatility Change Over Time?’, Journal of Finance, XLIV (5), December, 1115–53 7. Tim Bollerslev (1987), ‘A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return’, Review of Economics and Statistics, 69 (3), August, 542–7 8. Tim Bollerslev and Jeffrey M. Wooldridge (1992), ‘Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariances’, Econometric Reviews, 11 (2), 143–72 9. Alexander J. McNeil and Rüdiger Frey (2000), ‘Estimation of Tail-Related Risk Measures for Heteroscedastic Financial Time Series: An Extreme Value Approach’, Journal of Empirical Finance: Special Issue on Risk Management, 7 (3–4), November, 271–300 10. Lawrence R. Glosten, Ravi Jagannathan and David E. Runkle (1993), ‘On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks’, Journal of Finance, XLVIII (5), December, 1779–801 11. Jean-Michel Zakoian (1994), ‘Threshold Heteroskedastic Models’, Journal of Economic Dynamics and Control, 18 (5), September, 931–55 12. Daniel B. Nelson (1991), ‘Conditional Heteroskedasticity in Asset Returns: A New Approach’, Econometrica, 59 (2), March, 347–70 13. Zhuanxin Ding, Clive W. J. Granger and Robert F. Engle (1993), ‘A Long Memory Property of Stock Market Returns and a New Model’, Journal of Empirical Finance, 1 (1), June, 83–106 14. Richard T. Baillie, Tim Bollerslev and Hans Ole Mikkelsen (1996), ‘Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity’, Journal of Econometrics, 74 (1), September, 3–30 15. Peter R. Hansen and Asger Lunde (2005), ‘A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?’, Journal of Applied Econometrics, 20 (7), December, 873–89 PART III STOCHASTIC VOLATILITY MODELS 16. Peter K. Clark (1973), ‘A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices’, Econometrica, 41 (1), January, 135–55 17. George E. Tauchen and Mark Pitts (1983), ‘The Price Variability-Volume Relationship on Speculative Markets’, Econometrica, 51 (2), March, 485–505 18. Torben G. Andersen (1996), ‘Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility’, Journal of Finance, LI (1), March, 169–204 19. Stephen J. Taylor (1982), ‘Financial Returns Modelled by the Product of Two Stochastic Processes – A Study of Daily Sugar Prices, 1961–79’, in Oliver D. Anderson (ed.), Time Series Analysis: Theory and Practice 1: Proceedings of the International Conference Held at Valencia, Spain, June 1981, Amsterdam, the Netherlands: North-Holland Publishing Company, 203–26 20. Torben G. Andersen (1994), ‘Stochastic Autoregressive Volatility: A Framework for Volatility Modeling’, Mathematical Finance, 4 (2), April, 75–102 21. C. Gourieroux, A. Monfort and E. Renault (1993), ‘Indirect Inference’, Journal of Applied Econometrics, Supplement: Special Issue on Econometric Inference Using Simulation Techniques, 8 (S1), December, S85–S118 22. A. Ronald Gallant and George Tauchen (1996), ‘Which Moments to Match?’, Econometric Theory, 12 (4), October, 657–81 23. Torben G. Andersen and Jesper Lund (1997), ‘Estimating Continuous-Time Stochastic Volatility Models of the Short-Term Interest Rate’, Journal of Econometrics, 77 (2), April, 343–77 24. Eric Jacquier, Nicholas G. Polson and Peter E. Rossi (1994), ‘Bayesian Analysis of Stochastic Volatility Models’, Journal of Business and Economic Statistics, 12 (4), October, 371–89 25. Nour Meddahi and Eric Renault (2004), ‘Temporal Aggregation of Volatility Models’, Journal of Econometrics: Dynamic Factor Models, 119 (2), April, 355–79 26. Fabienne Comte and Eric Renault (1998), ‘Long Memory in Continuous-Time Stochastic Volatility Models’, Mathematical Finance, 8 (4), October, 291–323 27. Laurent Calvet and Adlai Fisher (2002), ‘Multifractality in Asset Returns: Theory and Evidence’, Review of Economics and Statistics, LXXXIV (3), August, 381–406 PART IV MULTIVARIATE VOLATILITY MODELS 28. Tim Bollerslev, Robert F. Engle and Jeffrey M. Wooldridge (1988), ‘A Capital Asset Pricing Model with Time-varying Covariances’, Journal of Political Economy, 96 (1), February, 116–31 29. Robert F. Engle and Kenneth F. Kroner (1995), ‘Multivariate Simultaneous Generalized ARCH’, Econometric Theory, 11 (1), February, 122–50 30. Francis X. Diebold and Marc Nerlove (1989), ‘The Dynamics of Exchange Rate Volatility: A Multivariate Latent Factor ARCH Model’, Journal of Applied Econometrics, 4 (1), January–March, 1–21 31. Tim Bollerslev (1990), ‘Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model’, Review of Economics and Statistics, 72 (3), August, 498–505 32. Andrew Harvey, Esther Ruiz and Neil Shephard (1994), ‘Multivariate Stochastic Variance Models’, Review of Economic Studies, 61 (2), April, 247–64 33. Robert Engle (2002), ‘Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models’, Journal of Business and Economic Statistics, 20 (3), July, 339–50 34. Andrew J. Patton (2006), ‘Modelling Asymmetric Exchange Rate Dependence’, International Economic Review, 47 (2), May, 527–56 Volume II Contents Acknowledgements Introduction An introduction to both volumes by the editors appears in Volume I PART I OPTIONS AND VOLATILITY 1. Henry A. Latané and Richard J. Rendleman, Jr. (1976), ‘Standard Deviations of Stock Price Ratios Implied in Option Prices’, Journal of Finance, XXXI (2), May, 369–81, Correction 2. John Hull and Alan White (1987), ‘The Pricing of Options on Assets with Stochastic Volatilities’, Journal of Finance, XLII (2), June, 281–300 3. Steven L. Heston (1993), ‘A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options’, Review of Financial Studies, 6 (2), April, 327–43 4. Jin-Chuan Duan (1995), ‘The GARCH Option Pricing Model’, Mathematical Finance, 5 (1), January, 13–32 5. David S. Bates (1996), ‘Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options’, Review of Financial Studies, 9 (1), January, 69–107 6. Bjørn Eraker, Michael Johannes and Nicholas Polson (2003), ‘The Impact of Jumps in Volatility and Returns’, Journal of Finance, LVIII (3), June, 1269–300 7. Mark Britten-Jones and Anthony Neuberger (2000), ‘Option Prices, Implied Price Processes, and Stochastic Volatility’, Journal of Finance, LV (2), April, 839–66 8. Peter Carr and Liuren Wu (2009), ‘Variance Risk Premiums’, Review of Financial Studies, 22 (3), March, 1311–41 9. Tim Bollerslev, George Tauchen and Hao Zhou (2009), ‘Expected Stock Returns and Variance Risk Premia’, Review of Financial Studies, 22 (11), November, 4463–92 PART II VOLATILITY FORECASTING AND EVALUATION 10. Daniel B. Nelson (1992), ‘Filtering and Forecasting with Misspecified ARCH Models I: Getting the Right Variance with the Wrong Model’, Journal of Econometrics, 52 (1–2), April–May, 61–90 11. Dean P. Foster and Dan B. Nelson (1996), ‘Continuous Record Asymptotics for Rolling Sample Variance Estimators ’, Econometrica, 64 (1), January, 139–74 12. Torben G. Andersen and Tim Bollerslev (1997), ‘Intraday Periodicity and Volatility Persistence in Financial Markets’, Journal of Empirical Finance: High Frequency Data, Part 1, 4 (2–3), June, 115–58 13. Torben G. Andersen and Tim Bollerslev (1998), ‘Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts’, International Economic Review: Symposium on Forecasting and Empirical Methods in Macroeconomics and Finance, 39 (4), November, 885–905 14. Torben G. Andersen, Tim Bollerslev and Nour Meddahi (2004), ‘Analytical Evaluation of Volatility Forecasts,’ International Economic Review, 45 (4), November, 1079–110 15. Andrew J. Patton (2011), ‘Volatility Forecast Comparison Using Imperfect Volatility Proxies’, Journal of Econometrics: Realized Volatility, 160 (1), January, 246–56 16. Jeff Fleming, Chris Kirby and Barbara Ostdiek (2003), ‘The Economic Value of Volatility Timing Using “Realized” Volatility’, Journal of Financial Economics, 67 (3), March, 473–509 PART III HIGH-FREQUENCY DATA AND REALIZED VOLATILITIES 17. Torben G. Andersen, Tim Bollerslev, Francis X. Diebold and Paul Labys (2001), ‘The Distribution of Realized Exchange Rate Volatility’, Journal of the American Statistical Association, 96 (453), March, 42–55, Correction 18. Torben G. Andersen, Tim Bollerslev, Francis X. Diebold and Paul Labys (2003), ‘Modeling and Forecasting Realized Volatility’, Econometrica, 71 (2), March, 579–625 19. Fulvio Corsi (2009), ‘A Simple Approximate Long-Memory Model of Realized Volatility’, Journal of Financial Econometrics, 7 (2), Spring, 174–96 20. Eric Ghysels, Pedro Santa-Clara and Rossen Valkanov (2006), ‘Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies’, Journal of Econometrics, 131 (1–2), March–April, 59–95 21. Torben G. Andersen, Tim Bollerslev, Francis X. Diebold and Clara Vega (2003), ‘Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange’, American Economic Review, 93 (1), March, 38–62 22. Ole E. Barndorff-Nielsen and Neil Shephard (2004), ‘Power and Bipower Variation with Stochastic Volatility and Jumps’, Journal of Financial Econometrics, 2 (1), January, 1–37 23. Torben G. Andersen, Tim Bollerslev and Francis X. Diebold (2007), ‘Roughing it up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility’, Review of Economics and Statistics, 89 (4), November, 701–20 24. Cecilia Mancini (2009), ‘Non-parametric Threshold Estimation for Models with Stochastic Diffusion Coefficient and Jumps’, Scandinavian Journal of Statistics, 36 (2), June, 270–96 25. Peter R. Hansen and Asger Lunde (2006), ’Realized Variance and Market Microstructure Noise’, Journal of Business and Economic Statistics, 24 (2), April, 127–61 26. Bin Zhou (1996), ‘High-Frequency Data and Volatility in Foreign-Exchange Rates’, Journal of Business and Economic Statistics, 14 (1), January, 45–52 27. Ole E. Barndorff-Nielsen, Peter Reinhard Hansen, Asger Lunde and Neil Shephard (2008), ‘Designing Realized Kernels to Measure the Ex Post Variation of Equity Prices in the Presence of Noise’, Econometrica, 76 (6), November, 1481–536 28. Lan Zhang, Per A. Mykland and Yacine Aït-Sahalia (2005), ‘A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data’, Journal of the American Statistical Association, 100 (472), December, 1394–411 29. Jean Jacod, Yingying Li, Per A. Mykland, Mark Podolskij and Mathias Vetter (2009), ‘Microstructure Noise in the Continuous Case: The Pre-Averaging Approach’, Stochastic Processes and their Applications, 119 (7), July, 2249–76 30. Thomas W. Epps (1979), ‘Comovements in Stock Prices in the Very Short Run’, Journal of the American Statistical Association, 74 (366a), June, 291–8 31. Ole E. Barndorff-Nielsen and Neil Shephard (2004), ‘Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics’, Econometrica, 72 (3), May, 885–925 Index

