Econometrics and economic statistics Books
Princeton University Press The Econometrics of Financial Markets
Book SynopsisCovers the spectrum of empirical finance, including the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, and the term structure of interest rates, dynamic models of economic equilibrium.Trade ReviewWinner of the 2014 Eugene Fama Prize for Outstanding Contributions to Doctoral Education, University of Chicago Booth School of Business Winner of the 1997 Award for Best Professional/Scholarly Book in Economics, Association of American Publishers Winner of the 1997 Paul A. Samuelson Award, TIAA-CREF "The definitive work explaining this complex but important field of academic endeavor. Oh, and by the way, it's not just academic. The big question that financial econometircs addresses is: What can you learn about the future from the financial data available from the past? This broad issue can be specified in many different ways, and all the important ones are discussed in the book... The vast literature on all the topics examined is assessed, rendered coherent, and then analysed by three men who themselves have made significant advances in the field."--Ruben Lee, London Financial Market "This book is sophisticated, yet accessible; full of details, yet intriguing... Instructors will appreciate the attempt to make each chapter as self contained as possible which leaves them free to choose specified sequences of topics. Professionals will be pleased with the quick and authoritative introductions to important areas of Finance... [A] well written introduction (indeed, something more) to Financial Econometrics. It is alert, explicit and articulate about assumptions... a splendid offering... "--Maurizio Tiso, Review of Financial Studies "Written by the "A" team of financial empiricism, it is a long awaited book. It covers many topics one could only usually find couched in the technical jargon of research papers, presented in this volume with pedagogical intentions. The language, while remaining technical, is quite accessible. It can be effortlessly read by scientific traders with standard knowledge of statistical methods... This book should be made mandatory reading in research departments."--Derivative StrategiesTable of ContentsList of Figures xiii List of Tables xv Preface xix 1Introduction 3 1.1 Organization of the Book 4 1.2 Useful Background 6 1.2.1 Mathematics Background 6 1.2.2 Probability and Statistics Background 6 1.2.3 Finance Theory Background 7 1.3 Notation 8 1.4 Prices, Returns, and Compounding 9 1.4.1 Definitions and Conventions 9 1.4.2 The Marginal, Conditional, and Joint Distribution of Returns 13 1.5 Market Efficiency 20 1.5.1 Efficient Markets and the Law of Iterated Expectations 22 1.5.2 Is Market Efficiency Testable? 24 2The Predictability of Asset Returns 27 2.1 The Random Walk Hypotheses 28 2.1.1 The Random Walk 1: IID Increments 31 2.1.2 The Random Walk 2: Independent Increments 32 2.1.3 The Random Walk 3: Uncorrelated Increments 33 2.2 Tests of Random Walk 1: IID Increments 33 2.2.1 Traditional Statistical Tests 33 2.2.2 Sequences and Reversals, and Runs 34 2.3 Tests of Random Walk 2: Independent Increments 41 2.3.1 Filter Rules 42 2.3.2 Technical Analysis 43 2.4 Tests of Random Walk 3: Uncorrelated Increments 44 2.4.1 Autocorrelation Coefficients 44 2.4.2 Portmanteau Statistics 47 2.4.3 Variance Ratios 48 2.5 Long-Horizon Returns 55 2.5.1 Problems with Long-Horizon Inferences 57 2.6 Tests For Long-Range Dependence 59 2.6.1 Examples of Long-Range Dependence 59 2.6.2 The Hurst-Mandelbrot Rescaled Range Statistic 62 2.7 Unit Root Tests 64 2.8 Recent Empirical Evidence 65 2.8.1 Autocorrelations 66 2.8.2 Variance Ratios 68 2.8.3 Cross-Autocorrelations and Lead-Lag Relations 74 2.8.4 Tests Using Long-Horizon Returns 78 2.9 Conclusion 80 3Market Microstructure 83 3.1 Nonsynchronous Trading 84 3.1.1 A Model of Nonsynchronous Trading 85 3.1.2 Extensions and Generalizations 98 3.2 The Bid-Ask Spread 99 3.2.1 Bid-Ask Bounce 101 3.2.2 Components of the Bid-Ask Spread 103 3.3 Modeling Transactions Data 107 3.3.1 Motivation 108 3.3.2 Rounding and Barrier Models 114 3.3.3 The Ordered Probit Model 122 3.4 Recent Empirical Findings 128 3.4.1 Nonsynchronous Trading 128 3.4.2 Estimating the Effective Bid-Ask Spread 134 3.4.3 Transactions Data 136 3.5 Conclusion 144 4Event-Study Analysis 149 4.1 Outline of an Event Study 150 4.2 An Example of an Event Study 152 4.3 Models for Measuring Normal Performance 153 4.3.1 Constant-Mean-Return Model 154 4.3.2 Market Model 155 4.3.3 Other Statistical Models 155 4.3.4 Economic Models 156 4.4 Measuring and Analyzing Abnormal Returns 157 4.4.1 Estimation of the Market Model 158 4.4.2 Statistical Properties of Abnormal Returns 159 4.4.3 Aggregation of Abnormal Returns 160 4.4.4 Sensitivity to Normal Return Model 162 4.4.5 CARs for the Earnings-Announcement Example 163 4.4.6 Inferences with Clustering 166 4.5 Modifying the Null Hypothesis 167 4.6 Analysis of Power 168 4.7 Nonparametric Tests 172 4.8 Cross-Sectional Models 173 4.9 Further Issues 175 4.9.1 Role of the Sampling Interval 175 4.9.2 Inferences with Event-Date Uncertainty 176 4.9.3 Possible Biases 177 4.10 Conclusion 178 5The Capital Asset