Econometrics and economic statistics Books

Book SynopsisStudents in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book, now in its second edition, provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics and quantitative social sciences. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter onTable of Contents1. Introduction; 2. Model specification and estimation; 3. Basic count regression; 4. Generalized count regression; 5. Model evaluation and testing; 6. Empirical illustrations; 7. Time series data; 8. Multivariate data; 9. Longitudinal data; 10. Endogenous regressors and selection; 11. Flexible methods for counts; 12. Bayesian methods for counts; 13. Measurement errors.

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Book SynopsisEdited by several of the leading figures in the field, this is the first book to provide a state-of-the-art, accessibly written methodological introduction to the tools and techniques of agent-based modelling. Using these building blocks, readers will learn how to design, simulate, and validate agent-based models in economics.Trade Review'Some 25 years ago, Frank Hahn a leading economic theorist said, '… wildly complex systems need simulating … while there will be work for the computer scientist, I very much doubt that economists will be able to establish general propositions in any but very special examples'. Economists have reacted by saying 'show us an alternative'. This book does just that. It provides the elements of an alternative computational approach in which aggregate phenomena such as crises do not appear from the blue, but emerge from the interaction between simple but heterogeneous agents.' Alan Kirman, University of Aix-Marseille III'The authors conceive of economies as complex systems of heterogeneous interacting agents with bounded rationality and limited information, and they view agent-based modeling as a necessary tool for the exploration of such systems. In this book the authors provide a comprehensive introduction to agent-based modeling. Although macroeconomic applications are stressed, the coverage of topics such as rationality, behavior, expectations, and learning will be of value for many other applications as well. A particularly welcome aspect of the book is its attention to historical antecedents and its inclusion of chapters devoted to empirical validation and estimation issues.' Leigh Tesfatsion, Iowa State UniversityTable of Contents1. Introduction; 2. Agent-based computational economics: what, why, when; 3. Agent-based models as recursive systems; 4. Rationality, behaviour and expectations; 5. Agents' behaviour and learning; 6. Interaction; 7. The agent-based experiment; 8. Empirical validation of agent-based models; 9. Estimation of agent-based models; 10. Epilogue.

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Book SynopsisA First Course in Quantitative Finance is suitable for economics, finance, econometrics and mathematics students with an interest in quantitative finance. Covering all topics from the architecture of financial markets to derivatives, it uses stereoscopic images to allow 3D visualisation of complex subjects without the need for additional tools.Trade Review'A First Course in Quantitative Finance is a gentle introduction in a complicated subject. It covers most important topics - such as portfolio optimisation, derivative pricing, and fixed income products - and discusses them from the perspective of financial economics and financial mathematics. It provides the necessary mathematical background, contains the financial discussion, and is full of illustrative examples. It will be useful for anyone who wants to study the subject area on an advanced level.' Rüdiger Kiesel, Universität Duisburg-Essen'This is a remarkably complete book on all aspects of modern finance, covering topics from the puzzles of financial economics, through modern portfolio management to the pricing of exotic options under stochastic volatility at an equally accessible yet state-of-the-art level. Quants, portfolio managers, students and teachers of finance alike will find it to be an invaluable source of insights and a must-have reference to have on their desks.' Peter Tankov, École nationale de la statistique et de l'administration économiqueTable of Contents1. Introduction; Part I. Technical Basics: 2. A primer on probability; 3. Vector spaces; 4. Utility theory; Part II. Financial Markets and Portfolio Theory: 5. Architecture of financial markets; 6. Modern portfolio theory; 7. CAPM and APT; 8. Portfolio performance and management; 9. Financial economics; 10. Behavioral finance; Part III. Derivatives: 11. Forwards, futures and options; 12. The binomial model; 13. The Black–Scholes theory; 14. Exotics in the Black–Scholes model; 15. Deterministic volatility; 16. Stochastic volatility; 17. Processes with jumps; Part IV. The Fixed-Income World: 18. Basic fixed-income instruments; 19. Plain vanilla fixed-income derivatives; 20. Term structure models; 21. The LIBOR market model; Appendix A. Complex analysis; Appendix B. Solutions to problems.

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Book SynopsisThis second edition retains the positive features of being clearly written, well organized, and incorporating calculus in the text, while adding expanded coverage on game theory, experimental economics, and behavioural economics. It remains more focused and manageable than similar textbooks, and provides a concise yet comprehensive treatment of the core topics of microeconomics, including theories of the consumer and of the firm, market structure, partial and general equilibrium, and market failures caused by public goods, externalities and asymmetric information. The book includes helpful solved problems in all the substantive chapters, as well as over seventy new mathematical exercises and enhanced versions of the ones in the first edition. The authors make use of the book''s full color with sharp and helpful graphs and illustrations. This mathematically rigorous textbook is meant for students at the intermediate level who have already had an introductory course in microeconomics, anTrade Review'There are many textbooks covering intermediate microeconomics, but this one is distinctive for how clearly yet concisely it conveys the material. I highly recommend it.' Eric Maskin, Nobel Laureate in Economics, Harvard University, Massachusetts'This thoughtfully conceived and beautifully written textbook covers all of the material that one would hope to see in a modern course on intermediate microeconomics, from consumer theory and general equilibrium, to game theory and markets with asymmetric information. Rich examples and exercises follow each chapter and, all-combined, make this a masterfully executed book.' Philip J. Reny, Hugo F. Sonnenschein Distinguished Service Professor in Economics, University of ChicagoTable of Contents1. Introduction; Part I. Theory of the Consumer: 2. Preferences and utility; 3. The budget constraint and the consumer's optimal choice; 4. Demand functions; 5. Supply functions for labor and savings; 6. Welfare economics 1: the one-person case; 7. Welfare economics 2: the many-person case; Part II. Theory of the Producer: 8. Theory of the firm 1: the single-input model; 9. Theory of the firm 2: the long run, multiple-input model; 10. Theory of the firm 3: the short run, multiple-input model; Part III. Partial Equilibrium: Market Structure: 11. Perfectly competitive markets; 12. Monopoly and monopolistic competition; 13. Duopoly; 14. Game theory; Part IV. General Equilibrium: 15. An exchange economy; 16. A production economy; Part V. Market Failure: 17. Externalities; 18. Public goods; 19. Uncertainty and expected utility; 20. Uncertainty and asymmetric information.