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Book SynopsisWorld Statistics on Mining and Utilities 2018 provides a unique biennial overview of the role of mining and utility activities in the world economy. This extensive resource from UNIDO provides detailed time series data on the level, structure and growth of international mining and utility activities by country and sector. Country level data is clearly presented on the number of establishments, employment and output of activities such as: coal, iron ore and crude petroleum mining as well as production and supply of electricity, natural gas and water.This unique and comprehensive source of information meets the growing demand of data users who require detailed and reliable statistical information on the primary industry and energy producing sectors. The publication provides internationally comparable data to economic researchers, development strategists and business communities who influence the policy of industrial development and its environmental sustainability.Table of ContentsAbout this Publication Introduction Part I: Summary Tables Part II: Country Tables

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Book SynopsisWritten in a comprehensive yet accessible style, this Handbook introduces readers to a range of modern empirical methods with applications in microeconomics, illustrating how to use two of the most popular software packages, Stata and R, in microeconometric applications. International contributors expertly investigate the development of advanced methods driven by the accumulation of numerous data sets at the level of individuals, households and firms, and by an increase in the capacity and speed of computers. The Handbook highlights that, while the more traditional empirical methods were largely limited to establishing correlations, these new methods aim to uncover causality. Examination of these advances shows new possibilities for applied research in microeconomics in the estimation of sophisticated structural models and the evaluation of policy interventions. This insightful Handbook is a must-read for graduate students and instructors in applied microeconomics as well as researchers in government departments and academia pursuing modern advanced methods of policy evaluation and data analysis.Table of ContentsContents: Introduction to the Handbook of Research Methods and Applications in Empirical Microeconomics ix Nigar Hashimzade and Michael A. Thornton PART I ECONOMETRIC METHODS IN MICROECONOMICS 1 Linear dynamic panel data models 2 Ryo Okui 2 Spatial autoregressive nonlinear models in R with an empirical application in labour economics 23 Anna Gloria Billé 3 Econometric analyses of auctions: a selective review 42 Tong Li and Xiaoyong Zheng 4 An introduction to flexible methods for policy evaluation 82 Martin Huber PART II HOUSEHOLDS, BUSINESSES AND SOCIETIES 5 Econometric models of fertility 113 Alfonso Miranda and Pravin K. Trivedi 6 Measuring discrimination in the labour market 155 Emmanuel Duguet 7 Microeconomic models for designing and evaluating tax-transfer systems 195 Ugo Colombino 8 Bounds on counterfactuals in semiparametric discrete-choice models 223 Khai X. Chiong, Yu-Wei Hsieh and Matthew Shum 9 Bank performance analysis 238 Natalya Zelenyuk and Valentin Zelenyuk 10 Empirical methods in social epidemiology 280 Christopher F. Baum PART III POLICY EVALUATION AND CAUSALITY 11 Policy evaluation using causal inference methods 294 Denis Fougère and Nicolas Jacquemet 12 Regression discontinuity designs in policy evaluation 325 Otávio Bartalotti, Marinho Bertanha and Sebastian Calonico 13 Measuring the effect of health events in the labour market 359 Emmanuel Duguet PART IV NETWORKS AND BIG DATA IN MICROECONOMICS 14 Exploring social media: Twitteronomics and beyond 388 Tho Pham, Piotr Śpiewanowski and Oleksandr Talavera 15 Econometrics of networks with limited access to network data: a literature survey 416 Pedro C.L. Souza 16 Machine learning for causal inference: estimating heterogeneous treatment effects 438 Vishalie Shah, Noemi Kreif and Andrew M. Jones PART V STATA AND R IN MICROECONOMETRIC APPLICATIONS 17 Stochastic frontier analysis in Stata: using existing and coding new commands 489 Oleg Badunenko 18 Modern R workflow and tools for microeconometric data analysis 518 Giovanni Baiocchi 19 Robust inference in panel data microeconometrics, using R 564 Giovanni Millo 20 Econometric estimation of the “Constant Elasticity of Substitution” function in R: the micEconCES package 596 Arne Henningsen, Géraldine Henningsen and Gergő Literáti Index 641

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Book SynopsisElgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences, business and law, expertly written by the world’s leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas.This Advanced Introduction provides a critical review and discussion of research concerning spatial statistics, differentiating between it and spatial econometrics, to answer a set of core questions covering the geographic-tagging-of-data origins of the concept and its theoretical underpinnings, conceptual advances, and challenges for future scholarly work. It offers a vital tool for understanding spatial statistics and surveys how concerns about violating the independent observations assumption of statistical analysis developed into this discipline.Key Features: A concise overview of spatial statistics theory and methods, looking at parallel developments in geostatistics and spatial econometrics, highlighting the eclipsing of centography and point pattern analysis by geostatistics and spatial autoregression, and the emergence of local analysis Contemporary descriptions of popular geospatial random variables, emphasizing one- and two-parameter spatial autoregression specifications, and Moran eigenvector spatial filtering coupled with a broad coverage of statistical estimation techniques A detailed articulation of a spatial statistical workflow conceptualization The helpful insights from empirical applications of spatial statistics in agronomy, criminology, demography, economics, epidemiology, geography, remotely sensed data, urban studies, and zoology/botany, will make this book a useful tool for upper-level students in these disciplines. Trade Review‘With widespread and increasingly available georeferenced data, this book offers a timely assessment of contemporary methods, models, and metrics—such as the eigenvector spatial filtering approach to handling spatial autocorrelation—in spatial statistics. I salute the authors for this enlightening contribution! The book will greatly empower us to better uncover mechanisms behind georeferenced data.’ -- Li An, San Diego State University, USTable of ContentsContents: Preface 1. An advanced introduction to spatial statistics: motivation and scope 2. Describing spatial random variables 3. Spatial statistical model parameter estimation 4. A spatial statistical modeling workflow 5. Applications from A to Z of spatial statistical modeling 6. Nonparametric spatial statistical models Afterword References Index

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Book SynopsisElgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences, business and law, expertly written by the world’s leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas.This Advanced Introduction provides a critical review and discussion of research concerning spatial statistics, differentiating between it and spatial econometrics, to answer a set of core questions covering the geographic-tagging-of-data origins of the concept and its theoretical underpinnings, conceptual advances, and challenges for future scholarly work. It offers a vital tool for understanding spatial statistics and surveys how concerns about violating the independent observations assumption of statistical analysis developed into this discipline.Key Features: A concise overview of spatial statistics theory and methods, looking at parallel developments in geostatistics and spatial econometrics, highlighting the eclipsing of centography and point pattern analysis by geostatistics and spatial autoregression, and the emergence of local analysis Contemporary descriptions of popular geospatial random variables, emphasizing one- and two-parameter spatial autoregression specifications, and Moran eigenvector spatial filtering coupled with a broad coverage of statistical estimation techniques A detailed articulation of a spatial statistical workflow conceptualization The helpful insights from empirical applications of spatial statistics in agronomy, criminology, demography, economics, epidemiology, geography, remotely sensed data, urban studies, and zoology/botany, will make this book a useful tool for upper-level students in these disciplines. Trade Review‘With widespread and increasingly available georeferenced data, this book offers a timely assessment of contemporary methods, models, and metrics—such as the eigenvector spatial filtering approach to handling spatial autocorrelation—in spatial statistics. I salute the authors for this enlightening contribution! The book will greatly empower us to better uncover mechanisms behind georeferenced data.’ -- Li An, San Diego State University, USTable of ContentsContents: Preface 1. An advanced introduction to spatial statistics: motivation and scope 2. Describing spatial random variables 3. Spatial statistical model parameter estimation 4. A spatial statistical modeling workflow 5. Applications from A to Z of spatial statistical modeling 6. Nonparametric spatial statistical models Afterword References Index