Pricing Model 181 5.1 Review of the CAPM 181 5.2 Results from Efficient-Set Mathematics 184 5.3 Statistical Framework for Estimation and Testing 188 5.3.1 Sharpe-Lintner Version 189 5.3.2 Black Version 196 5.4 Size of Tests 203 5.5 Power of Tests 204 5.6 Nonnormal and Non-IID Returns 208 5.7 Implementation of Tests 211 5.7.1 Summary of Empirical Evidence 211 5.7.2 Illustrative Implementation 212 5.7.3 Unobservability of the Market Portfolio 213 5.8 Cross-Sectional Regressions 215 5.9 Conclusion 217 6Multifactor Pricing Models 219 6.1 Theoretical Background 219 6.2 Estimation and Testing 222 6.2.1 Portfolios as Factors with a Riskfree Asset 223 6.2.2 Portfolios as Factors without a Riskfree Asset 224 6.2.3 Macroeconomic Variables as Factors 226 6.2.4 Factor Portfolios Spanning the Mean-Variance\protect\\ Frontier 228 6.3 Estimation of Risk Premia and Expected Returns 231 6.4 Selection of Factors 233 6.4.1 Statistical Approaches 233 6.4.2 Number of Factors 238 6.4.3 Theoretical Approaches 239 6.5 Empirical Results 240 6.6 Interpreting Deviations from Exact Factor Pricing 242 6.6.1 Exact Factor Pricing Models, Mean-Variance Analysis, and the Optimal Orthogonal Portfolio 243 6.6.2 Squared Sharpe Ratios 245 6.6.3 Implications for Separating Alternative Theories 246 6.7 Conclusion 251 7Present-Value Relations 253 7.1 The Relation between Prices, Dividends, and Returns 254 7.1.1 The Linear Present-Value Relation with Constant Expected Returns 255 7.1.2 Rational Bubbles 258 7.1.3 An Approximate Present-Value Relation with Time-Varying Expected Returns 260 7.1.4 Prices and Returns in a Simple Example 264 7.2 Present-Value Relations and US Stock Price Behavior 267 7.2.1 Long-Horizon Regressions 267 7.2.2 Volatility Tests 275 7.2.3 Vector Autoregressive Methods 279 7.3 Conclusion 286 8Intertemporal Equilibrium Models 291 8.1 The Stochastic Discount Factor 293 8.1.1 Volatility Bounds 296 8.2 Consumption-Based Asset Pricing with Power Utility 304 8.2.1 Power Utility in a Lognormal Model 306 8.2.2 Power Utility and Generalized Method of\protect\\ Moments 314 8.3 Market Frictions 314 8.3.1 Market Frictions and Hansen-Jagannathan\protect\\ Bounds 315 8.3.2 Market Frictions and Aggregate Consumption\protect\\ Data 316 8.4 More General Utility Functions 326 8.4.1 Habit Formation 326 8.4.2 Psychological Models of Preferences 332 8.5 Conclusion 334 9Derivative Pricing Models 339 9.1 Brownian Motion 341 9.1.1 Constructing Brownian Motion 341 9.1.2 Stochastic Differential Equations 346 9.2 A Brief Review of Derivative Pricing Methods 349 9.2.1 The Black-Scholes and Merton Approach 350 9.2.2 The Martingale Approach 354 9.3 Implementing Parametric Option Pricing Models 355 9.3.1 Parameter Estimation of Asset Price Dynamics 356 9.3.2 Estimating $\sigma $ in the Black-Scholes Model 361 9.3.3 Quantifying the Precision of Option Price Estimators 367 9.3.4 The Effects of Asset Return Predictability 369 9.3.5 Implied Volatility Estimators 377 9.3.6 Stochastic Volatility Models 379 9.4 Pricing Path-Dependent Derivatives Via Monte Carlo Simulation 382 9.4.1 Discrete Versus Continuous Time 383 9.4.2 How Many Simulations to Perform 384 9.4.3 Comparisons with a Closed-Form Solution 384 9.4.4 Computational Efficiency 386 9.4.5 Extensions and Limitations 390 9.5 Conclusion 391 10Fixed-Income Securities 395 10.1 Basic Concepts 396 10.1.1 Discount Bonds 397 10.1.2 Coupon Bonds 401 10.1.3 Estimating the Zero-Coupon Term Structure 409 10.2 Interpreting the Term Structure of Interest Rates 413 10.2.1 The Expectations Hypothesis 413 10.2.2 Yield Spreads and Interest Rate Forecasts 418 10.3 Conclusion 423 11Term-Structure Models 427 11.1 Affine-Yield Models 428 11.1.1 A Homoskedastic Single-Factor Model 429 11.1.2 A Square-Root Single-Factor Model 435 11.1.3 A Two-Factor Model 438 11.1.4 Beyond Affine-Yield Models 441 11.2 Fitting Term-Structure Models to the Data 442 11.2.1 Real Bonds, Nominal Bonds, and Inflation 442 11.2.2 Empirical Evidence on Affine-Yield Models 445 11.3 Pricing Fixed-Income Derivative Securities 455 11.3.1 Fitting the Current Term Structure Exactly 456 11.3.2 Forwards and Futures 458 11.3.3 Option Pricing in a Term-Structure Model 461 11.4 Conclusion 464 12Nonlinearities in Financial Data 467 12.1 Nonlinear Structure in Univariate Time Series 468 12.1.1 Some Parametric Models 470 12.1.2 Univariate Tests for Nonlinear Structure 475 12.2 Models of Changing Volatility 479 12.2.1 Univariate Models 481 12.2.2 Multivariate Models 490 12.2.3 Links between First and Second Moments 494 12.3 Nonparametric Estimation 498 12.3.1 Kernel Regression 500 12.3.2 Optimal Bandwidth Selection 502 12.3.3 Average Derivative Estimators 504 12.3.4 Application: Estimating State-Price Densities 507 12.4 Artificial Neural Networks 512 12.4.1 Multilayer Perceptrons 512 12.4.2 Radial Basis Functions 516 12.4.3 Projection Pursuit Regression 518 12.4.4 Limitations of Learning Networks 518 12.4.5 Application: Learning the Black-Scholes Formula 519 12.5 Overfitting and Data-Snooping 523 12.6 Conclusion 524 Appendix 527 A.1 Linear Instrumental Variables 527 A.2 Generalized Method of Moments 532 A.3 Serially Correlated and Heteroskedastic Errors 534 A.4 GMM and Maximum Likelihood 536 References 541 Author Index 587 Subject Index 597