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Book SynopsisThis essential reference for students and scholars in the input-output research and applications community has been fully revised and updated to reflect important developments in the field. Expanded coverage includes construction and application of multiregional and interregional models, including international models and their application to global economic issues such as climate change and international trade; structural decomposition and path analysis; linkages and key sector identification and hypothetical extraction analysis; the connection of national income and product accounts to input-output accounts; supply and use tables for commodity-by-industry accounting and models; social accounting matrices; non-survey estimation techniques; and energy and environmental applications. Input-Output Analysis is an ideal introduction to the subject for advanced undergraduate and graduate students in many scholarly fields, including economics, regional science, regional economics, city, regiTrade Review'It is not an exaggeration to call this book the Bible of input-output practitioners. Past editions of this book have served as the undergraduate and post-graduate textbook, introducing scholars from outside the Economics discipline to extended topics such as social accounting, resource depletion, pollution, and environmental impacts. The book has recently enjoyed increased popularity and attention at higher levels of academic and decision-making impact. Therefore, this latest edition book is a timely update of a truly seminal foundation.' Manfred Lenzen, The University of Sydney'This book comes just at a time when multi-country input-output analysis has become the key instrument to understand the economic, social and environmental consequences of international trade flows between sectors, global value chains or supply chains disruptions, caused for example by COVID-19. The authors draw on the traditional literature and expand it again very smartly to incorporate the latest advances in input-output analysis, thus offering the reader a reference unique for students, professionals, researchers and policy makers around the world.' José M. Rueda-Cantuche, European Commission Joint Research Centre'Since the publication of the second edition of this book, the world changed rapidly when production activities became organized in global value chains and we started to realize that our consumption at home also had environmental consequences on the other side of the globe. To handle the new circumstances, today's analyses require global input-output tables and models. This new, third edition includes a discussion of such tables and models, and their application to relevant issues such as climate change and international trade. In other words, the input-output textbook is up-to-date again.' Erik Dietzenbacher, University of Groningen'The expanding community of scholars and practitioners who have used the prior two editions will welcome the addition of a third version that addresses the increasing use of input-output systems in environmental and trade modeling, with attention to life-cycle analysis and value chains. This edition retains the book's stature as an amazingly valuable digestion of an ever-expanding literature that is presented in a logical and clear fashion.' Geoffrey J.D. Hewings, University of Illinois'It is highly difficult if not impossible for input-output researchers to write a new textbook on the field, because they already have at hand Input-Output Analysis: Foundations and Extensions. This book is so comprehensive in coverage and continuously evolving for updates, allowing very little room for other scholars to supplement. The book also embraces readers of differing levels and areas of interest, from university undergraduates to professionals, from trade economists to environmental analysts, which again makes it hard to imagine a substitute of any kind. The book is really a must-read literature.' Satoshi Inomata, The President of the International Input-Output Association & Chief Senior Researcher of Institute of Developing Economies, JETROTable of Contents1. Introduction and overview; 2. Foundations of input-output analysis; 3. Input-output models at the regional level; 4. Organization of basic data for input-output models; 5. The commodity-by-industry approach in input-output models; 6. Multipliers in the input-output model; 7. Supply-side models, linkages, and important coefficients; 8. Decomposition approaches; 9. Nonsurvey and partial-survey methods – fundamentals; 10. Nonsurvey and partial-survey methods – extensions; 11. Social accounting matrices; 12. Energy input-output analysis; 13. Environmental input-output analysis; 14. Mixed and dynamic models; 15. Additional topics; Postscript.

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Book SynopsisIn this Element and its accompanying second Element, A Practical Introduction to Regression Discontinuity Designs: Extensions, Matias Cattaneo, NicolásIdrobo, and Rociìo Titiunik provide an accessible and practical guide for the analysis and interpretation of regression discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence. In this Element, the authors discuss the foundations of the canonical SharpRD design, which has the following features: (i) the score is continuously distributed and has only one dimension, (ii) there is only one cutoff, and (iii) compliance with the treatment assignment is perfect. In the second Element, the authors discuss practical and conceptual extensions to this basic RD setup.Table of Contents1. Introduction; 2. The sharp RD design; 3. RD plots; 4. The continuity-based approach to RD analysis; 5. Validation and falsification of the RD design; 6. Final remarks.

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Book SynopsisThis must-have manual provides detailed solutions to all of the 300 exercises in Dickson, Hardy and Waters'' Actuarial Mathematics for Life Contingent Risks, 3 edition. This groundbreaking text on the modern mathematics of life insurance is required reading for the Society of Actuaries'' (SOA) LTAM Exam. The new edition treats a wide range of newer insurance contracts such as critical illness and long-term care insurance; pension valuation material has been expanded; and two new chapters have been added on developing models from mortality data and on changing mortality. Beyond professional examinations, the textbook and solutions manual offer readers the opportunity to develop insight and understanding through guided hands-on work, and also offer practical advice for solving problems using straightforward, intuitive numerical methods. Companion Excel spreadsheets illustrating these techniques are available for free download.Table of ContentsPreface; 1. Solutions for Chapter 1; 2. Solutions for Chapter 2; 3. Solutions for Chapter 3; 4. Solutions for Chapter 4; 5. Solutions for Chapter 5; 6. Solutions for Chapter 6; 7. Solutions for Chapter 7; 8. Solutions for Chapter 8; 9. Solutions for Chapter 9; 10. Solutions for Chapter 10; 11. Solutions for Chapter 11; 12. Solutions for Chapter 12; 13. Solutions for Chapter 13; 14. Solutions for Chapter 14; 15. Solutions for Chapter 15; 16. Solutions for Chapter 16; 17. Solutions for Chapter 17; 18. Solutions for Chapter 18; 19. Solutions for Chapter 19.

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Structural vector autoregressive (VAR) models are important tools for empirical work in macroeconomics, finance, and related fields. This book not only reviews the many alternative structural VAR approaches discussed in the literature, but also highlights their pros and cons in practice. It provides guidance to empirical researchers as to the most appropriate modeling choices, methods of estimating, and evaluating structural VAR models. The book traces the evolution of the structural VAR methodology and contrasts it with other common methodologies, including dynamic stochastic general equilibrium (DSGE) models. It is intended as a bridge between the often quite technical econometric literature on structural VAR modeling and the needs of empirical researchers. The focus is not on providing the most rigorous theoretical arguments, but on enhancing the reader''s understanding of the methods in question and their assumptions. Empirical examples are provided for illustration.