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Book SynopsisPresenting innovative modelling approaches to the analysis of fiscal policy and government debt, this book moves beyond previous models that have relied upon the assumption that various age-specific rates and policy variables remain unchanged when it comes to generating government expenditures and tax revenues. As a result of population ageing, current policy settings in many countries are projected to lead to unsustainable levels of public debt; Tax Policy and Uncertainty explores models that allow for feedbacks and uncertainty to combat this.Applicable to any country, the models in the book explore the optimal timing and extent of tax changes in the face of anticipated high future debt. Chapters produce stochastic debt projections, including probability distribution of debt ratios at each point in time. It also offers important analysis of fiscal policy trade-offs as well as providing advice on when and by how much tax rates should be increased.Economics scholars focusing on fiscal policy will appreciate the improved models in this book that allow both for uncertainty and feedback effects arising from responses to increased debt. It will also be helpful to economic policy advisors and economists in government departments.Trade Review’This book develops important innovations in addressing two problems in determining short term fiscal policy according to long run fiscal projections. The first problem is the difficulty of modelling the complex interactions of macroeconomic variables that generate feedback effects from policy decisions. Second is the potential sunk costs of making irreversible tax and spending decisions in the face of significant uncertainty about future phenomena such as population ageing and climate change. The authors build their analysis carefully and in a very readable style. It should provide a useful manual for fiscal policy makers around the world.’- Ross Guest, Griffith University, Australia -- ’Anyone seeking to understand tax policy modelling under uncertainty will certainly want to consult this book.’- James R. Hines Jr., University of Michigan, USTable of ContentsContents: 1. Introduction I Deterministic Projection Models 2. Projecting Tax Revenues 3. A Debt Projection Model II Uncertainty in Tax Models 4. Tax Policy under Uncertainty III Debt Projections and Uncertainty 5. Stochastic Projections and Debt 6. Optimal Tax Policy Bibliography Index

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Book SynopsisThis volume of the International Symposia in Economic Theory and Econometrics explores the latest economic and financial developments in Asia. Chapters cover a range of topics such as the consequences of green supply chain integration and environmental uncertainty on performance, along with the effects of perceived organisational support, transformational leadership, and teamwork on employee engagement. These peer-reviewed papers touch on a variety of timely, interdisciplinary subjects such as corporate social responsibility and the effects of public policy. Environmental, Social, and Governance Perspectives on Economic Development in Asia also includes empirical studies in financial economics and public governance. For example, one chapter analyses the impact of COVID-19 pandemic risk and lockdown on the Indian economy, while another empirically studies the influence of word of mouth variables on visits and re-visits for ecotourism in West Java. Together, ISETE 29 volume B, is a crucial resource of current, cutting-edge research for any scholar of international finance and economics.Table of ContentsChapter 1. The Economic Performance of China in Trade War: The Case Study of Three Global Economic Crisis in 1997 – 2020; Budi Sasongko, Suryaning Bawono, and Bambang Hadi Prabowo Chapter 2. Identifying Neighborhood Reinforcement Method to Enhancing Socio-Economic Development in Indonesia: A Quintuple Helix Perspective; Vidya Purnamasari, Ermita Yusida, Vika Annisa Qurrata, Santi Merlinda, Linda Seprillina, and Wen-Chi Huang Chapter 3. Materiality Analysis and CSR in Micro, Small, and Medium Enterprises; Erwin Saraswati Chapter 4. Green Supply Chain Integration and Environmental Uncertainty to Performance: The Mediating Role of Green Innovation; Siti Aisjah and Sri Palupi Prabandari Chapter 5. Entrepreneurial Perspective on Firm’s Environmental Activities; Duangporn Puttawong and Anusorn Kunanusorn Chapter 6. Facing International Environmental Challenges by Enhancing Employee Engagement; Reny Diana and B. Medina Nilasari Chapter 7. Innovation Orientation, Marketing Capability, Dynamic Capability, and Performance; Bahrun Borahima, Noermijati Noermijati, Djumilah Hadiwidjojo, and Ainur Rofiq Chapter 8. The Interactive Effect of Ethical Leadership and Employee Citizenship Behaviour on Organisational Success: Do Lenses of Corporate Governance Matter?; Amy Yeo Chu May, Carmen Teoh Chia Wen, and Jeffton Low Boon Tiong Chapter 9. Entrepreneurship in The Tourism Industry: Implication on Sustainable Economic Development; Murniati, Ghozali Maski, Iswan Noor, and Marlina Ekawaty Chapter 10. Investigation of Taxation Knowledge, Services, and Sanctions of The Head of Village Government Financial Affairs of Gunung Kidul Regency in Indonesia; Sang Ayu Putu Piastini Gunaasih Chapter 11. Impact of COVID-19 Pandemic Risk and Lockdown on the Indian Economy; Soumya Bhadury Chapter 12. The Impacts of Electronic Word of Mouth on the Intention of Patients to Join “In Vitro Fertilization” Program; Muhammad Rizal, Endang Ruswanti, and Moehammad Unggul Januarko Chapter 13. Model of Increasing Tourists Revisit Intention: Utilising the Environment as an Ecotourism Area; Dani Dagustani, Gatot Iwan Kurniawan, Heppy Agustiana Vidyastuti, and Rediawan Miharja

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Book Synopsis'In Economics as Anatomy Peter Swann has produced a wonderful sequel to his earlier 2006 classic, Putting Econometrics into Its Place. In this powerful new book, Peter Swann shows how key ideas from the economics of innovation can reconstruct economics as an empirical science. The challenge for mainstream economists is to embrace diversity and help rebuild the subject of economics so that it is no less innovative and dynamic than the economy itself. Economists need to go back to their roots and build something different.'- Kevin Dowd, Durham University, UK'This is an important, thought-provoking, well-argued and provocative work which questions the methodological basis of, and the status accorded to, econometric analyses. . . This book will prove useful to all economic researchers, whatever the stage of their career - from undergraduates to longstanding professors. This book should stimulate a lively debate and should result in all researching economists to reflect critically on their current approaches and become more open to methods other than the strictly econometric.'- Adrian Darnell, Durham University, UKThere are two fundamentally different approaches to innovation: incremental and radical. In Economics as Anatomy, G.M. Peter Swann argues that economics as a discipline needs both perspectives in order to create the maximum beneficial effect for the economy.Chapters explore how and why mainstream economics is very good at incremental innovation but seems uncomfortable with radical innovation. Swann argues that economics should follow the example of many other disciplines, transitioning from one field to a range of semi-autonomous sub-disciplines. In this book, he compares the missing link in empirical economics to being the economic equivalent of anatomy, the basis of medical discourse.Working as a sequel to Swann's Putting Econometrics in its Place, this book will be a vital resource to those who are discontent with the state of mainstream economics, especially those actively seeking to promote change in the discipline. Students wishing to see progress in the teaching of economics will also benefit from this timely book.Trade ReviewEconomics as Anatomy is a superb sequel to Peter Swann's earlier book, Putting Econometrics in its Place. It is filled with educated common sense. It critiques the profession without condemnation, and provides some excellent and well-reasoned suggestions for how the profession can (and should) do better. --David Colander, Middlebury College, US'Peter Swann's call for economics to embrace a federation of approaches to economics, akin to the diversity one finds in medicine, is a thoughtful insider's response to the calls for pluralism in economics by internal and external critics after the financial crisis of 2008. His demonstration that the signal to noise ratio in the vast majority of econometric studies is far too low to rely on econometrics alone as a way to evaluate economic theories should be read and appreciated by all econometricians. Humility may be the first necessary step in the reform of economics.' --Steve Keen, Kingston University London, UK'Swann applies ideas from the economics of innovation and standards plus entertaining analogies from medicine and Sherlock Holmes to make a persuasive case for improving empirical economics. He shows how this can be done by adopting a wider variety of methods and embracing free trade and a division of labour with other disciplines.' --Ron Smith, Birkbeck, University of London, UKTable of ContentsContents: Preface PART I RE-APPRAISAL 1. Introduction 2. How Good are Econometric Results? 3. Assumptions in Empirical Economics 4. Three Types of Rigour 5. Misinterpreting Kelvin’s Maxim 6. Myths about Case Studies 7. Discontent in the Academy 8. Wider Discontent PART II INNOVATION 9. Economics of Innovation 10. Incremental Innovation in Economics 11. Radical Innovation in Economics PART III THE FEDERATION 12. Why Emulate Medicine? 13. Economic Anatomy 14. Economic Physiology 15. Economic Pathology 16. Pathology in the Economics Discipline 17. Multidisciplinary Hybrids 18. Practitioner Hybrids 19. And Many Others … 209 20. Will the Federation Survive? References Index

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Book SynopsisThis book presents a quarter of a century of empirical research on interest rates and a variety of asset prices. It will serve to deepen our understanding of asset price inflation. The book includes extensive analysis of the measurement of interest rates, with case studies from The Netherlands, Belgium and EMU, and emphasizes statistical measurement and the attempt to understand interest rate behaviour through statistical estimation. The book also includes an examination of historical interest rate development in the long run, both theoretically and empirically. In conclusion, Professor Fase also analyses the behaviour of bonds, stocks and investment in art and examines the factors indispensable for a monetary strategy designed to target inflation.Table of ContentsContents: Introduction Part I: On Interest Rates 1. A Principal Components Analysis of Market Interest Rates in The Netherlands, 1962–1970 2. The Interdependence of Short-term Interest Rates in the Major Financial Centres of the World 3. On Interest Rates in Belgium and The Netherlands 4. International Convergence of Capital Market Interest Rates 5. Bond Yields and Expected Inflation 6. Anticipated Inflation and Interest Rates in an Open Economy 7. The Fisher Hypothesis and Exchange Rates in EMU 8. The Demand for Mortgage Credit and the Mortgage Rate in The Netherlands 9. Seventy Years of Interest Rates Part II: On Asset Prices 10. Index-linked Bonds and Inflation Risk in EMU 11. Price Determination on the Equity Market 12. The Linkage of Stock Exchange Markets Between Countries 13. The Interaction Between Trading Volume of Stocks and Options 14. The Equity Premium Puzzle 15. The Risk Premium on Equity in Core EMU 16. Purchase of Art Bibliography Index