£58.50
Commonwealth Secretariat Small States: Economic Review and Basic
Book SynopsisThis unique annual collection of key economic and statistical data on states with fewer than five million inhabitants is an essential reference for economists, planners and policy-makers. The Commonwealth’s definition of small states is those with a population of one and a half million or less. For comparison purposes this volume presents, where available, data on states with a population of up to five million. The book contains fifty-four tables covering selected economic, social, demographic and Millennium Development Goal indicators culled from international and national sources and presents information unavailable elsewhere. A detailed parallel commentary on trends in Commonwealth small states, looking at growth, employment, inflation, human development, and economic policy, permits a deeper understanding of developments behind the figures. The book also includes three articles focusing on trade in services: ‘Post-crisis Growth in Small States: The Role of Trade in Knowledge-based Services’ by Dirk Willem te Velde, ‘Exporting Health and Wellness: Prospects and Issues for Small States’ by Estella Aryada, and ‘Pro-poor Tourism Interventions through the Creation of Agro-tourism Linkages’ by Chanda Chella. Dirk Willem te Velde currently works at the ODI as the Director of Programmes in the International Economic Development Group. Ms Aryada and Ms Chella are Trade Advisers in the Special Advisory Services Division of the Commonwealth Secretariat.Table of ContentsForeword; Abbreviations and Acronyms; Part I. Recent Economic Trends in Commonwealth Small States; 1. Recent Economic Trends in Commonwealth Small States; Introduction; Economic environment; External performance; Economic development issues; Human development indicators; Key policy issues; Conclusion; 2. Post-crisis Growth in Small States: The Role of Trade in Knowledge-based Services by Dirk Willem te Velde; Introduction; Knowledge-intensive services and small states: definitions and data; The role of knowledge-based services in crisis-resilient growth in small states; Measures to promote services and development; Promoting knowledge-based services in small states; Strategic and practical implications; Sectors; 3. Exporting Health and Wellness: Prospects and Issues for Small States by Estella Aryada; Introduction; Scope; Exports; Major challenges; Health and wellness strategy for St Lucia; Conclusion; 4. Pro-poor Tourism Interventions through the Creation of Agro-tourism Linkages by Chanda Chella; Introduction; Tourism, economic development and poverty reduction; Commonwealth Secretariat initiatives; Lessons; Part II. Social and Economic Data on Small States; Technical notes for tables; Tables; Economic indicators; Social and demographic indicators; Other development indicators; List of Articles and Reviews in Previous Volumes; Readership Survey
£42.75
Taylor & Francis Technical Analysis of Stock Trends
Book SynopsisTechnical Analysis of Stock Trends helps investors make smart, profitable trading decisions by providing proven long- and short-term stock trend analysis. It gets right to the heart of effective technical trading concepts, explaining technical theory such as The Dow Theory, reversal patterns, consolidation formations, trends and channels, technical analysis of commodity charts, and advances in investment technology. It also includes a comprehensive guide to trading tactics from long and short goals, stock selection, charting, low and high risk, trend recognition tools, balancing and diversifying the stock portfolio, application of capital, and risk management. This updated new edition includes patterns and modifiable charts that are tighter and more illustrative. Expanded material is also included on Pragmatic Portfolio Theory as a more elegant alternative to Modern Portfolio Theory; and a newer, simpler, and more powerful alternative to Dow Theory is presented.Table of ContentsPart I: Technical theory 1. The technical approach to trading and investing 2. Charts 3. The Dow Theory 4. The Dow Theory’s defects 5. Replacing Dow Theory with John Magee’s Basing points Procedure 6. Important Reversal Patterns 7. Important Reversal Patterns: continued 8. Important Reversal Patterns: the Triangles 9. More important Reversal Patterns 10. Other Reversal phenomena 11. Consolidation Formations 12. Gaps 13. Support and Resistance 14. Trendlines and Channels 15. Major Trendlines 16. Technical analysis of commodity charts 17. A summary and concluding comments Part II: Trading tactics 18. The tactical problem 19. The all-important details 20. The kind of stocks we want: the speculator’s viewpoint 21. Selection of stocks to chart 22. Selection of stocks to chart: continued 23. Choosing and managing high-risk stocks: tulip stocks, Internet sector, and speculative frenzies 24. The probable moves of your stocks 25. Two touchy questions 26. Round lots or odd lots? 27. Stop orders 28. What is a Bottom and what is a Top? 29. Trendlines in action 30. Use of Support and Resistance 31. Not all in one basket 32. Measuring implications in technical chart patterns 33. Tactical review of chart action 34. A quick summation of tactical methods 35. Effect of technical trading on market action 36. Automated trendline: the Moving Average 37. The same old patterns 38. Balanced and diversified 39. Trial and error 40. How much capital to use in trading 41. Application of capital in practice 42. Portfolio risk management 43. Stick to your guns