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Book SynopsisStochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of stochastic differential equations in target tracking and medical technology and, in particular, their use in methodologies such as filtering, smoothing, parameter estimation, and machine learning. It builds an intuitive hands-on understanding of what stochastic differential equations are all about, but also covers the essentials of Itô calculus, the central theorems in the field, and such approximation schemes as stochastic RungeKutta. Greater emphasis is given to solution methods than to analysis of theoretical properties of the equations. The book''s practical approach assumes only prior understanding of ordinary differential equations. The numerous worked examples and end-of-chapter exercises include application-Trade Review'Stochastic differential equations have long been used by physicists and engineers, especially in filtering and prediction theory, and more recently have found increasing application in the life sciences, finance and an ever-increasing range of fields. The authors provide intended users with an intuitive, readable introduction and overview without going into technical mathematical details from the often-demanding theory of stochastic analysis, yet clearly pointing out the pitfalls that may arise if its distinctive differences are disregarded. A large part of the book deals with underlying ideas and methods, such as analytical, approximative and computational, which are illustrated through many insightful examples. Linear systems, especially with additive noise and Gaussian solutions, are emphasized, though nonlinear systems are not neglected, and a large number of useful results and formulas are given. The latter part of the book provides an up to date survey and comparison of filtering and parameter estimation methods with many representative algorithms, and culminates with their application to machine learning.' Peter Kloeden, Johann Wolfgang Goethe-Universität Frankfurt am Main'Overall, this is a very well-written and excellent introductory monograph to SDEs, covering all important analytical properties of SDEs, and giving an in-depth discussion of applied methods useful in solving various real-life problems.' Igor Cialenco, MathSciNet'Chapters are rich in examples, numerical simulations, illustrations, derivations and computational assignment' Martin Ondreját, the European Mathematical Society and the Heidelberg Academy of Sciences and HumanitiesTable of Contents1. Introduction; 2. Some background on ordinary differential equations; 3. Pragmatic introduction to stochastic differential equations; 4. Ito calculus and stochastic differential equations; 5. Probability distributions and statistics of SDEs; 6. Statistics of linear stochastic differential equations; 7. Useful theorems and formulas for SDEs; 8. Numerical simulation of SDEs; 9. Approximation of nonlinear SDEs; 10. Filtering and smoothing theory; 11. Parameter estimation in SDE models; 12. Stochastic differential equations in machine learning; 13. Epilogue.

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Book SynopsisMark L. Berenson is Professor of Information Management and Business Analytics at Montclair State University and Professor Emeritus of Information Systems and Statistics at Baruch College. He currently teaches graduate and undergraduate courses in statistics and operations management in the School of Business, and an undergraduate course in international justice and human rights that he co-developed in the College of Humanities and Social Sciences. Berenson received a BA in economic statistics and an MBA in business statistics from City College of New York and a PhD in business from the City University of New York. Berenson's research has been published in Decision Sciences Journal of Innovative Education, Review of Business Research, The American Statistician, Communications in Statistics, Psychometrika, Educational and Psychological Measurement, Journal of MTable of ContentsBrief Contents First Things First (online) Defining and Collecting Data Organizing and Visualizing Variables Numerical Descriptive Measures Basic Probability Discrete Probability Distributions The Normal Distribution and Other Continuous Distributions Sampling Distributions Confidence Interval Estimation Fundamentals of Hypothesis Testing: One-Sample Tests Two-Sample Tests Analysis of Variance Chi-Square and Nonparametric Tests Simple Linear Regression Introduction to Multiple Regression Multiple Regression Model Building Time-Series Forecasting Business Analytics Getting Ready to Analyze Data in the Future Statistical Applications in Quality Management (online) Decision Making (online)

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Book SynopsisAn introduction to the concepts and tools of natural resource economics, including dynamic models, market failures, and institutional remedies.This introduction to natural resource economics treats resources as a type of capital; their management is an investment problem requiring forward-looking behavior within a dynamic setting. Market failures are widespread, often associated with incomplete or nonexistent property rights, complicated by policy failures. The book covers standard resource economics topics, including both the Hotelling model for nonrenewable resources and models for renewable resources. The book also includes some topics in environmental economics that overlap with natural resource economics, including climate change.The text emphasizes skills and intuition needed to think about dynamic models and institutional remedies in the presence of both market and policy failures. It presents the nuts and bolts of resource economics as applied to nonrenewable r

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Book SynopsisDevelop the analytical skills that are in high demand in businesses today with Camm/Cochran/Fry/Ohlmann's best-selling BUSINESS ANALYTICS, 5E. You master the full range of analytics as you strengthen descriptive, predictive and prescriptive analytic skills. Real examples and memorable visuals clearly illustrate data and results. Step-by-step instructions guide you through using Excel, Tableau, R or the Python-based Orange data mining software to perform advanced analytics. Practical, relevant problems at all levels of difficulty let you apply what you've learned. Updates throughout this edition address topics beyond traditional quantitative concepts, such as data wrangling, data visualization and data mining, which are increasingly important in today's business environment. MindTap and WebAssign online learning platforms are also available with an interactive eBook, algorithmic practice problems and Exploring Analytics visualizations to strengthen your understanding of key concepts.Table of Contents1. Introduction. 2. Descriptive Statistics. 3. Data Visualization. 4. Data Wrangling. 5. Probability: An Introduction to Modeling Uncertainty. 6. Descriptive Data Mining. 7. Statistical Inference. 8. Linear Regression. 9. Time Series Analysis and Forecasting. 10. Predictive Data Mining: Regression. 11. Predictive Data Mining: Classification. 12. Spreadsheet Modeling. 13. Monte Carlo Simulation. 14. Linear Optimization Models. 15. Integer Linear Optimization Models. 16. Nonlinear Optimization Models. 17. Decision Analysis. Appendix A: Basics of Excel. Appendix B: Database Basics with Microsoft Access. Appendix C: Solutions to Even-Numbered Questions (online).

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Book SynopsisRenowned for its clear prose and no-nonsense emphasis on core concepts, Statistics covers fundamentals using real examples to illustrate the techniques.