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Book SynopsisThe rapidly increasing importance of China, India, Indonesia, Japan, South Korea and Taiwan both in Asia and in the world economy, represents a trend that is set to continue into the 21st century.This book provides an authoritative assessment of the 20th century performance of these countries, and in particular the factors contributing to the acceleration of Asian growth in the latter part of the century. The contributors look at Asia within a global perspective and detailed comparisons are drawn with Australia and the USA. Contributions from leading experts offer a comprehensive review of the procedures necessary to establish valid international comparisons for countries with very different economic histories and levels of development. These include methods of growth performance measurement and techniques of growth accounting.The Asian Economies in the Twentieth Century will be an indispensable new tool for policy analysts, international agencies and academic researchers.Trade Review'. . . this book should be on the shelf of economists and economic historians interested in Asia.' -- J. Thomas Lindblad, Bulletin of Indonesian Economic Studies'. . . this is a valuable collection of papers, which students of long-term economic growth in Asia will certainly need to read and ponder. Sceptics of historical national accounting may not be completely won over, but they will be forced to acknowledge that we can learn much from careful quantitative studies of the kind presented here.' -- Anne Booth, The World Economy'It would be a useful addition to faculty libraries in social science and business faculties. . . A book with a strong focus like this one and a wide regional range makes it a strong candidate for library purchase.' -- Malcolm Warner, Asia Pacific Business ReviewTable of ContentsContents: 1. Introduction: Measuring Asian Performances 2. The International Comparison of Real Product and Productivity 3. International Comparison of Farm Sector Performance: Methodological Options and Empirics Findings for Asia-Pacific Economies, 1900–94 4. A Comparison of Real Output and Productivity Levels in Australian and United States Manufacturing, 1970–95 5. Industrial Output and Labour Productivity in China 1949–94: A Reassessment 6. Twentieth Century Economic Performance of India 7. Indonesia’s Growth Performance in the Twentieth Century 8. The Long-term Performance of the Japanese Economy 9. Realising Growth Potential: South Korea and Taiwan, 1960 to 1998 Index

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Book SynopsisA unique and comprehensive source of information, this book is the only international publication providing economists, planners, policymakers and business people with worldwide statistics on current performance and trends in the manufacturing sector.The Yearbook is designed to facilitate international comparisons relating to manufacturing activity and industrial development and performance. It provides data which can be used to analyse patterns of growth and related long term trends, structural change, and industrial performance in individual industries. Statistics on employment patterns, wages, consumption and gross output and other key indicators are also presented.Trade Review'This annual publication seems to be the only international publication providing worldwide statistics on current performance and trends in the manufacturing sector. In terms of comprehensiveness, accuracy, and cross-country comparisons this volume is unparalleled . . . If you are looking for an authoritative source for comparative international statistics on industrial information, this is it.' -- Andrea Meyer, Business Information AlertAcclaim for previous editions:'This is a unique and massive effort by UNIDO providing comparative statistics on current performance and trends in the manufacturing sector worldwide . . . There is no doubt that the volume is a most important source book for economists, planners and policymakers.' -- Pradosh Nath, Journal of Science and Industrial Research'UNIDO has done well to bridge gaps in information noticed so far in industrial statistics worldwide and its companionship and usefulness will be realised by all users of this documentation in governmental, industrial and academic circles, as a must on every working desk. Its reliability is fully backed up by authoritative analysis.' -- Rajinder Kunmar, Marketing and Management NewsTable of ContentsContents: Introduction Appendices Part I: Summary Tables Part II: Country Tables

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Book SynopsisThis revised edition of Ryuzo Sato's seminal work illustrates the timeless nature of his contribution to economics. It is as pertinent today as when it was originally conceived, over twenty years ago.This book deals with a variety of topics in economic theory, ranging from the analysis of production functions to the general recoverability problem of optimal dynamic behavior. They are unified in the theme of 'transformation and invariance'. This book demonstrates the first application of the Lie theory to modern economics and provides a revealing analysis of market behavior and economic invariance.This book will be of interest to scholars of industrial economics, innovation, econometrics and microeconomics.Trade Review'Not until economists have given his impressive treatise a thorough reading shall we be in a position to put useful bounds on the value added to economics by this powerful and elegant technique. The ball is now in our court.' -- From the original preface by Paul A. Samuelson'Ryuzo Sato is a mathematical pioneer, and when Lie group theory is finally recognized as the natural language for the discussion of technical change and many other pivotal questions in the behavior of maximizing systems, this book will be seen as providing the foundation for the new approach.' -- Tom Russell, Santa Clara University, USTable of ContentsContents: Preface 1. An Overview 2. Holotheticity of a Technology 3. A Theory of Endogenous Technical Progress 4. “G-Neutral” Technical Change, Comparative Statics, and Integrability Conditions 5. Holotheticity of an Implicit Technology 6. Self-Dual Preferences and Technologies 7. Dynamic Symmetries and Economic Conservation Laws 8. A Lie Group Approach to the Index Number Problems 9. The Group Structure and the Theory of Observable Market Behavior Appendix: A Brief Survey of Lie’s Theory of Continuous Transformation Groups Index

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Book SynopsisThis unorthodox book derives and tests a simple theory of economic time series using several well-known empirical economic puzzles, from stock market bubbles to the failure of conventional economic theory, to explain low levels of inflation and unemployment in the US.Professor Stanley develops a new econometric methodology which demonstrates the explanatory power of the behavioral inertia hypothesis and solves the pretest/specification dilemma. He then applies this to important measures of the world's economies including GDP, prices and consumer spending. The behavioral inertia hypothesis claims that inertia and randomness (or 'caprice') are the most important factors in representing and forecasting many economic time series. The development of this new model integrates well-known patterns in economic time series data with well-accepted ideas in contemporary philosophy of science.Academic economists will find this book interesting as it presents a unified approach to economic time series, solves a number of important empirical puzzles and introduces a new econometric methodology. Business and financial analysts will also find it useful because it offers a simple, yet powerful, framework in which to study and predict financial market movements.Table of ContentsContents: Preface 1. Introduction 2. Empirical Paradox and the Behavioral Inertia Hypothesis 3. Economic Inertia as Humean Habit and Stylized Fact 4. Caprice: Dostoevsky’s Uncertainty Principle 5. Empirical Economics? An Econometric Dilemma with only a Methodological Solution 6. Ain’t Misbehavin’ – Capricious Consumption or Permanent Income? 7. Prices, Inflation, Unemployment, and Okun’s Law 8. An Empirical Critique of the Lucas Critique 9. Meta-Analysis of Ricardian Equivalence: New Wine in Old Bottles 10. The Trouble with Testing: Bubbles, Inertia and Experience in Experimental Asset Markets 11. Dénouement: What’s the Difference? References Index