£92.14
Making Hard Decisions with DecisionTools
Book SynopsisMAKING HARD DECISIONS WITH DECISIONTOOLS is a new edition of Bob Clemen's best-selling title, MAKING HARD DECISIONS. This straightforward book teaches the fundamental ideas of decision analysis, without an overly technical explanation of the mathematics used in decision analysis. This new version incorporates and implements the powerful DecisionTools software by Palisade Corporation, the world's leading toolkit for risk and decision analysis. At the end of each chapter, topics are illustrated with step-by-step instructions for DecisionTools. This new version makes the text more useful and relevant to students in business and engineering.Trade Review1. Introduction to Decision Analysis. SECTION I: MODELING DECISIONS. 2. Elements of Decision Problems. 3. Structuring Decisions. 4. Making Choices. 5. Sensitivity Analysis. 6. Organizational Decision Making. SECTION II: MODELING UNCERTAINTY. 7. Probability Basics. 8. Subjective Probability. 9. Theoretical Probability Models. 10. Using Data. 11. Monte Carlo Simulation. 12. Value of Information. 13. Real Options. SECTION III. MODELING PREFERENCES. 14. Risk Attitudes. 15. Utility Axioms, Paradoxes, and Implications. 16. Conflicting Objectives I: Fundamental Objectives and the Additive Utility Function. 17. Conflicting Objectives II: Multiattribute Utility Models with Interactions 18. Conclusions and Further Reading.Table of Contents1. Introduction to Decision Analysis. SECTION I: MODELING DECISIONS. 2. Elements of Decision Problems. 3. Structuring Decisions. 4. Making Choices. 5. Sensitivity Analysis. 6. Organizational Decision Making. SECTION II: MODELING UNCERTAINTY. 7. Probability Basics. 8. Subjective Probability. 9. Theoretical Probability Models. 10. Using Data. 11. Monte Carlo Simulation. 12. Value of Information. 13. Real Options. SECTION III. MODELING PREFERENCES. 14. Risk Attitudes. 15. Utility Axioms, Paradoxes, and Implications. 16. Conflicting Objectives I: Fundamental Objectives and the Additive Utility Function. 17. Conflicting Objectives II: Multiattribute Utility Models with Interactions 18. Conclusions and Further Reading.
£312.62
Independently Published Introductory Business Statistics 2e (paperback, b&w)
£28.79
Pearson Education Technical Analysis
Book SynopsisCharles D. Kirkpatrick II, CMT, relative to technical analysis, is or has been: President, Kirkpatrick & Company, Inc., Kittery, Maine--a private firm specializing in technical research; editor and publisher of the Market Strategist newsletter. Author of several other books on aspects of technical analysis in the trading markets. Adjunct professor of finance, Brandeis University International School of Business, Waltham, Massachusetts. Director and vice president, Market Technicians Association Educational Foundation, Cambridge, Massachusetts--a charitable foundation dedicated to encouraging and providing educational courses in technical analysis at the college and university level. Editor, Journal of Technical Analysis, New York, New York--the official journal of technical analysis research. Director, Market Technicians Association, New York, New York--an association of professional technical analysts. Table of ContentsPart I: Introduction Chapter 1: Introduction to Technical Analysis 1 Chapter 2: The Basic Principle of Technical Analysis--The Trend 7 Chapter 3: History of Technical Analysis 21 Chapter 4: The Technical Analysis Controversy 33 Part II: Markets and Market Indicators Chapter 5: An Overview of Markets 57 Chapter 6: Dow Theory 77 Chapter 7: Sentiment 91 Chapter 8: Measuring Market Strength 143 Chapter 9: Temporal Patterns and Cycles 177 Chapter 10: Flow of Funds 195 Part III: Trend Analysis Chapter 11: History and Construction of Charts 219 Chapter 12: Trends--The Basics 249 Chapter 13: Breakouts, Stops, and Retracements 281 Chapter 14: Moving Averages 305 Part IV: Chart Pattern Analysis Chapter 15: Bar Chart Patterns 333 Chapter 16: Point and Figure Chart Patterns 367 Chapter 17: Short-Term Patterns 393 Part V: Trend Confirmation Chapter 18: Confirmation 439 Part VI: Other Technical Methods and Rules Chapter 19: Cycles 481 Chapter 20: Elliott, Fibonacci, and Gann 509 Part VII: Selection Chapter 21: Selection of Markets and Issues: Trading and Investing 533 Part VIII: System Testing and Management Chapter 22: System Design and Testing 559 Chapter 23: Money and Portfolio Risk Management 589 Part IX: Appendices Appendix A: Basic Statistics 611 Appendix B: Types of Orders and Other Trader Terminology 639 Bibliography 643 Index 675
£56.52
Random House USA Inc Weapons of Math Destruction
Book SynopsisLonglisted for the National Book AwardNew York Times BestsellerA former...
£11.70
Penguin Putnam Inc The Data Detective
Book Synopsis
£16.65
Taylor & Francis Introductory Econometrics
a huge range and FREE tracked UK delivery on ALL orders.