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Book SynopsisStatistical Theories and Methods with Applications to Economics and Business highlights recent advances in statistical theory and methods that benefit econometric practice. It deals with exploratory data analysis, a prerequisite to statistical modelling and part of data mining. It provides recently developed computational tools useful for data mining, analysing the reasons to do data mining and the best techniques to use in a given situation. Provides a detailed description of computer algorithms. Provides recently developed computational tools useful for data mining Highlights recent advances in statistical theory and methods that benefit econometric practice. Features examples with real life data. Accompanying software featuring DASC (Data Analysis and Statistical Computing). Essential reading for practitioners in any area of econometrics; business analysts involved in economics and management; and Graduate Table of ContentsForeword xi Preface xiii Acknowledgements xvii 1 Introduction 1 1.1 Nature and Scope of Econometrics 2 1.1.1 What is Econometrics and Why Study Econometrics? 2 1.1.2 Econometrics and Scientific Credibility of Business and Economic Decisions 4 1.2 Types of Economic Problems, Types of Data, and Types of Models 5 1.2.1 Experimental Data from a Marketing Experiment 5 1.2.2 Cross-Section Data: National Sample Survey Data on Consumer Expenditure 6 1.2.3 Non-Experimental Data Taken from Secondary Sources: The Case of Pharmaceutical Industry in India 8 1.2.4 Loan Default Risk of a Customer and the Problem Facing Decision on a Loan Application 9 1.2.5 Panel Data: Performance of Banks in India by the Type of Ownership after Economic Reforms 10 1.2.6 Single Time Series Data: The Bombay Stock Exchange (BSE) Index 12 1.2.7 Multiple Time Series Data: Stock Prices in BRIC Countries 12 1.3 Pattern Recognition and Exploratory Data Analysis 14 1.3.1 Some Basic Issues in Econometric Modeling 14 1.3.2 Exploratory Data Analysis Using Correlations and Scatter Diagrams: The Relative Importance of Managerial Function and Labor 16 1.3.3 Cleaning and Reprocessing Data to Discover Patterns: BSE Index Data 22 1.4 Econometric Modeling: The Roadmap of This Book 24 1.4.1 The Econometric Modeling Strategy 24 1.4.2 Plan of the Book 25 Electronic References for Chapter 1 27 References 27 2 Independent Variables in Linear Regression Models 29 2.1 Brief Review of Linear Regression 29 2.1.1 Brief Review of Univariate Linear Regression 29 2.1.2 Brief Review of Multivariate Linear Regression 38 2.2 Selection of Independent Variable and Stepwise Regression 49 2.2.1 Principles of Selection of Independent Variables 49 2.2.2 Stepwise Regression 52 2.3 Multivariate Data Transformation and Polynomial Regression 57 2.3.1 Linear Regression after Multivariate Data Transformation 57 2.3.2 Polynomial Regression on an Independent Variable 61 2.3.3 Multivariable Polynomial Regression 62 2.4 Column Multicollinearity in Design Matrix and Ridge Regression 65 2.4.1 Effect of Column Multicollinearity of Design Matrix 65 2.4.2 Ridge Regression 68 2.4.3 Ridge Trace Analysis and Ridge Parameter Selection 70 2.4.4 Generalized Ridge Regression 71 2.5 Recombination of Independent Variable and Principal Components Regression 72 2.5.1 Concept of Principal Components Regression 72 2.5.2 Determination of Principal Component 74 Electronic References for Chapter 2 79 References 80 3 Alternative Structures of Residual Error in Linear Regression Models 83 3.1 Heteroscedasticity: Consequences and Tests for Its Existence 85 3.1.1 Consequences of Heteroscedasticity 85 3.1.2 Tests for Heteroscedasticity 87 3.2 Generalized Linear Model with Covariance Being a Diagonal Matrix 90 3.2.1 Diagonal Covariance Matrix and Weighted Least Squares 90 3.2.2 Model with Two Unknown Variances 91 3.2.3 Multiplicative Heteroscedastic Model 92 3.3 Autocorrelation in a Linear Model 95 3.3.1 Linear Model with First-Order Residual Autoregression 96 3.3.2 Autoregressive Conditional Heteroscedasticity (ARCH) Model 101 3.4 Generalized Linear Model with Positive Definite Covariance Matrix 106 3.4.1 Model Definition, Parameter Estimation and Hypothesis Tests 106 3.4.2 Some Equivalent Conditions 108 3.5 Random Effects and Variance Component Model 109 3.5.1 Random Effect Regression Model 109 3.5.2 The Variance Component Model 112 3.5.3 Analysis of Variance Method to Solve Variance Component Model 113 3.5.4 Minimum Norm Quadratic Unbiased Estimation (MINQUE) to Solve Variance Component 121 3.5.5 Maximum Likelihood Method to Solve Variance Component Model 124 Electronic References for Chapter 3 125 References 125 4 Discrete Variables and Nonlinear Regression Model 129 4.1 Regression Model When Independent Variables are Categorical 130 4.1.1 Problem About Wage and Gender Differences 131 4.1.2 Structural Changes in the Savings Function (Use of Categorical Variables in Combination with Continuous Variables) 133 4.1.3 Cross Section Analysis 138 4.1.4 Seasonal Analysis Model 141 4.2 Models with Categorical or Discrete Dependent Variables 144 4.2.1 Linear Model with Binary Dependent Variable 144 4.2.2 Logit Regression Model 148 4.2.3 Probit Regression Model 153 4.2.4 Tobit Regression Model 154 4.3 Nonlinear Regression Model and Its Algorithm 160 4.3.1 The Least Squares Estimate for Nonlinear Regression Model 162 4.3.2 Maximum Likelihood Estimation of Nonlinear Regression Model 164 4.3.3 Equivalence of Maximum Likelihood Estimation and Least Squares Estimation 166 4.4 Nonlinear Regression Models in Practice 169 4.4.1 Growth Curve Models 169 4.4.2 Box–Cox Transformation Model 176 4.4.3 Survival Data and Failure Rate Model 177 4.4.4 Total Factor Productivity (TFP) 181 Electronic References for Chapter 4 188 References 188 5 Nonparametric and Semiparametric Regression Models 193 5.1 Nonparametric Regression and Weight Function Method 194 5.1.1 The Concept of Nonparametric Regression 194 5.1.2 Weight Function Method 196 5.2 Semiparametric Regression Model 199 5.2.1 Linear Semiparametric Regression Model 202 5.2.2 Single-Index Semiparametric Regression Model 205 5.3 Stochastic Frontier Regression Model 208 5.3.1 Stochastic Frontier Linear Regression Model and Asymptotically Efficient Estimator of Its Parameters 208 5.3.2 Semiparametric Stochastic Frontier Model 210 Electronic References for Chapter 5 212 References 213 6 Simultaneous Equations Models and Distributed Lag Models 215 6.1 Simultaneous Equations Models and Inconsistency of OLS Estimators 216 6.1.1 Demand-and-Supply Model, Keynesian Model and Wage-Price Model (Phillips Curve) 218 6.1.2 Macroeconomic IS Model, LM Model and Klein’s Econometric Model 220 6.1.3 Inconsistency of OLS Estimation 222 6.2 Statistical Inference for Simultaneous Equations Models 223 6.2.1 Indirect Least Squares and Generalized Least Squares 224 6.2.2 Two Stage Least Squares 229 6.3 The Concepts of Lag Regression Models 235 6.3.1 Consumption Lag 236 6.3.2 Inflation Lag 237 6.3.3 Deposit Re-Creation 238 6.4 Finite Distributed Lag Models 239 6.4.1 Estimation of Distributed Lag Models When the Lag Length is Known and Finite 239 6.4.2 The Determination of Distributed Lag Length 239 6.5 Infinite Distributed Lag Models 242 6.5.1 Adaptive Expectations Model and Partial Adjustment Model 243 6.5.2 Koyck Transformation and Estimation of Geometric Lag Models 245 Electronic References for Chapter 6 249 References 250 7 Stationary Time Series Models 253 7.1 Auto-Regression Model AR( p) 255 7.1.1 AR( p) Model and Stationarity 255 7.1.2 Auto-Covariance Function and Autocorrelation Function of AR( p) Model 258 7.1.3 Spectral Density of AR( p) Model and Partial Correlation Coefficient 263 7.1.4 Estimation of Parameters for AR( p) Model with Known Order p 267 7.1.5 Order Identification for AR( p) Process 274 7.2 Moving Average Model MA(q) 276 7.2.1 MA(q) Model and Its Properties 276 7.2.2 Parameter Estimation of MA(q) Model When the Order q is Known 278 7.2.3 Spectral Density Estimation for MA(q) Process 282 7.2.4 Order Identification for MA(q) Process 284 7.3 Auto-Regressive Moving-Average Process ARMA( p, q) 285 7.3.1 ARMA(p, q) Model and Its Properties 285 7.3.2 Parameter Estimations for ARMA(p, q) Model 288 7.3.3 Test for ARMA( p, q) Model 291 7.3.4 Order Identification for ARMA( p, q) Model 291 7.3.5 Univariate Time Series Modeling: The Basic Issues and Approaches 292 Electronic References for Chapter 7 293 References 293 8 Multivariate and Nonstationary Time Series Models 297 8.1 Multivariate Stationary Time Series Model 299 8.1.1 General Description of Multivariable Stationary Time Series Model 299 8.1.2 Estimation of Mean and Autocovariance Function of Multivariate Stationary Time Series 300 8.1.3 Vector Autoregression Model of Order p: VAR( p) 301 8.1.4 Wold Decomposition and Impulse-Response 301 8.1.5 Variance Decomposition with VAR( p) 306 8.1.6 Granger Causality with VAR(p) Specification 309 8.2 Nonstationary Time Series 311 8.2.1 Stochastic Trends and Unit Root Processes 311 8.2.2 Test for Unit Root Hypothesis 314 8.3 Cointegration and Error Correction 321 8.3.1 The Concept and Representation of Cointegration 322 8.3.2 Simultaneous (Structural) Equation System (SES) and Vector Auto Regression (VAR) 324 8.3.3 Cointegration and Error Correction Representation 325 8.3.4 Estimation of Parameters of Cointegration Process 329 8.3.5 Test of Hypotheses on the Number of Cointegrating Equations 330 8.4 Autoregression Conditional Heteroscedasticity in Time Series 333 8.4.1 ARCH Model 334 8.4.2 Generalized ARCH Model—GARCH Model 338 8.4.3 Other Generalized Forms of ARCH Model 342 8.5 Mixed Models of Multivariate Regression with Time Series 346 8.5.1 Mixed Model of Multivariate Regression with Time Series 346 8.5.2 Mixed Model of Multivariate Regression and Cointegration with Time Series 349 Electronic References for Chapter 8 353 References 353 9 Multivariate Statistical Analysis and Data Analysis 357 9.1 Model of Analysis of Variance 358 9.1.1 Single Factor Analysis of Variance Model 358 9.1.2 Two Factor Analysis of Variance with Non-Repeated Experiment 361 9.1.3 Two Factor Analysis of Variance with Repeated Experiment 364 9.2 Other Multivariate Statistical Analysis Models 370 9.2.1 Discriminate Analysis Model 370 9.2.2 Factor Analysis Model 376 9.2.3 Principal Component Analysis and Multidimensional Scaling Method 380 9.2.4 Canonical Correlation Analysis 384 9.3 Customer Satisfaction Model and Path Analysis 387 9.3.1 Customer Satisfaction Model and Structural Equations Model 387 9.3.2 Partial Least Square and the Best Iterative Initial Value 391 9.3.3 Definite Linear Algorithm for SEM 399 9.3.4 Multi-Layers Path Analysis Model 402 9.4 Data Analysis and Process 404 9.4.1 Panel Data Analysis 404 9.4.2 Truncated Data Analysis 405 9.4.3 Censored Data Analysis 406 9.4.4 Duration Data Analysis 407 9.4.5 High Dimensional Data Visualization 409 Electronic References for Chapter 9 412 References 413 10 Summary and Further Discussion 415 10.1 About Probability Distributions: Parametric and Non-Parametric 416 10.1.1 Distributions of Functions of Random Variables 416 10.1.2 Parametric, Non-Parametric, and Semi-Parametric Specification of Distributions 417 10.1.3 Non-Parametric Specification of Density Functions 418 10.2 Regression 421 10.2.1 Regression as Conditional Mean of the Dependent Variable 421 10.2.2 Regressions with Homoscedastic and Heteroscedastic Variance 421 10.2.3 General Regression Functions: Quantiles and Quantile Regression 423 10.2.4 Design of Experiments, Regression, and Analysis of Variance 424 10.3 Model Specification and Prior Information 425 10.3.1 Data Generation Process (DGP) and Economic Structure 426 10.3.2 Deterministic but Unknown Parameters and Model Specification as a Maintained Hypothesis 428 10.3.3 Stochastic Prior Information on Unknown Parameters 429 10.4 Classical Theory of Statistical Inference 430 10.4.1 The Likelihood Function, Sufficient Statistics, Complete Statistics, and Ancillary Statistics 430 10.4.2 Different Methods of Estimation of Unknown Parameters 434 10.4.3 Biased and Unbiased Estimators, Consistency of Estimators 437 10.4.4 Information Limit to Variance of an Estimator, Cramer-Rao Bound, and Rao-Blackwell Theorem 438 10.4.5 Approximate Sufficiency and Robust Estimation 440 10.5 Computation of Maximum Likelihood Estimates 441 10.5.1 Newton-Raphson Method and Rao’s Method of Scoring 442 10.5.2 Davidon-Fletcher-Powell-Reeves Conjugate Gradient Procedure 443 10.5.3 Estimates of the Variance Covariance Matrix of Maximum Likelihood Estimators 444 10.6 Specification Searches 445 10.6.1 Choice Between Alternate Specifications: Akaike and Schwarz Information Criteria 445 10.6.2 Generalized Information and Complexity-Based Model Choice Criterion 447 10.6.3 An Illustration of Model Choice: Engel Curve for Food Consumption in India 448 10.7 Resampling and Sampling Distributions – The Bootstraps Method 450 10.7.1 The Concept of Resampling and the Bootstraps Method 450 10.7.2 Bootstraps in Regression Models 452 10.8 Bayesian Inference 454 10.8.1 The Bayes Rule 454 10.8.2 Choice of Prior Probability Distribution for the Parameter 455 10.8.3 Bayesian Concepts for Statistical Inference 456 Electronic References for Chapter 10 457 References 458 Index 461