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Book SynopsisBehavioral finance is the study of how psychology affects financial decision making and financial markets. A valuable resource for both academics and practitioners, this authoritative collection brings together the main works in both psychology and finance, dealing with the debate between proponents of the behavioral school and advocates of the efficient market school. The first volume contains works written by leading psychologists that underlie behavioral finance, focusing on general issues in asset pricing theory, and the studies on over-reaction and under-reaction. The second volume contains key works that develop and extend these themes. Topics include the psychology of prediction, reactions to corporate announcements, the term structure of interest rates, the equity premium, and options prices. The final volume is devoted to the psychology of decisions by individuals, both investors and corporate managers.Table of ContentsContents Volume I Acknowledgements Foreword Richard Roll Introduction Hersh Shefrin PART I BEHAVIORAL FOUNDATIONS 1. Paul Slovic (1972), ‘Psychological Study of Human Judgment: Implications for Investment Decision Making’ 2. Amos Tversky and Daniel Kahneman (1974), ‘Judgment Under Uncertainty: Heuristics and Biases’ 3. Amos Tversky and Daniel Kahneman (1982), ‘Evidential Impact of Base Rates’ 4. Ward Edwards (1982), ‘Conservatism in Human Information Processing’ 5. Dale Griffin and Amos Tversky (1992), ‘The Weighing of Evidence and the Determinants of Confidence’ 6. Stuart Oskamp (1982), ‘Overconfidence in Case-study Judgments’ PART II ASSET PRICING THEORY 7. Hersh Shefrin and Meir Statman (1994), ‘Behavioral Capital Asset Pricing Theory’ 8. Edward M. Miller (1977), ‘Risk, Uncertainty, and Divergence of Opinion’ 9. Lawrence Blume and David Easley (1992), ‘Evolution and Market Behavior’ 10. Andrei Shleifer and Robert W. Vishny (1997), ‘The Limits of Arbitrage’ 11. Terrance Odean (1998), ‘Volume, Volatility, Price, and Profit When All Traders Are Above Average’ PART III STUDIES ABOUT OVERREACTION AND UNDERREACTION 12. Werner F.M. De Bondt and Richard H. Thaler (1987), ‘Further Evidence on Investor Overreaction and Stock Market Seasonality’ 13. Jay R. Ritter (1988), ‘The Buying and Selling Behavior of Individual Investors at the Turn of the Year’ 14. Narasimhan Jegadeesh and Sheridan Titman (1993), ‘Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency’ 15. Josef Lakonishok, Andrei Shleifer and Robert W. Vishny (1994), ‘Contrarian Investment, Extrapolation, and Risk’ 16. Nicholas Barberis, Andrei Shleifer and Robert Vishny (1998), ‘A Model of Investor Sentiment’ 17. Kent Daniel, David Hirshleifer and Avanidhar Subrahmanyam (1998), ‘Investor Psychology and Security Market Under- and Overreactions’ 18. Harrison Hong and Jeremy C. Stein (1999), ‘A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets’ PART IV COMPETING VIEWS 19. Eugene F. Fama and Kenneth R. French (1996), ‘Multifactor Explanations of Asset Pricing Anomalies’ 20. Michael E. Solt and Meir Statman (1989), ‘Good Companies, Bad Stocks’ 21. Hersh Shefrin and Meir Statman (1995), ‘Making Sense of Beta, Size, and Book-to-Market’ 22. Kent Daniel and Sheridan Titman (1997), ‘Evidence on the Characteristics of Cross Sectional Variation in Stock Returns’ 23. Jennifer Conrad and Gautam Kaul (1993), ‘Long-term Market Overreaction or Biases in Computed Returns?’ 24. Ray Ball, S.P. Kothari and Jay Shanken (1995), ‘Problems in Measuring Portfolio Performance: An Application to Contrarian Investment Strategies’ 25. Tim Loughran and Jay R. Ritter (1996), ‘Long-term Market Overreaction: The Effect of Low-priced Stocks’ 26. Eugene F. Fama (1998), ‘Market Efficiency, Long-term Returns, and Behavioral Finance’ Name Index Volume II Acknowledgements Introduction Hersh Shefrin PART I PREDICTION 1. Michael E. Solt and Meir Statman (1988), ‘How Useful is the Sentiment Index?’ 2. Werner F.M. De Bondt (1993), ‘ Betting on Trends: Intuitive Forecasts of Financial Risk and Return’ 3. Paul B. Andreassen (1990), ‘Judgmental Extrapolation and Market Overreaction: On the Use and Disuse of News’ 4. Werner F.M. De Bondt (1992), Earnings Forecasts and Share Price Reversals 5. Rafael La Porta (1996), ‘Expectations and the Cross-section of Stock Returns’ 6. Benjamin Czaczkes and Yoav Ganzach (1996), ‘The Natural Selection of Prediction Heuristics: Anchoring and Adjustment versus Representativeness’ 7. Eli Amir and Yoav Ganzach (1998), ‘Overreaction and Underreaction in Analysts’ Forecasts’ PART II MARKET REACTIONS TO THE PREDICTIONS AND ANNOUNCEMENTS 8. Kent L. Womack (1996), ‘Do Brokerage Analysts' Recommendations Have Investment Value?’ 9. Roni Michaely and Kent L. Womack (1999), ‘Conflict of Interest and the Credibility of Underwriter Analyst Recommendations’ 10. Tim Loughran and Jay R. Ritter (1995), ‘The New Issues Puzzle’ 11. Tim Loughran and Jay R. Ritter (1997), ‘The Operating Performance of Firms Conducting Seasoned Equity Offerings’ 12. David Ikenberry, Josef Lakonishok and Theo Vermaelen (1995), ‘Market Underreaction to Open Market Share Repurchases’ 13. David L. Ikenberry, Graeme Rankine and Earl K. Stice (1996), ‘What Do Stock Splits Really Signal?’ 14. Roni Michaely, Richard H. Thaler and Kent L. Womack (1995), ‘Price Reactions to Dividend Initiations and Omissions: Overreaction or Drift?’ 15. Peter Klibanoff, Owen Lamont and Thierry A. Wizman (1998), ‘Investor Reaction to Salient News in Closed-end Country Funds’ PART III VOLATILITY IN THE TERM STRUCTURE OF INTEREST RATES 16. Robert J. Shiller (1979), ‘The Volatility of Long-term Interest Rates and Expectations Models of the Term Structure’ 17. John Y. Campbell and Robert J. Shiller (1991), ‘Yield Spreads and Interest Rate Movements: A Bird’s Eye View’ 18. Kenneth A. Froot (1989), ‘New Hope for the Expectations Hypothesis of the Term Structure of Interest Rates’ 19. Werner F.M. De Bondt and Mary M. Bange (1992), ‘Inflation Forecast Errors and Time Variation in Term Premia’ PART IV VOLATILITY IN EQUITY MARKETS 20. John Y. Campbell and Robert J. Shiller (1998), ‘Valuation Ratios and the Long-run Stock Market Outlook’ 21. Franco Modigliani and Richard A. Cohn (1979), ‘Inflation, Rational Valuation and the Market’ 22. Charles M.C. Lee, James Myers and Bhaskaran Swaminathan (1999), ‘What is the Intrinsic Value of the Dow?’ 23. Vernon L. Smith, Gerry L. Suchanek and Arlington W. Williams (1988), ‘Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets’ PART V OPTIONS AND ARBITRAGE 24. John E. Gilster, Jr. (1997), ‘Option Pricing Theory: Is "Risk-free" Hedging Feasible?’ 25. Robert Jarrow (1997), ‘Review of John E. Gilster, Jr. – "Option Pricing Theory: Is ‘Risk-free’ Hedging Feasible?"’ 26. Gary L. Gastineau (1997), ‘Comment on John E. Gilster, Jr. "Option Pricing Theory: Is ‘Risk-free’ Hedging Feasible?"’ 27. Hersh Shefrin (1999), ‘Irrational Exuberance and Option Smiles’ 28. Kenneth A. Froot and Emil M. Dabora (1999), ‘How are Stock Prices Affected by the Location of Trade?’ Name Index Volume III Acknowledgements Introduction Hersh Shefrin PART I FOUNDATION WORKS 1. Harry Markowitz (1952), ‘The Utility of Wealth’ 2. A.D. Roy (1952), ‘Safety First and the Holding of Assets’ 3. Daniel Kahneman and Amos Tversky (1979), ‘Prospect Theory: An Analysis of Decision Under Risk’ 4. Amos Tversky and Daniel Kahneman (1986), ‘Rational Choice and the Framing of Decisions’ 5. Lola L. Lopes (1987), ‘Between Hope and Fear: The Psychology of Risk’ PART II THE STRUCTURE OF INDIVIDUAL INVESTORS’ PORTFOLIOS 6. Hersh Shefrin and Meir Statman (2000), ‘Behavioral Portfolio Theory’ 7. Richard H. Thaler (1999), ‘Mental Accounting Matters’ 8. John J. McConnell and Eduardo S. Schwartz (1992), ‘The Origin of LYONs: A Case Study in Financial Innovation’ 9. Hersh Shefrin and Meir Statman (1993), ‘Behavioral Aspects of the Design and Marketing of Financial Products’ 10. Brad M. Barber and Terrance Odean (2000), ‘Trading is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors’ 11. Werner F.M. De Bondt (1998), ‘A Portrait of the Individual Investor’ 12. Jay R. Ritter (1996), ‘How I Helped to Make Fischer Black Wealthier’ PART III THE DISPOSITION EFFECT 13. Terrance Odean (1998), ‘Are Investors Reluctant to Realize Their Losses?’ 14. Terrance Odean (1999), ‘Do Investors Trade Too Much?’ 15. Jeffrey Heisler (1994), ‘Loss Aversion in a Futures Market: An Empirical Test’ 16. Martin Weber and Colin F. Camerer (1998), ‘The Disposition Effect in Securities Trading: An Experimental Analysis’ 17. Chip Heath, Steven Huddart and Mark Lang (1999), ‘Psychological Factors and Stock Option Exercises’ PART IV INTERTEMPORAL ISSUES 18. Michael S. Rozeff (1994), ‘Lump-sum Investing versus Dollar-averaging’ 19. Meir Statman (1995), ‘A Behavioral Framework for Dollar-cost Averaging’ 20. Laurence Levin (1998), ‘Are Assets Fungible? Testing the Behavioral Theory of Life-cycle Savings’ 21. Shlomo Benartzi and Richard H. Thaler (1995), ‘Myopic Loss Aversion and the Equity Premium Puzzle’ 22. Lola L. Lopes (1996), ‘When Time Is of the Essence: Averaging, Aspiration, and the Short Run’ 23. Stephen A. Ross (1999), ‘Adding Risks: Samuelson’s Fallacy of Large Numbers Revisited’ 24. Kenneth L. Fisher and Meir Statman (1999), ‘A Behavioral Framework for Time Diversification’ 25. Eldar Shafir, Peter Diamond and Amos Tversky (1997), ‘Money Illusion’ 26. Jeffrey Pontiff (1997), ‘Excess Volatility and Closed-end Funds’ PART V MANAGERIAL DECISION-MAKING 27. John Lintner (1956), ‘Distribution of Incomes of Corporations among Dividends, Retained Earnings, and Taxes’ 28. Merton H. Miller (1986), ‘Behavioral Rationality in Finance: The Case of Dividends’ 29. Shlomo Benartzi, Roni Michaely and Richard Thaler (1997), ‘ Do Changes in Dividends Signal the Future or the Past?’ 30. François Degeorge, Jayendu Patel and Richard Zeckhauser (1999), ‘Earnings Management to Exceed Thresholds’ 31. Meir Statman and James F. Sepe (1989), ‘Project Termination Announcements and the Market Value of the Firm’ 32. Jeremy C. Stein (1996), ‘Rational Capital Budgeting in an Irrational World’ Name Index

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Book SynopsisThis volume addresses important issues in economic theory and international trade with contributions from internationally renowned researchers - including some of Murray C. Kemp's many colleagues and former students.Economic Theory and International Trade begins with an examination of classical trade theory and welfare economics. It goes on to discuss international trade policy, including international trading agreements, taxation, tariffs and quotas. Attention then turns to the role of market structure in joint ventures, innovation, tariff policy and political economy. The final section is devoted to economic dynamics and international economics, with an emphasis on learning mechanisms, sustainable growth and immigration.This book will be indispensable to academics and graduate students in the area of international trade. Economic theorists and international trade specialists such as research units and researchers in government will also find this book of great interest.Table of ContentsContents: Introduction Publications of Murray C. Kemp from 1992 Part I: Classical Trade Theory 1. A New Approach to the Theory of International Trade under Increasing Returns: The Two-Commodities Case 2. On the Shape of Production Possibility Loci under Variable Returns to Scale: The n Commodity m Factor Case 3. A Second Correspondence Principle 4. The Welfare Economics of Encouraging More Births Part II: International Trade Policy 5. The Economic Significance of the GATT/WTO Rules 6. Are Special Economic Zones Desirable? 7. Redistributive Taxation in Closed and Open Economies 8. Unilateral Reforms of Trade and Environmental Policy 9. Quotas, Voluntary Export Restraints and Welfare Part III: Market Structure 10. International Joint Ventures, Moral Hazards and Technology Spillovers 11. The Innovation and Market Structure in General Equilibrium 12. Imperfect Substitutes and Strategic Trade Policies under Cournot Duopoly 13. Comparative Static Analysis for Cournot Oligopoly 14. Endogenous Timing and Welfare in the Game of Trade Policies under International Oligopoly 15. Politics and the Nature of Competition in Oligopolistic Markets Part IV: Economic Dynamics 16. Learning in a Generalised Dornbusch Model of Exchange Rate Dynamics 17. International Immigration, Non-Traded Goods and Economic Welfare in an Overlapping Generations Model 18. The Environment, Externalities and Sustainable Growth in an Overlapping Generations Model 19. Why Trade Matters to Development: A Learning Model 20. The Effects of Growth of its Trading Partners on Malaysia Index