£82.64
Princeton University Press Nonparametric Econometrics
Book SynopsisTailored to the needs of applied econometricians and social scientists, this work emphasizes nonparametric techniques suited to the rich array of data types - continuous, nominal, and ordinal - within one coherent framework. It also covers the various material necessary to understand and apply nonparametric methods for real-world problems.Trade Review"Overall, the text is a must for graduate students undertaking research in this area; the large number of exercises at the end of each chapter makes it very suitable for a graduate class on nonparametric and semiparametric techniques. In addition, because the coverage of the book is very comprehensive and up-to-date, it constitutes an excellent reference for researchers applying these techniques. Therefore, it can satisfy the needs of both audiences with a solid background in theoretical econometrics and more applied audiences."--Margarita Genius, European Review of Agricultural Economics "This book is ideal for a specialised graduate course. Li and Racine have done a fantastic job of bringing together all the latest developments in non-parametric estimation and treating them in a unified, accessible way. In particular, recent developments on using mixed continuous and discrete data, research to which Li and Raci have contributed immensely, are well covered."--Economic RecordTable of ContentsPreface xvii PART I: Nonparametric Kernel Methods 1 Chapter 1: Density Estimation 3 1.1 Univariate Density Estimation 4 1.2 Univariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods 14 1.3 Univariate Bandwidth Selection: Cross-Validation ZMethods 15 1.3.1 Least Squares Cross-Validation 15 1.3.2 Likelihood Cross-Validation 18 1.3.3 An Illustration of Data-Driven Bandwidth Selection 19 1.4 Univariate CDF Estimation 19 1.5 Univariate CDF Bandwidth Selection: Cross- Validation Methods 23 1.6 Multivariate Density Estimation 24 1.7 Multivariate Bandwidth Selection: Rule-of-Thumb and Plug-In Methods 26 1.8 Multivariate Bandwidth Selection: Cross-Validation Methods 27 1.8.1 Least Squares Cross-Validation 27 1.8.2 Likelihood Cross-Validation 28 1.9 Asymptotic Normality of Density Estimators 28 1.10 Uniform Rates of Convergence 30 1.11 Higher Order Kernel Functions 33 1.12 Proof of Theorem 1.4 (Uniform Almost Sure Convergence) 35 1.13 Applications 40 1.13.1 Female Wage Inequality 41 1.13.2 Unemployment Rates and City Size 43 1.13.3 Adolescent Growth 44 1.13.4 Old Faithful Geyser Data 44 1.13.5 Evolution of Real Income Distribution in Italy, 1951-1998 45 1.14 Exercises 47 Chapter 2: Regression 57 2.1 Local Constant Kernel Estimation 60 2.1.1 Intuition Underlying the Local Constant Kernel Estimator 64 2.2 Local Constant Bandwidth Selection 66 2.2.1 Rule-of-Thumb and Plug-In Methods 66 2.2.2 Least Squares Cross-Validation 69 2.2.3 AICc 72 2.2.4 The Presence of Irrelevant Regressors 73 2.2.5 Some Further Results on Cross-Validation 78 2.3 Uniform Rates of Convergence 78 2.4 Local Linear Kernel Estimation 79 2.4.1 Local Linear Bandwidth Selection: Least Squares Cross-Validation 83 2.5 Local Polynomial Regression (General pth Order) 85 2.5.1 The Univariate Case 85 2.5.2 The Multivariate Case 88 2.5.3 Asymptotic Normality of Local Polynomial Estimators 89 2.6 Applications 92 2.6.1 Prestige Data 92 2.6.2 Adolescent Growth 92 2.6.3 Inflation Forecasting and Money Growth 93 2.7 Proofs 97 2.7.1 Derivation of (2.24) 98 2.7.2 Proof of Theorem 2.7 100 2.7.3 Definitions of Al,p+1 and Vl Used in Theorem 2.10 106 2.8 Exercises 108 Chapter 3: Frequency Estimation with Mixed Data 115 3.1 Probability Function Estimation with Discrete Data 116 3.2 Regression with Discrete Regressors 118 3.3 Estimation with Mixed Data: The Frequency Approach 118 3.3.1 Density Estimation with Mixed Data 118 3.3.2 Regression with Mixed Data 119 3.4 Some Cautionary Remarks on Frequency Methods 120 3.5 Proofs 122 3.5.1 Proof of Theorem 3.1 122 3.6 Exercises 123 Chapter 4: Kernel Estimation with Mixed Data 125 4.1 Smooth Estimation of Joint Distributions with Discrete Data 126 4.2 Smooth Regression with Discrete Data 131 4.3 Kernel Regression with Discrete Regressors: The Irrelevant Regressor Case 134 4.4 Regression with Mixed Data: Relevant Regressors 136 4.4.1 Smooth Estimation with Mixed Data 136 4.4.2 The Cross-Validation Method 138 4.5 Regression with Mixed Data: Irrelevant Regressors 140 4.5.1 Ordered Discrete Variables 144 4.6 Applications 145 4.6.1 Food-Away-from-Home Expenditure 145 4.6.2 Modeling Strike Volume 147 4.7 Exercises 150 Chapter 5: Conditional Density Estimation 155 5.1 Conditional Density Estimation: Relevant Variables 155 5.2 Conditional Density Bandwidth Selection 157 5.2.1 Least Squares Cross-Validation: Relevant Variables 157 5.2.2 Maximum Likelihood Cross-Validation: Relevant Variables 160 5.3 Conditional Density Estimation: Irrelevant Variables 162 5.4 The Multivariate Dependent Variables Case 164 5.4.1 The General Categorical Data Case 167 5.4.2 Proof of Theorem 5.5 168 5.5 Applications 171 5.5.1 A Nonparametric Analysis of Corruption 171 5.5.2 Extramarital Affairs Data 172 5.5.3 Married Female Labor Force Participation 175 5.5.4 Labor Productivity 177 5.5.5 Multivariate Y Conditional Density Example: GDP Growth and Population Growth Conditional on OECD Status 178 5.6 Exercises 180 Chapter 6: Conditional CDF and Quantile Estimation 181 6.1 Estimating a