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Book SynopsisA guide to the growing importance of extreme value risk theory, methods, and applications in the financial sector Presenting a uniquely accessible guide, Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications features a combination of the theory, methods, and applications of extreme value theory (EVT) in finance and a practical understanding of market behavior including both ordinary and extraordinary conditions. Beginning with a fascinating history of EVTs and financial modeling, the handbook introduces the historical implications that resulted in the applications and then clearly examines the fundamental results of EVT in finance. After dealing with these theoretical results, the handbook focuses on the EVT methods critical for data analysis. Finally, the handbook features the practical applications and techniques and how these can be implemented in financial markets. Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications includes: Over 40 contributions from international experts in the areas of finance, statistics, economics, business, insurance, and risk managementTopical discussions on univariate and multivariate case extremes as well as regulation in financial marketsExtensive references in order to provide readers with resources for further studyDiscussions on using R packages to compute the value of risk and related quantities The book is a valuable reference for practitioners in financial markets such as financial institutions, investment funds, and corporate treasuries, financial engineers, quantitative analysts, regulators, risk managers, large-scale consultancy groups, and insurers. Extreme Events in Finance: A Handbook of Extreme Value Theory and Its Applications is also a useful textbook for postgraduate courses on the methodology of EVTs in finance.Table of ContentsAbout the Editor xiii About the Contributors xv 1 Introduction 1François Longin 1.1 Extremes 1 1.2 History 2 1.3 Extreme value theory 2 1.4 Statistical estimation of extremes 2 1.5 Applications in finance 4 1.6 Practitioners’ points of view 6 1.7 A broader view on modeling extremes 6 1.8 Final words 7 1.9 Thank you note 7 References 8 2 Extremes Under Dependence—Historical Development and Parallels with Central Limit Theory 11M.R. Leadbetter 2.1 Introduction 11 2.2 Classical (I.I.D.) central limit and extreme value theories 12 2.3 Exceedances of levels, kth largest values 14 2.4 CLT and EVT for stationary sequences, bernstein’s blocks, and strong mixing 15 2.5 Weak distributional mixing for EVT, D(un), extremal index 18 2.6 Point process of level exceedances 19 2.7 Continuous parameter extremes 20 References 22 3 The Extreme Value Problem in Finance: Comparing the Pragmatic Program with the Mandelbrot Program 25Christian Walter 3.1 The extreme value puzzle in financial modeling 25 3.2 The sato classification and the two programs 28 3.3 Mandelbrot’s program: A fractal approach 34 3.4 The Pragmatic Program: A data-driven approach 39 3.5 Conclusion 47 Acknowledgments 48 References 48 4 Extreme Value Theory: An Introductory Overview 53Isabel Fraga Alves and Cláudia Neves 4.1 Introduction 53 4.2 Univariate case 56 4.3 Multivariate case: Some highlights 84 Further reading 90 Acknowledgments 90 References 90 5 Estimation of the Extreme Value Index 97Beirlant J., Herrmann K., and Teugels J.L. 5.1 Introduction 97 5.2 The main limit theorem behind extreme value theory 98 5.3 Characterizations of the max-domains of attraction and extreme value index estimators 99 5.4 Consistency and asymptotic normality of the estimators 103 5.5 Second-order reduced-bias estimation 104 5.6 Case study 106 5.7 Other topics and comments 108 References 111 6 Bootstrap Methods in Statistics of Extremes 117M. Ivette Gomes, Frederico Caeiro, Lígia Henriques-Rodrigues, and B.G. Manjunath 6.1 Introduction 117 6.2 A few details on EVT 119 6.3 The bootstrap methodology in statistics of univariate extremes 127 6.4 Applications to simulated data 133 6.5 Concluding remarks 133 Acknowledgments 135 References 135 7 Extreme Values Statistics for Markov Chains with Applications to Finance and Insurance 139Patrice Bertail, Stéphan Clémençon, and Charles Tillier 7.1 Introduction 139 7.2 On the (pseudo) regenerative approach for markovian data 141 7.3 Preliminary results 151 7.4 Regeneration-based statistical methods for extremal events 154 7.5 The extremal index 156 7.6 The regeneration-based hill estimator 159 7.7 Applications to ruin theory and financial time series 161 7.8 An application to the CAC40 165 7.9 Conclusion 167 References 167 8 Lévy Processes and Extreme Value Theory 171Olivier Le Courtois and Christian Walter 8.1 Introduction 171 8.2 Extreme value theory 173 8.3 Infinite divisibility and Lévy processes 178 8.4 Heavy-tailed Lévy processes 182 8.5 Semi-heavy-tailed Lévy processes 184 8.6 Lévy processes and extreme values 187 8.7 Conclusion 192 References 192 9 Statistics of Extremes: Challenges and Opportunities 195M. de Carvalho 9.1 Introduction 195 9.2 Statistics of bivariate extremes 196 9.3 Models based on families of tilted measures 204 9.4 Miscellanea 209 References 211 10 Measures of Financial Risk 215S.Y. Novak 10.1 Introduction 215 10.2 Traditional measures of risk 215 10.3 Risk estimation 218 10.4 “Technical analysis” of financial data 222 10.5 Dynamic risk measurement 226 10.6 Open problems and further research 234 10.7 Conclusion 235 Acknowledgment 235 References 235 11 On the Estimation of the Distribution of Aggregated Heavy-Tailed Risks: Application to Risk Measures 239Marie Kratz 11.1 Introduction 239 11.2 A brief review of existing methods 245 11.3 New approaches: Mixed limit theorems 247 11.4 Application to risk measures and comparison 269 11.5 Conclusion 277 References 279 12 Estimation Methods for Value at Risk 283Saralees Nadarajah and Stephen Chan 12.1 Introduction 283 12.2 General properties 289 12.3 Parametric methods 300 12.4 Nonparametric methods 326 12.5 Semiparametric methods 332 12.6 Computer software 344 12.7 Conclusions 347 Acknowledgment 347 References 347 13 Comparing Tail Risk and Systemic Risk Profiles for Different Types of U.S. Financial Institutions 357Stefan Straetmans and Thanh Thi Huyen Dinh 13.1 Introduction 357 13.2 Tail risk and systemic risk indicators 361 13.3 Tail risk and systemic risk estimation 364 13.4 Empirical results 368 13.5 Conclusions 381 References 382 14 Extreme Value Theory and Credit Spreads 391Wesley Phoa 14.1 Preliminaries 391 14.2 Tail behavior of credit markets 394 14.3 Some multivariate analysis 398 14.4 Approximating value at risk for credit portfolios 401 14.5 Other directions 403 References 404 15 Extreme Value Theory and Risk Management in Electricity Markets 405Kam Fong Chan and Philip Gray 15.1 Introduction 405 15.2 Prior literature 407 15.3 Specification of VaR estimation approaches 409 15.4 Empirical analysis 413 15.5 Conclusion 422 Acknowledgment 423 References 423 16 Margin Setting and Extreme Value Theory 427John Cotter and Kevin Dowd 16.1 Introduction 427 16.2 Margin setting 428 16.3 Theory and methods 430 16.4 Empirical results 434 16.5 Conclusions 439 Acknowledgment 440 References 440 17 The Sortino Ratio and Extreme Value Theory: An Application to Asset Allocation 443G. Geoffrey Booth and John Paul Broussard 17.1 Introduction 443 17.2 Data definitions and description 446 17.3 Performance ratios and their estimations 451 17.4 Performance measurement results and implications 456 17.5 Concluding remarks 460 Acknowledgments 461 References 461 18 Portfolio Insurance: The Extreme Value Approach Applied to the CPPI Method 465Philippe Bertrand and Jean-Luc Prigent 18.1 Introduction 465 18.2 The CPPI method 467 18.3 CPPI and quantile hedging 472 18.4 Conclusion 481 References 481 19 The Choice of the Distribution of Asset Returns: How Extreme Value Can Help? 483François Longin 19.1 Introduction 483 19.2 Extreme value theory 485 19.3 Estimation of the tail index 488 19.4 Application of extreme value theory to discriminate among distributions of returns 490 19.5 Empirical results 493 19.6 Conclusion 501 References 501 20 Protecting Assets Under Non-Parametric Market Conditions 507Jean-Marie Choffray and Charles Pahud de Mortanges 20.1 Investors’ “known knowns” 509 20.2 Investors’ “known unknowns” 512 20.3 Investors’ “unknown knowns” 515 20.4 Investors’ “unknown unknowns” 518 20.5 Synthesis 522 References 523 21 EVT Seen by a Vet: A Practitioner’s Experience on Extreme Value Theory 525Jean-François Boulier 21.1 What has the vet done? 525 21.2 Why use EVT? 526 21.3 What EVT could additionally bring to the party? 528 21.4 A final thought 528 References 528 22 The Robotization of Financial Activities: A Cybernetic Perspective 529Hubert Rodarie 22.1 An increasingly complex system 530 22.2 Human error 532 22.3 Concretely, what do we need to do to transform a company into a machine? 534 References 543 23 Two Tales of Liquidity Stress 545Jacques Ninet 23.1 The french money market fund industry. How history has shaped a potentially vulnerable framework 546 23.2 The 1992–1995 forex crisis 547 23.3 Four mutations paving the way for another meltdown 549 23.4 The subprime crisis spillover. How some MMFs were forced to lock and some others not 551 23.5 Conclusion. What lessons can be drawn from these two tales? 552 Further Readings 553 24 Managing Operational Risk in the Banking Business – An Internal Auditor Point of View 555Maxime Laot Further Reading 559 References 560 Annexes 560 25 Credo Ut Intelligam 563Henri Bourguinat and Eric Briys 25.1 Introduction 563 25.2 “Anselmist” finance 563 25.3 Casino or dance hall? 565 25.4 Simple-minded diversification 566 25.5 Homo sapiens versus homo economicus 568 Acknowledgement 569 References 569 26 Bounded Rationalities, Routines, and Practical as well as Theoretical Blindness: On the Discrepancy Between Markets and Corporations 571Laurent Bibard 26.1 Introduction: Expecting the unexpected 571 26.2 Markets and corporations: A structural and self-disruptive divergence of interests 572 26.3 Making a step back from a dream: On people expectations 574 26.4 How to disentangle people from a unilateral short-term orientation? 578 References 580 Name Index 583 Subject Index 593