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Book SynopsisThe Dynamics of Industrial Collaboration revisits and reformulates issues previously raised by inter-firm collaboration. The latest research in collaboration, processes and evaluation of cooperation, and industrial and research networks, is presented by way of both empirical and theoretical studies. The authors use several theoretical perspectives to explain inter-firm and inter-institutional collaboration: the theory of transaction costs and contracts, evolutionary theory, and the resource-based view. The book illustrates that none of these approaches are dominant.The issue of collaboration is raised in various contexts such as the new economics, biotechnology, and the motor industry. It will be of special interest to industrial economists and scholars of evolutionary economics.Table of ContentsContents: Preface Introduction Part I: Forms of Collaboration: Theories and Trends Part II: Process and Evolution of Cooperation Part III: Industrial and Research Networks Index

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Book SynopsisA unique and comprehensive source of information, this book is the only international publication providing economists, planners, policymakers and business people with worldwide statistics on current performance and trends in the manufacturing sector.The Yearbook is designed to facilitate international comparisons relating to manufacturing activity and industrial development and performance. It provides data which can be used to analyse patterns of growth and related long term trends, structural change and industrial performance in individual industries. Statistics on employment patterns, wages, consumption and gross output and other key indicators are also presented.Trade Review'This annual publication seems to be the only international publication providing worldwide statistics on current performance and trends in the manufacturing sector. In terms of comprehensiveness, accuracy, and cross-country comparisons this volume is unparalleled . . . If you are looking for an authoritative source for comparative international statistics on industrial information, this is it.' -- Andrea Meyer, Business Information Alert'. . . one of the few books where this type of information can be obtained. . . This text would be indispensable in any libraries that have a focus on international industrial statistics.' -- Herbert W. Ockerman, American Reference Books Annual 2002Acclaim for previous editions:'UNIDO has done well to bridge gaps in information noticed so far in industrial statistics worldwide and its companionship and usefulness will be realised by all users of this documentation in governmental, industrial and academic circles, as a must on every working desk. Its reliability is fully backed up by authoritative analysis.' -- Rajinder Kunmar, Marketing and Management NewsTable of ContentsContents: Introduction Appendices Part I: Summary Tables 1.1. The Manufacturing Sector 1.2. The Manufacturing Branches Part II: Country Tables

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Book SynopsisThis volume provides a coherent analysis of the economic, monetary and political aspects of growth dynamics in the Euro area. The different relevant aspects in this debate, presented and discussed by leading scholars and representatives of international organizations, include an assessment of the newest theoretical growth models for open economies, and empirical investigation of: the growth divergence between the US and Europe the extent to which fiscal co-ordination is desirable in a monetary union the role of product and labor market reforms the complex relationships between exchange rates and growth the contribution of monetary policy to economic growth and the prospects for economic growth in monetary unions. Although primarily focused on the Euro area, the analysis is equally relevant to all other common currency areas and will be welcomed by academics and students with an interest in European studies and financial economics, as well as policy and decision makers in international organisations, national institutions and central banks.Table of ContentsContents: Foreword by Guy Quaden Introduction 1. Growth in an Open Economy 2. Knowledge, Technology and Economic Growth 3. Fiscal Policy and Growth in the Context of European Integration 4. Economic Growth and the Labour Markets 5. The Role of Exchange Rate in Economic Growth 6. The Contribution of Monetary Policy 7. Monetary Union and Economic Growth Index

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Book SynopsisNon-market valuation has become a broadly accepted and widely practiced means of measuring the economic values of the environment and natural resources. In this book, the authors provide a guide to the statistical and econometric practices that economists employ in estimating non-market values.The authors develop the econometric models that underlie the basic methods: contingent valuation, travel cost models, random utility models and hedonic models. They analyze the measurement of non-market values as a procedure with two steps: the estimation of parameters of demand and preference functions and the calculation of benefits from the estimated models. Each of the models is carefully developed from the preference function to the behavioral or response function that researchers observe. The models are then illustrated with datasets that characterize the kinds of data researchers typically deal with. The real world data and clarity of writing in this book will appeal to environmental economists, students, researchers and practitioners in multilateral banks and government agencies.Table of ContentsContents: Preface 1. Welfare Economics for Non-market Valuation 2. Parametric Models for Contingent Valuation 3. Distribution-Free Models for Contingent Valuation 4. The Distribution of Willingness to Pay 5. Topics in Discrete Choice Contingent Valuation 6. Modeling the Demand for Recreation 7. Single Site Demand Estimation 8. Site Choice Models 9. Hedonic Price Equations 10. New Directions in Non-market Valuation References A. Maximum Likelihood Estimation B. Some Useful Results Index

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Book SynopsisA unique and comprehensive source of information, this book is the only international publication providing economists, planners, policymakers and business people with worldwide statistics on current performance and trends in the manufacturing sector.The Yearbook is designed to facilitate international comparisons relating to manufacturing activity and industrial development and performance. It provides data which can be used to analyse patterns of growth and related long term trends, structural change and industrial performance in individual industries. Statistics on employment patterns, wages, consumption and gross output and other key indicators are also presented.Trade Review'This is one of the most important annual publications of UNIDO. The present volume is the eighth in series and as comprehensive as the earlier volumes. In any such international publication, comparability of data remains the major problem. The volume achieved a major milestone by accounting for the switchover by countries from their respective national industrial classification to ISIC system. . . Like the earlier volumes the present volume is a commendable accomplishment.' -- Pradosh Nath, Journal of Scientific and Industrial Research'This annual publication seems to be the only international publication providing worldwide statistics on current performance and trends in the manufacturing sector. In terms of comprehensiveness, accuracy, and cross-country comparisons this volume is unparalleled . . . If you are looking for an authoritative source for comparative international statistics on industrial information, this is it.' -- Andrea Meyer, Business Information AlertTable of ContentsContents: Introduction Appendices Part I: Summary Tables 1.1. The Manufacturing Sector 1.2. The Manufacturing Branches Part II: Country Tables

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Book SynopsisThis authoritative collection brings together the most important papers in time series econometrics published since 1990. These articles cover a range of central aspects of the field, concentrating in the main on theoretical and methodological developments. Taken together, they provide an overview of the current status of research in time series econometrics, emphasising those areas that appear to have attracted most recent interest in the profession.Volume I includes sections on unit root and stationarity tests; cointegration; structural breaks; nonlinearity; and long memory. Volume II covers conditional heteroskedasticity; stochastic volatility; unobserved components; trend function analysis; prediction; seasonality; and causality.These volumes will be essential reading for all who have an interest in this rapidly advancing subject.Trade Review'To summarise, these two volumes reach exactly the purpose they aim at. They represent an excellent reference for the academic researcher as they really contain some of the most important papers on time series analysis that have been written in the last decade.' -- Marco R. Barassi, The Economic JournalTable of ContentsContents: Volume I Acknowledgements Introduction Paul Newbold and Stephen J. Leybourne PART I UNIT ROOT AND STATIONARITY TESTS 1. Serena Ng and Pierre Perron (1995), ‘Unit Root Tests in ARMA Models With Data-Dependent Methods for the Selection of the Truncation Lag’ 2. Sastry G. Pantula, Graciela Gonzalez-Farias and Wayne A. Fuller (1994), ‘A Comparison of Unit-Root Test Criteria’ 3. Graham Elliott, Thomas J. Rothenberg and James H. Stock (1996), ‘Efficient Tests for an Autoregressive Unit Root’ 4. Denis Kwiatkowski, Peter C.B. Phillips, Peter Schmidt and Yongcheol Shin (1992), ‘Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We that Economic Time Series Have a Unit Root?’ 5. S.J. Leybourne and B.P.M. McCabe (1999), ‘Modified Stationarity Tests With Data-Dependent Model-Selection Rules’ 6. Ignacio N. Lobato and Peter M. Robinson (1998), ‘A Nonparametric Test for I(0)’ PART II COINTEGRATION 7. Cheng Hsiao (1997), ‘Cointegration and Dynamic Simultaneous Equations Model’ 8. Cheng Hsiao (2001), ‘Identification and Dichotomization of Long- and Short-run Relations of Cointegrated Vector Autoregressive Models’ 9. Alfred A. Haug (1996), ‘Tests for Cointegration: A Monte Carlo Comparison’ 10. Michael T.K. Horvath and Mark W. Watson (1995), ‘Testing for Cointegration When Some of the Cointegrating Vectors Are Prespecified’ 11. Pentti Saikkonen and Helmut Lütkepohl (2000), ‘Testing for the Cointegrating Rank of a VAR Process With an Intercept’ 12. Søren Johansen (1997), ‘Likelihood Analysis of the J(2) Model’ 13. Yongcheol Shin (1994), ‘A Residual-based Test of the Null of Cointegration Against the Alternative of No Cointegration’ PART III STRUCTURAL BREAKS 14. Stephen J. Leybourne, Terence C. Mills and Paul Newbold (1998), ‘Spurious Rejections by Dickey-Fuller Tests in the Presence of a Break Under the Null’ 15. Jushan Bai (1994), ‘Least Squares Estimation of a Shift in Linear Processes’ 16. Jushan Bai and Pierre Perron (1998), ‘Estimating and Testing Linear Models with Multiple Structural Changes’ 17. Eric Zivot and Donald W.K. Andrews (1992), ‘Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis’ 18. Timothy J. Vogelsang and Pierre Perron (1998), ‘Additional Tests for a Unit Root Allowing for a Break in the Trend Function at an Unknown Time’ PART IV NONLINEARITY 19. Ruey S. Tsay (1998), ‘Testing and Modeling Multivariate Threshold Models’ 20. Timo Teräsvirta (1994), ‘Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models’ 21. Øyvind Eitrheim and Timo Teräsvirta (1996), ‘Testing the Adequacy of Smooth Transition Autoregressive Models’ 22. Walter Enders and C.W.J. Granger (1998), ‘Unit-Root Tests and Asymmetric Adjustment With an Example Using the Term Structure of Interest Rates’ PART V LONG MEMORY 23. Richard T. Baillie (1996), ‘Long Memory Processes and Fractional Integration in Econometrics’ 24. P.M. Robinson (1994), ‘Efficient Tests of Nonstationary Hypotheses’ 25. P.M. Robinson (1995), ‘Gaussian Semiparametric Estimation of Long Range Dependence’ 26. Carlos Velasco and Peter M. Robinson (2000), ‘Whittle Pseudo-Maximum Likelihood Estimation for Nonstationary Time Series’ 27. I.N. Lobato and N.E. Savin (1998), ‘Real and Spurious Long-Memory Properties of Stock-Market Data’ Name Index Volume II Acknowledgements An introduction by the editors to both volumes appears in Volume I PART I CONDITIONAL HETEROSKEDASTICITY 1. Tim Bollerslev, Ray Y. Chou and Kenneth F. Kroner (1992), ‘ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence’ 2. Robert F. Engle and Kenneth F. Kroner (1995), ‘Multivariate Simultaneous Generalized ARCH’ 3. Richard T. Baillie, Tim Bollerslev and Hans Ole Mikkelsen (1996), ‘Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity’ PART II STOCHASTIC VOLATILITY 4. Esther Ruiz (1994), ‘Quasi-maximum Likelihood Estimation of Stochastic Volatility Models’ 5. Andrew Harvey, Esther Ruiz and Neil Shephard (1994), ‘Multivariate Stochastic Variance Models’ 6. Bruce E. Hansen (1995), ‘Regression with Nonstationary Volatility’ 7. Andrew Harvey and Mariane Streibel (1998), ‘Testing for a Slowly Changing Level with Special Reference to Stochastic Volatility’ 8. Sangjoon Kim, Neil Shephard and Siddhartha Chib (1998), ‘Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models’ 9. F. Jay Breidt, Nuno Crato and Pedro de Lima (1998), ‘The Detection and Estimation of Long Memory in Stochastic Volatility’ PART III UNOBSERVED COMPONENTS 10. Agustín Maravall and Christophe Planas (1999), ‘Estimation Error and the Specification of Unobserved Component Models’ 11. Andrew Harvey and Siem Jan Koopman (2000), ‘Signal Extraction and the Formulation of Unobserved Components Models’ PART IV TREND FUNCTION ANALYSIS 12. Eugene Canjels and Mark W. Watson (1997), ‘Estimating Deterministic Trends in the Presence of Serially Correlated Errors’ 13. Timothy J. Vogelsang (1998), ‘Trend Function Hypothesis Testing in the Presence of Serial Correlation’ PART V PREDICTION 14. Kenneth D. West (1996), ‘Asymptotic Inference about Predictive Ability’ 15. Todd E. Clark and Michael W. McCracken (2001), ‘Tests of Equal Forecast Accuracy and Encompassing for Nested Models’ PART VI SEASONALITY 16. Eric Ghysels, Clive W.J. Granger and Pierre L. Siklos (1996), ‘Is Seasonal Adjustment a Linear or Nonlinear Data-Filtering Process?’ 17. Denise R. Osborn (1991), ‘The Implications of Periodically Varying Coefficients for Seasonal Time-Series Processes’ 18. S. Hylleberg, R.F. Engle, C.W.J. Granger and B.S. Yoo (1990), ‘Seasonal Integration and Cointegration’ 19. Richard J. Smith and A.M. Robert Taylor (1999), ‘Likelihood Ratio Tests for Seasonal Unit Roots’ 20. H. Peter Boswijk and Philip Hans Franses (1996), ‘Unit Roots in Periodic Autoregressions’ PART VII CAUSALITY 21. Hafida Boudjellaba, Jean-Marie Dufour and Roch Roy (1992), ‘Testing Causality Between Two Vectors in Multivariate Autoregressive Moving Average Models’ 22. Helmut Lütkepohl and D.S. Poskitt (1996), ‘Testing for Causation Using Infinite Order Vector Autoregressive Processes’ 23. Clive W.J. Granger and Jin-Lung Lin (1995), ‘Causality in the Long Run’ Name Index