Conditional CDF with Continuous Covariates without Smoothing the Dependent Variable 182 6.2 Estimating a Conditional CDF with Continuous Covariates Smoothing the Dependent Variable 184 6.3 Nonparametric Estimation of Conditional Quantile Functions 189 6.4 The Check Function Approach 191 6.5 Conditional CDF and Quantile Estimation with Mixed Discrete and Continuous Covariates 193 6.6 A Small Monte Carlo Simulation Study 196 6.7 Nonparametric Estimation of Hazard Functions 198 6.8 Applications 200 6.8.1 Boston Housing Data 200 6.8.2 Adolescent Growth Charts 202 6.8.3 Conditional Value at Risk 202 6.8.4 Real Income in Italy, 1951-1998 206 6.8.5 Multivariate Y Conditional CDF Example: GDP Growth and Population Growth Conditional on OECD Status 206 6.9 Proofs 209 6.9.1 Proofs of Theorems 6.1, 6.2, and 6.4 209 6.9.2 Proofs of Theorems 6.5 and 6.6 (Mixed Covariates Case) 214 6.10 Exercises 215 PART II: Semiparametric Methods 219 Chapter 7: Semiparametric Partially Linear Models 221 7.1 Partially Linear Models 222 7.1.1 Identification of 222 7.2 Robinson's Estimator 222 7.2.1 Estimation of the Nonparametric Component 228 7.3 Andrews's MINPIN Method 230 7.4 Semiparametric Efficiency Bounds 233 7.4.1 The Conditionally Homoskedastic Error Case 233 7.4.2 The Conditionally Heteroskedastic Error Case 235 7.5 Proofs 238 7.5.1 Proof of Theorem 7.2 238 7.5.2 Verifying Theorem 7.3 for a Partially Linear Model 244 7.6 Exercises 246 Chapter 8: Semiparametric Single Index Models 249 8.1 Identification Conditions 251 8.2 Estimation 253 8.2.1 Ichimura's Method 253 8.3 Direct Semiparametric Estimators for 258 8.3.1 Average Derivative Estimators 258 8.3.2 Estimation of g() 262 8.4 Bandwidth Selection 263 8.4.1 Bandwidth Selection for Ichimura's Method 263 8.4.2 Bandwidth Selection with Direct Estimation Methods 265 8.5 Klein and Spady's Estimator 266 8.6 Lewbel's Estimator 267 8.7 Manski's Maximum Score Estimator 269 8.8 Horowitz's Smoothed Maximum Score Estimator 270 8.9 Han's Maximum Rank Estimator 270 8.10 Multinomial Discrete Choice Models 271 8.11 Ai's Semiparametric Maximum Likelihood Approach 272 8.12 A Sketch of the Proof of Theorem 8.1 275 8.13 Applications 277 8.13.1 Modeling Response to Direct Marketing Catalog Mailings 277 8.14 Exercises 281 Chapter 9: Additive and Smooth (Varying) Coefficient Semiparametric Models 283 9.1 An Additive Model 283 9.1.1 The Marginal Integration Method 284 9.1.2 A Computationally Efficient Oracle Estimator 286 9.1.3 The Ordinary Backfitting Method 289 9.1.4 The Smoothed Backfitting Method 290 9.1.5 Additive Models with Link Functions 295 9.2 An Additive Partially Linear Model 297 9.2.1 A Simple Two-Step Method 299 9.3 A Semiparametric Varying (Smooth) Coefficient Model 301 9.3.1 A Local Constant Estimator of the Smooth Coefficient Function 302 9.3.2 A Local Linear Estimator of the Smooth Coefficient Function 303 9.3.3 Testing for a Parametric Smooth Coefficient Model 306 9.3.4 Partially Linear Smooth Coefficient Models 308 9.3.5 Proof of Theorem 9.3 310 9.4 Exercises 312 Chapter 10: Selectivity Models 315 10.1 Semiparametric Type-2 Tobit Models 316 10.2 Estimation of a Semiparametric Type-2 Tobit Model 317 10.2.1 Gallant and Nychka's Estimator 318 10.2.2 Estimation of the Intercept in Selection Models 319 10.3 Semiparametric Type-3 Tobit Models 320 10.3.1 Econometric Preliminaries 320 10.3.2 Alternative Estimation Methods 323 10.4 Das, Newey and Vella's Nonparametric Selection Model 328 10.5 Exercises 330 Chapter 11: Censored Models 331 11.1 Parametric Censored Models 332 11.2 Semiparametric Censored Regression Models 334 11.3 Semiparametric Censored Regression Models with Nonparametric Heteroskedasticity 336 11.4 The Univariate Kaplan-Meier CDF Estimator 338 11.5 The Multivariate Kaplan-Meier CDF Estimator 341 11.5.1 Nonparametric Regression Models with Random Censoring 343 11.6 Nonparametric Censored Regression 345 11.6.1 Lewbel and Linton's Approach 345 11.6.2 Chen, Dahl and Khan's Approach 346 11.7 Exercises 348 III Consistent Model Specification Tests 349 Chapter 12: Model Specification Tests 351 12.1 A Simple Consistent Test for Parametric Regression Functional Form 354 12.1.1 A Consistent Test for Correct Parametric Functional Form 355 12.1.2 Mixed Data 360 12.2 Testing for Equality of PDFs 362 12.3 More Tests Related to Regression Functions 365 12.3.1 Hardle and Mammen's Test for a Parametric Regression Model 365 12.3.2 An Adaptive and Rate Optimal Test 367 12.3.3 A Test for a Parametric Single Index Model 369 12.3.4 A Nonparametric Omitted Variables Test 370 12.3.5 Testing the Significance of Categorical Variables 375 12.4 Tests Related to PDFs 378 12.4.1 Testing Independence between Two Random Variables 378 12.4.2 A Test for a Parametric PDF 380 12.4.3 A Kernel Test for Conditional Parametric Distributions 382 12.5 Applications 385 12.5.1 Growth Convergence Clubs 385 12.6 Proofs 388 12.6.1 Proof of Theorem 12.1 388 12.6.2 Proof of Theorem 12.2 389 12.6.3 Proof of Theorem 12.5 389 12.6.4 Proof of Theorem 12.9 391 12.7 Exercises 394 Chapter 13: Nonsmoothing Tests 397 13.1 Testing for Parametric Regression Functional Form 398 13.2 Testing for Equality of PDFs 401 13.3 A Nonparametric Significance Test 401 13.4 Andrews's Test for Conditional CDFs 402 13.5 Hong's Tests for Serial Dependence 404 13.6 More on Nonsmoothing Tests 408 13.7 Proofs 409 13.7.1 Proof of Theorem 13.1 409 13.8 Exercises 410 PART IV: Nonparametric