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Book SynopsisProviding a solid managerial perspective, PURCHASING AND SUPPLY CHAIN MANAGEMENT, 6e draws from the authors' firsthand experiences and relationships with executives and practitioners worldwide to present the most current and complete coverage of today's supply management process. The text includes critical developments from the field, such as cases from emerging healthcare and service industries, procure-to-pay redesign, supply risk, innovation, sustainability, collaboration, and much more. It also examines key changes in supply management and the impact of the global economy and ongoing business uncertainty on continuous cost and value management across the supply chain. Numerous real-world cases and captivating examples give you contextual insights and knowledge into the strategies, processes, and practices of supply management. PURCHASING AND SUPPLY CHAIN MANAGEMENT, 6E equips future managers with a thorough understanding of the impact that purchasing and supply chain management havTable of ContentsPart 1: INTRODUCTION. 1. Introduction to Purchasing and Supply Chain Management. Part 2: PURCHASING OPERATIONS AND STRUCTURE. 2. The Purchasing Process. 3. Purchasing Policies and Procedures. 4. Supply Management Integration for Competitive Advantage. 5. Purchasing and Supply Management Organization. Part 3: STRATEGIC SOURCING. 6. Supply Management and Commodity Strategy Development. 7. Supplier Evaluation and Selection. 8. Supplier Quality Management. 9. Supplier Management and Development: Creating a World-Class Supply Base. 10. Worldwide Sourcing. Part 4: STRATEGIC SOURCING PROCESS. 11. Strategic Cost Management. 12. Purchasing and Supply Chain Analysis: Tools and Techniques. 13. Negotiation and Conflict Management. 14. Contract Management. 15. Purchasing Law and Ethics. Part 5: CRITICAL SUPPLY CHAIN ELEMENTS. 16. Lean Supply Chain Management. 17. Purchasing Services. 18. Supply Chain Information Systems and Electronic Sourcing. 19. Performance Measurement and Evaluation. Part 6: FUTURE DIRECTIONS. 20. Purchasing and Supply Strategy Trends.