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Book SynopsisIn this landmark collection, the editor has selected the most influential papers on the econometrics of panel data published in the period from 1992-2001, thus providing an update on developments in the field since the two volumes edited by G.S. Maddala in 1993, which covered the period from 1966-1992.Topics covered in these latest volumes include core articles on dynamic panels and the generalized method of moments, heterogeneous panels, non-stationary panels including spurious regression, unit roots and tests for cointegration in panels, limited dependent variable models using panel data including models with censored endogenous variables and sample selection, non-linear panel data models, unbalanced panels, pseudo-panels and specification tests in panels.Trade Review'The editor of volumes like the present one has the chance to direct the reader's attention to nearly unknown but relevant papers. Baltagi has done this job very well. . . Summing up, the two volumes on recent developments of panel data belong in every library. The collection helps the newcomer to better understand this topic and it is a useful reference for the expert.' -- Olaf Hubler, Statistical PapersTable of ContentsContents: Volume I Acknowledgements Introduction Badi H. Baltagi PART I DYNAMIC PANELS AND GMM 1. Seung C. Ahn and Peter Schmidt (1995), ‘Efficient Estimation of Models for Dynamic Panel Data’ 2. Seung C. Ahn and Peter Schmidt (1997), ‘Efficient Estimation of Dynamic Panel Data Models: Alternative Assumptions and Simplified Estimation’ 3. César Alonso-Borrego and Manuel Arellano (1999), ‘Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data’ 4. Manuel Arellano and Olympia Bover (1995), ‘Another Look at the Instrumental Variable Estimation of Error-Components Models’ 5. Richard Blundell and Stephen Bond (1998), ‘Initial Conditions and Moment Restrictions in Dynamic Panel Data Models’ 6. Bruno Crepon, Francis Kramarz and Alain Trognon (1997), ‘Parameters of Interest, Nuisance Parameters and Orthogonality Conditions: An Application to Autoregressive Error Component Models’ 7. Jinyong Hahn (1999), ‘How Informative is the Initial Condition in the Dynamic Panel Model with Fixed Effects?’ 8. Jan F. Kiviet (1995), ‘On Bias, Inconsistency, and Efficiency of Various Estimators in Dynamic Panel Data Models’ 9. Tom Wansbeek (2001), ‘GMM Estimation in Panel Data Models with Measurement Error’ 10. James P. Ziliak (1997), ‘Efficient Estimation with Panel Data when Instruments are Predetermined: An Empirical Comparison of Moment-Condition Estimators’ PART II HETEROGENEOUS PANELS 11. Badi H. Baltagi and James M. Griffin (1997), ‘Pooled Estimators vs. their Heterogeneous Counterparts in the Context of Dynamic Demand for Gasoline’ 12. Cheng Hsiao and A. Kamil Tahmiscioglu (1997), ‘A Panel Analysis of Liquidity Constraints and Firm Investment’ 13. G.S. Maddala, Robert P. Trost, Hongyi Li and Frederick Joutz (1997), ‘Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators’ 14. M. Hashem Pesaran and Ron Smith (1995), ‘Estimating Long-Run Relationships from Dynamic Heterogeneous Panels’ PART III NON-STATIONARY PANELS 15. Kaddour Hadri (2000), ‘Testing for Stationarity in Heterogeneous Panel Data’ 16. Richard D.F. Harris and Elias Tzavalis (1999), ‘Inference for Unit Roots in Dynamic Panels Where the Time Dimension is Fixed’ 17. Chihwa Kao (1999), ‘Spurious Regression and Residual-Based Tests for Cointegration in Panel Data’ 18. G.S. Maddala and Shaowen Wu (1999), ‘A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test’ 19. Hyungsik R. Moon and Peter C.B. Phillips (1999), ‘Maximum Likelihood Estimation in Panels with Incidental Trends’ 20. Peter Pedroni (2000), ‘Fully Modified OLS for Heterogeneous Cointegrated Panels’ 21. M. Hashem Pesaran, Yongcheol Shin, and Ron P. Smith (1999), ‘Pooled Mean Group Estimation of Dynamic Heterogeneous Panels’ 22. Peter C.B. Phillips and Hyungsik R. Moon (1999), ‘Linear Regression Limit Theory for Nonstationary Panel Data’ Name Index Volume II Acknowledgements An introduction by the editor to both volumes appears in Volume I PART I LIMITED DEPENDENT VARIABLES 1. Bo E. Honoré (1992), ‘Trimmed LAD and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects’ 2. Bo E. Honoré and Ekaterini Kyriazidou (2000), ‘Panel Data Discrete Choice Models with Lagged Dependent Variables’ 3. Michael P. Keane (1994), ‘A Computationally Practical Simulation Estimator for Panel Data’ 4. Ekaterini Kyriazidou (1997), ‘Estimation of a Panel Data Sample Selection Model’ 5. Michael Lechner (1995), ‘Some Specification Tests for Probit Models Estimated on Panel Data’ 6. Myoung-jae Lee (1999), ‘A Root-N Consistent Semiparametric Estimator for Related-Effect Binary Response Panel Data’ 7. Francis Vella and Marno Verbeek (1999), ‘Two-step Estimation of Panel Data Models with Censored Endogenous Variables and Selection Bias’ 8. Jeffrey M. Wooldridge (1995), ‘Selection Corrections for Panel Data Models under Conditional Mean Independence Assumptions’ PART II NON-LINEAR PANEL MODELS 9. Jason Abrevaya (1999), ‘Leapfrog Estimation of a Fixed-Effects Model with Unknown Transformation of the Dependent Variable’ 10. Jeffrey M. Wooldridge (1999), ‘Distribution-free Estimation of Some Nonlinear Panel Data Models’ PART III UNBALANCED PANELS 11. Werner Antweiler (2001), ‘Nested Random Effects Estimation in Unbalanced Panel Data’ 12. Badi H. Baltagi and Ping X. Wu (1999), ‘Unequally Spaced Panel Data Regressions with AR (1) Disturbances’ 13. Badi H. Baltagi, Seuck Heun Song and Byoung Cheol Jung (2001), ‘The Unbalanced Nested Error Component Regression Model’ 14. Peter Davis (2002), ‘Estimating Multi-way Error Components Models with Unbalanced Data Structures’ PART IV PSEUDO-PANELS 15. M. Dolores Collado (1997), ‘Estimating Dynamic Models from Time Series of Independent Cross-Sections’ 16. Sourafel Girma (2000), ‘A Quasi-Differencing Approach to Dynamic Modelling from a Time Series of Independent Cross-Sections’ 17. David J. McKenzie (2001), ‘Estimation of AR(1) Models with Unequally Spaced Pseudo-Panels’ 18. Robert Moffitt (1993), ‘Identification and Estimation of Dynamic Models with a Time Series of Repeated Cross-Sections’ PART V SPECIFICATION TESTS IN PANELS 19. Badi H. Baltagi, Javier Hidalgo and Qi Li (1996), ‘A Nonparametric Test for Poolability Using Panel Data’ 20. Badi H. Baltagi and Qi Li (1995), ‘Testing AR(1) against MA(1) Disturbances in an Error Component Model’ 21. Anil K. Bera, Walter Sosa-Escudero and Mann Yoon (2001), ‘Tests for the Error Component Model in the Presence of Local Misspecification’ 22. Q. Li and C. Hsiao (1998), ‘Testing Serial Correlation in Semiparametric Panel Data Models’ 23. Gilbert E. Metcalf (1996), ‘Specification Testing in Panel Data with Instrumental Variables’ Name Index