Nearest Neighbor and Series Methods 413 Chapter 14: K-Nearest Neighbor Methods 415 14.1 Density Estimation: The Univariate Case 415 14.2 Regression Function Estimation 419 14.3 A Local Linear k-nn Estimator 421 14.4 Cross-Validation with Local Constant k-nn Estimation 422 14.5 Cross-Validation with Local Linear k-nn Estimation 425 14.6 Estimation of Semiparametric Models with k-nn Methods 427 14.7 Model Specification Tests with k-nn Methods 428 14.7.1 A Bootstrap Test 431 14.8 Using Different k for Different Components of x 432 14.9 Proofs 432 14.9.1 Proof of Theorem 14.1 435 14.9.2 Proof of Theorem 14.5 435 14.9.3 Proof of Theorem 14.10 440 14.10 Exercises 444 Chapter 15: Nonparametric Series Methods 445 15.1 Estimating Regression Functions 446 15.1.1 Convergence Rates 449 15.2 Selection of the Series Term K 451 15.2.1 Asymptotic Normality 453 15.3 A Partially Linear Model 454 15.3.1 An Additive Partially Linear Model 455 15.3.2 Selection of Nonlinear Additive Components 461 15.3.3 Estimating an Additive Model with a Known Link Function 463 15.4 Estimation of Partially Linear Varying Coefficient Models 466 15.4.1 Testing for Correct Parametric Regression Functional Form 471 15.4.2 A Consistent Test for an Additive Partially Linear Model 474 15.5 Other Series-Based Tests 479 15.6 Proofs 480 15.6.1 Proof of Theorem 15.1 480 15.6.2 Proof of Theorem 15.3 484 15.6.3 Proof of Theorem 15.6 488 15.6.4 Proof of Theorem 15.9 492 15.6.5 Proof of Theorem 15.10 497 15.7 Exercises 502 PART V: Time Series, Simultaneous Equation, and Panel Data Models 503 Chapter 16: Instrumental Variables and Efficient Estimation of Semiparametric Models 505 16.1 A Partially Linear Model with Endogenous Regressors in the Parametric Part 505 16.2 A Varying Coefficient Model with Endogenous Regressors in the Parametric Part 509 16.3 Ai and Chen's Efficient Estimator with Conditional Moment Restrictions 511 16.3.1 Estimation Procedures 511 16.3.2 Asymptotic Normality for 513 16.3.3 A Partially Linear Model with the Endogenous Regressors in the Nonparametric Part 515 16.4 Proof of Equation (16.16) 517 16.5 Exercises 520 Chapter 17: Endogeneity in Nonparametric Regression Models 521 17.1 A Nonparametric Model 521 17.2 A Triangular Simultaneous Equation Model 522 17.3 Newey-Powell Series-Based Estimator 527 17.4 Hall and Horowitz's Kernel-Based Estimator 529 17.5 Darolles, Florens and Renault's Estimator 532 17.6 Exercises 533 Chapter 18: Weakly Dependent Data 535 18.1 Density Estimation with Dependent Data 537 18.1.1 Uniform Almost Sure Rate of Convergence 541 18.2 Regression Models with Dependent Data 541 18.2.1 The Martingale Difference Error Case 541 18.2.2 The Autocorrelated Error Case 544 18.2.3 One-Step-Ahead Forecasting 546 18.2.4 d-Step-Ahead Forecasting 547 18.2.5 Estimation of Nonparametric Impulse Response Functions 548 18.3 Semiparametric Models with Dependent Data 551 18.3.1 A Partially Linear Model with Dependent Data 551 18.3.2 Additive Regression Models 552 18.3.3 Varying Coefficient Models with Dependent Data 553 18.4 Testing for Serial Correlation in Semiparametric Models 554 18.4.1 The Test Statistic and Its Asymptotic Distribution 554 18.4.2 Testing Zero First Order Serial Correlation 555 18.5 Model Specification Tests with Dependent Data 556 18.5.1 A Kernel Test for Correct Parametric Regression Functional Form 556 18.5.2 Nonparametric Significance Tests 557 18.6 Nonsmoothing Tests for Regression Functional Form 558 18.7 Testing Parametric Predictive Models 559 18.7.1 In-Sample Testing of Conditional CDFs 559 18.7.2 Out-of-Sample Testing of Conditional CDFs 562 18.8 Applications 564 18.8.1 Forecasting Short-Term Interest Rates 564 18.9 Nonparametric Estimation with Nonstationary Data 566 18.10 Proofs 567 18.10.1 Proof of Equation (18.9) 567 18.10.2 Proof of Theorem 18.2 569 18.11 Exercises 572 Chapter 19: Panel Data Models 575 19.1 Nonparametric Estimation of Panel Data Models: Ignoring the Variance Structure 576 19.2 Wang's Efficient Nonparametric Panel Data Estimator 578 19.3 A Partially Linear Model with Random Effects 584 19.4 Nonparametric Panel Data Models with Fixed Effects 586 19.4.1 Error Variance Structure Is Known 587 19.4.2 The Error Variance Structure Is Unknown 590 19.5 A Partially Linear Model with Fixed Effects 592 19.6 Semiparametric Instrumental Variable Estimators 594 19.6.1 An Infeasible Estimator 594 19.6.2 The Choice of Instruments 595 19.6.3 A Feasible Estimator 597 19.7 Testing for Serial Correlation and for Individual Effects in Semiparametric Models 599 19.8 Series Estimation of Panel Data Models 602 19.8.1 Additive Effects 602 19.8.2 Alternative Formulation of Fixed Effects 604 19.9 Nonlinear Panel Data Models 606 19.9.1 Censored Panel Data Models 607 19.9.2 Discrete Choice Panel Data Models 614 19.10 Proofs 618 19.10.1 Proof of Theorem 19.1 618 19.10.2 Leading MSE Calculation of Wang's Estimator 621 19.11 Exercises 624 Chapter 20: Topics in Applied Nonparametric Estimation 627 20.1 Nonparametric Methods in Continuous-Time Models 627 20.1.1 Nonparametric Estimation of Continuous-Time Models 627 20.1.2 Nonparametric Tests for Continuous-Time Models 632 20.1.3 Ait-Sahalia's Test 632 20.1.4 Hong and Li's Test 633 20.1.5 Proofs 636 20.2 Nonparametric Estimation of Average Treatment Effects 639 20.2.1 The Model 640 20.2.2 An Application: Assessing the Efficacy of Right Heart Catheterization 642 20.3 Nonparametric Estimation of Auction