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Book SynopsisHelps you put statistical theories into practice. This calculus-based book offers a comprehensive introduction to probability and statistics while demonstrating how to apply concepts, models, and methodologies in today's engineering and scientific workplaces.Table of Contents1. OVERVIEW AND DESCRIPTIVE STATISTICS. Populations, Samples, and Processes. Pictorial and Tabular Methods in Descriptive Statistics. Measures of Location. Measures of Variability. 2. PROBABILITY. Sample Spaces and Events. Axioms, Interpretations, and Properties of Probability. Counting Techniques. Conditional Probability. Independence. 3. DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Random Variables. Probability Distributions for Discrete Random Variables. Expected Values. The Binomial Probability Distribution. Hypergeometric and Negative Binomial Distributions. The Poisson Probability Distribution. 4. CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS. Probability Density Functions. Cumulative Distribution Functions and Expected Values. The Normal Distribution. The Exponential and Gamma Distributions. Other Continuous Distributions. Probability Plots. 5. JOINT PROBABILITY DISTRIBUTIONS AND RANDOM SAMPLES. Jointly Distributed Random Variables. Expected Values, Covariance, and Correlation. Statistics and Their Distributions. The Distribution of the Sample Mean. The Distribution of a Linear Combination. 6. POINT ESTIMATION. Some General Concepts of Point Estimation. Methods of Point Estimation. 7. STATISTICAL INTERVALS BASED ON A SINGLE SAMPLE. Basic Properties of Confidence Intervals. Large-Sample Confidence Intervals for a Population Mean and Proportion. Intervals Based on a Normal Population Distribution. Confidence Intervals for the Variance and Standard Deviation of a Normal Population. 8. TESTS OF HYPOTHESIS BASED ON A SINGLE SAMPLE. Hypotheses and Test Procedures. z Tests for Hypotheses About a Population Mean. The One-Sample t Test. Tests Concerning a Population Proportion. Further Aspects of Hypothesis Testing. 9. INFERENCES BASED ON TWO SAMPLES. z Tests and Confidence Intervals for a Difference between Two Population Means. The Two-Sample t Test and Confidence Interval. Analysis of Paired Data. Inferences Concerning a Difference between Population Proportions. Inferences Concerning Two Population Variances. 10. THE ANALYSIS OF VARIANCE. Single-Factor ANOVA. Multiple Comparisons in ANOVA. More on Single-Factor ANOVA. 11. MULTIFACTOR ANALYSIS OF VARIANCE. Two-Factor ANOVA with Kij = 1. Two-Factor ANOVA with Kij > 1. Three-Factor ANOVA 11. 4 2p Factorial Experiments. 12. SIMPLE LINEAR REGRESSION AND CORRELATION. The Simple Linear Regression Model. Estimating Model Parameters. Inferences About the Slope Parameter ss1. Inferences Concerning Y*x* and the Prediction of Future Y Values. Correlation. 13. NONLINEAR AND MULTIPLE REGRESSION. Assessing Model Adequacy. Regression with Transformed Variables. Polynomial Regression. Multiple Regression Analysis. Other Issues in Multiple Regression. 14. GOODNESS-OF-FIT TESTS AND CATEGORICAL DATA ANALYSIS. Goodness-of-Fit Tests When Category Probabilities Are Completely Specified. Goodness-of-Fit Tests for Composite Hypotheses. Two-Way Contingency Tables 15. DISTRIBUTION-FREE PROCEDURES. The Wilcoxon Signed-Rank Test. The Wilcoxon Rank-Sum Test. Distribution-Free Confidence Intervals. Distribution-Free ANOVA. 16. QUALITY CONTROL METHODS. General Comments on Control Charts. Control Charts for Process Location. Control Charts for Process Variation. Control Charts for Attributes. CUSUM Procedures. Acceptance Sampling.