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Book SynopsisNonlinear Models, Labour Markets and Exchange offers a number of broad introductory surveys in the areas of nonlinear modelling, labour economics and the economic analysis of exchange. This collection of articles consists largely of recently published refereed papers. The early chapters provide an introduction to the analysis of 'chaos and strange attractors' and the use of the very flexible generalised exponential family of frequency distributions in analysing both time series and cross-sectional distributions. The volume then provides syntheses of the theories of internal labour markets, trade union bargaining, and population ageing and its implications. It goes on to survey a range of topics in the broad area of the theory of exchange, which is central to the neoclassical economic model. Finally, the book provides some advice for students who are about to start their first piece of research. It ends with a unique survey of the history of economic analysis. Providing introductory material and syntheses of a wide range of topics, Nonlinear Models, Labour Markets and Exchange will be welcomed by economics academics and researchers interested in labour economics and econometrics.Table of ContentsContents: Part I: Nonlinear Models Part II: Labour Markets Part III: Demand and Exchange Part IV: A Mixture Index

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Book SynopsisIn recent years there has been a substantial global increase in interest in the study of gambling. To some extent this has mirrored seismic changes in the way that betting and gaming markets worldwide are taxed and regulated. This has heightened interest in a wide range of issues related to this sector including its regulation, public policy and commercial strategy as well as the ideal structure of gambling taxes and devising optimal responses to environmental changes, such as the growth of online gambling. This volume, by bringing together the work of leading scholars, will cover the spectrum of such perspectives, as well as examining the efficiency of betting markets, to provide an assessment of developments and current understanding in the study of the economics of gambling. This timely collection will be an immensely valuable resource for academics, policy-makers, those commercially involved in the betting and gaming sectors as well as the interested layman. Table of ContentsContents: Acknowledgements Introduction Leighton Vaughan Williams PART I THE ECONOMICS OF RACETRACK BETTING 1. R.M. Griffith (1949), ‘Odds Adjustments by American Horse-Race Bettors’ 2. Wayne W. Snyder (1978), ‘Horse Racing: Testing the Efficient Markets Model’ 3. Richard H. Thaler and William T. Ziemba (1988), ‘Anomalies. Parimutuel Betting Markets: Racetracks and Lotteries’ 4. Jack Dowie (1976), ‘On the Efficiency and Equity of Betting Markets’ 5. Richard E. Quandt (1986), ‘Betting and Equilibrium’ 6. Joe Golec and Maurry Tamarkin (1998), ‘Bettors Love Skewness, Not Risk, at the Horse Track’ 7. William Hurley and Lawrence McDonough (1995), ‘A Note on the Hayek Hypothesis and the Favourite-Longshot Bias in Parimutuel Betting’ 8. Michael A. Smith, David Paton and Leighton Vaughan Williams (2006), ‘Market Efficiency in Person-to-Person Betting’ 9. Hyun Song Shin (1991), ‘Optimal Betting Odds against Insider Traders’ 10. Leighton Vaughan Williams and David Paton (1997), ‘Why is There a Favourite-Longshot Bias in British Racetrack Betting Markets?’ 11. N.F.R. Crafts (1985), ‘Some Evidence of Insider Knowledge in Horse Race Betting in Britain’ 12. Leighton Vaughan Williams (1999), ‘Information Efficiency in Betting Markets: A Survey’ 13. M. Sung and J.E.V. Johnson (2010), ‘Revealing Weak-Form Inefficiency in a Market for State Contingent Claims: The Importance of Market Ecology, Modelling Procedures and Investment Strategies’ 14. Ruth N. Bolton and Randall G. Chapman (1986), ‘Searching for Positive Returns at the Track: A Multinomial Logit Model for Handicapping Horse Races’ 15. Kelly Busche and Christopher D. Hall (1988), ‘An Exception to the Risk Preference Anomaly’ PART II THE ECONOMICS OF SPORTS BETTING 16. Michael Cain, David Law and David Peel (2000), ‘The Favourite-Longshot Bias and Market Efficiency in UK Football Betting’ 17. David Paton and Leighton Vaughan Williams (2005), ‘Forecasting Outcomes in Spread Betting Markets: Can Bettors Use “Quarbs” to Beat the Book?’ 18. Colin F. Camerer (1989), ‘Does the Basketball Market Believe in the “Hot Hand”?’ 19. William O. Brown and Raymond D. Sauer (1993), ‘Does the Basketball Market Believe in the “Hot Hand”? Comment’ 20. Steven D. Levitt (2004), ‘Why are Gambling Markets Organised so Differently from Financial Markets?’ PART III THE ECONOMICS OF GAMING AND CASINO GAMBLING 21. William R. Eadington (1999), ‘The Economics of Casino Gambling’ 22. Daniel B. Suits (1979), ‘The Elasticity of Demand for Gambling’ 23. John E. Anderson (2005), ‘Casino Taxation in the United States’ 24. David Paton, Donald S. Siegel and Leighton Vaughan Williams (2002), ‘A Policy Response to the E-Commerce Revolution: The Case of Betting Taxation in the UK’ 25. David Paton, Donald S. Siegel and Leighton Vaughan Williams (2004), ‘Taxation and the Demand for Gambling: New Evidence from the United Kingdom’ 26. Ricardo Gazel (1998), ‘The Economic Impacts of Casino Gambling at the State and Local Levels’ 27. Donald Siegel and Gary Anders (2001), ‘The Impact of Indian Casinos on State Lotteries: A Case Study of Arizona’ 28. Donald S. Elliott and John C. Navin (2002), ‘Has Riverboat Gambling Reduced State Lottery Revenue’ 29. Douglas M. Walker and John D. Jackson (2008), ‘Do U.S. Gambling Industries Cannibalize Each Other?’ 30. Chad Cotti (2008), ‘The Effect of Casinos on Local Labor Markets: A Country Level Analysis’ 31. Patricia B. Reagan and Robert J. Gitter (2007), ‘Is Gaming the Optimal Strategy? The Impact of Gaming Facilities on the Income and Employment of American Indians’ PART IV THE ECONOMICS OF NATIONAL AND STATE LOTTERIES 32. Dek Terrell (1994), ‘A Test of the Gambler’s Fallacy: Evidence from Pari-mutuel Games’ 33. Charles T. Clotfelter and Philip J. Cook (1991), ‘Lotteries in the Real World’ 34. Charles T. Clotfelter and Philip J. Cook (1993), ‘Notes: The “Gambler’s Fallacy” in Lottery Play’ 35. Jonathan Guryan and Melissa S. Kearney (2008), ‘Gambling at Lucky Stores: Empirical Evidence from State Lottery Sales’ 36. Thomas A. Garrett and Russell S. Sobel (1999), ‘Gamblers Favor Skewness, Not Risk: Further Evidence from United States’ Lottery Games’ 37. David Forrest, Robert Simmons and Neil Chesters (2002), ‘Buying a Dream: Alternative Models of Demand for Lotto’ 38. Richard Thalheimer and Mukhtar M. Ali (1995), ‘The Demand for Pari-mutuel Horse Race Wagering and Attendance’ 39. Melissa Schettini Kearney (2005), ‘State Lotteries and Consumer Behavior’ 40. Kent R. Grote and Victor A. Matheson (2006), ‘Dueling Jackpots: Are Competing Lotto Games Complements or Substitutes?’

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Book SynopsisNon-market valuation has become a broadly accepted and widely practiced means of measuring the economic values of the environment and natural resources. In this book, the authors provide a guide to the statistical and econometric practices that economists employ in estimating non-market values.The authors develop the econometric models that underlie the basic methods: contingent valuation, travel cost models, random utility models and hedonic models. They analyze the measurement of non-market values as a procedure with two steps: the estimation of parameters of demand and preference functions and the calculation of benefits from the estimated models. Each of the models is carefully developed from the preference function to the behavioral or response function that researchers observe. The models are then illustrated with datasets that characterize the kinds of data researchers typically deal with. The real world data and clarity of writing in this book will appeal to environmental economists, students, researchers and practitioners in multilateral banks and government agencies.Table of ContentsContents: Preface 1. Welfare Economics for Non-market Valuation 2. Parametric Models for Contingent Valuation 3. Distribution-Free Models for Contingent Valuation 4. The Distribution of Willingness to Pay 5. Topics in Discrete Choice Contingent Valuation 6. Modeling the Demand for Recreation 7. Single Site Demand Estimation 8. Site Choice Models 9. Hedonic Price Equations 10. New Directions in Non-market Valuation References A. Maximum Likelihood Estimation B. Some Useful Results Index

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