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Book SynopsisCounting Bounty highlights a widespread blindspot: most of us overlook land and its power to twist an economy. Householders typically spend most of their budget on land without awareness. The story begins with the official and academic efforts to minimize the total worth of Earth in America. A perusal of the historical relationship between the elite and the intellectual shows that "paying the piper" is the norm, even up to the present. Using a slew of statistics and others’ research findings, this book tracks rent to its recipients, the rentiers who own much and wield power. Aware reformers can address pressing problems by tapping land value. Watching rent flow sheds light on how economies operate, why they sometimes fail, and what a society can do about it.Trade Review"The vast number of references and the apt details reflects the enormous amount of expertise and time which has been invested in it." -- Team PlanningTank"Land and money are the two main elements in political economy. Jeff Smith has been digging into "the land problem" for a long time and his expertise in that subject is without question. His findings deserve a wide audience as we struggle to bring into being a more just, equitable, and sustainable world order. In this book, Smith reveals many little-known facts about things that affect our lives, particularly land ownership, the process of rent-seeking, the concentration of wealth, and the corruption of politics, education, and other aspects of society by which the one percent continue to control the general framework of public thought." -- Thomas H Greco, author of The End of Money and the Future of Civilization"All property is made partly out of natural resources that aren't 'naturally' anybody's property. The government makes them into property. It gives them to private interests for free, and they sell it back to us for money. That might be an opportunity for corruption. This book explains problems caused by the way the world's governments dole out resources to the privileged and the potential of a better resource policy." -- Karl Widerquist, an American political philosopher and economist at Georgetown University-Qatar, is co-founder if the US Basic Income Guarantee (USBIG) Network, has been co-chair of the Basic Income Earth Network (BIEN) since 2008, and co-founded Basic Income News in 2011

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Book SynopsisLa econometría aplicada es la ciencia de los datos en su estado original, y engloba los métodos estadísticos que se usan en economía para desentrañar causas y efectos de las actividades humanas. Con un lenguaje accesible y algunas dosis de humor con sabor a kung-fu, esta obra expone las herramientas esenciales del análisis econométrico y desvela por qué la econometría es una disciplina tan apasionante y útil.¿Mejoran la salud los seguros médicos? ¿Son mejores las universidades de élite que otro tipo de centros académicos? Cuando la banca privada se tambalea, y los inversores toman el dinero y huyen, ¿deben acudir en su ayuda los bancos centrales? Angrist y Pischke nos muestran que, con los métodos adecuados, la econometría es capaz de ofrecernos respuestas a preguntas tan dispares como estas.

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