Models 645 20.3.1 Estimation of First Price Auction Models 645 20.3.2 Conditionally Independent Private Information Auctions 648 20.4 Copula-Based Semiparametric Estimation of Multivariate Distributions 651 20.4.1 Some Background on Copula Functions 651 20.4.2 Semiparametric Copula-Based Multivariate Distributions 652 20.4.3 A Two-Step Estimation Procedure 653 20.4.4 A One-Step Efficient Estimation Procedure 655 20.4.5 Testing Parametric Functional Forms of a Copula 657 20.5 A Semiparametric Transformation Model 659 20.6 Exercises 662 A Background Statistical Concepts 663 1.1 Probability, Measure, and Measurable Space 663 1.2 Metric, Norm, and Functional Spaces 672 1.3 Limits and Modes of Convergence 680 1.3.1 Limit Supremum and Limit Infimum 680 1.3.2 Modes of Convergence 681 1.4 Inequalities, Laws of Large Numbers, and Central Limit Theorems 688 1.5 Exercises 694 Bibliography 697 Author Index 737 Subject Index 744
£999.99
Cambridge University Press Analysis of Panel Data
Book SynopsisNow in its fourth edition, this comprehensive introduction of fundamental panel data methodologies provides insights on what is most essential in panel literature. A capstone to the forty-year career of a pioneer of panel data analysis, this new edition''s primary contribution will be the coverage of advancements in panel data analysis, a statistical method widely used to analyze two or higher-dimensional panel data. The topics discussed in early editions have been reorganized and streamlined to comprehensively introduce panel econometric methodologies useful for identifying causal relationships among variables, supported by interdisciplinary examples and case studies. This book, to be featured in Cambridge''s Econometric Society Monographs series, has been the leader in the field since the first edition. It is essential reading for researchers, practitioners and graduate students interested in the analysis of microeconomic behavior.Trade ReviewA masterful new edition of Hsiao's classic text on panel data. This is a superbly comprehensive and accessible source for panel data with modern approaches to inference and identification, helpful to econometricians and other quantitative social scientists. Esfandiar Maasoumi, Emery UniversityThe latest edition of Cheng Hsiao's panel data monograph is most welcome to the econometrics profession. Benefitting from Professor Hsiao's deep understanding and insight, it has, since its first edition, not only become required reading for students, researchers and practitioners, but surely deserves no small credit for the huge growth of interest and activity in panel data. In this 4th edition, Professor Hsiao has very successfully built on the foundations of the earlier ones. Peter M. Robinson, London School of EconomicsProfessor Hsiao has done it again. This edition provides a lucid and comprehensive account of often complex problems, ranging from the analysis of panel data models with interactive effects, heterogeneity, spatial dependence, simultaneous dynamic models, to program evaluation – many areas to which he himself has made significant and lasting contributions. I have learned a great deal from the past three editions, and I very much look forward to the fourth edition and strongly recommend it to both students and research scholars of panel data alike. Hashem Pesaran, John Elliot University of Southern CaliforniaCheng Hsiao's Analysis of Panel Data has undoubtedly become the classic text book reference on panel data econometric methods. It is to be recommended for the clarity and deepness of its exposition, its wide coverage of the abundant and rapidly developing specialized literature, and its remarkable capacity to focus on what is most essential in this literature. Jacques Mairesse, Collège de FranceTable of ContentsPreface; 1. Introduction; 2. Static models with additive effects; 3. Dynamic models with additive effects; 4. Static simultaneous models with additive effects; 5. Dynamic system; 6. Qualitative choice models; 7. Limited dependent and sample section models; 8. Some nonlinear models; 9. Miscellaneous topics; 10. Interactive effects models; 11. Spatial models and cross-sectional dependent data; 12. Program evaluation; 13. Varying coefficients models; 14. Big data analysis.
£37.04
Cambridge University Press Quantitative Enterprise Risk Management
Book SynopsisThis well-balanced introduction to enterprise risk management integrates quantitative and qualitative approaches and motivates key mathematical and statistical methods with abundant real-world cases - both successes and failures. Worked examples and end-of-chapter exercises support readers in consolidating what they learn. The mathematical level, which is suitable for graduate and senior undergraduate students in quantitative programs, is pitched to give readers a solid understanding of the concepts and principles involved, without diving too deeply into more complex theory. To reveal the connections between different topics, and their relevance to the real world, the presentation has a coherent narrative flow, from risk governance, through risk identification, risk modelling, and risk mitigation, capped off with holistic topics - regulation, behavioural biases, and crisis management - that influence the whole structure of ERM. The result is a text and reference that is ideal for graduate and senior undergraduate students, risk managers in industry, and anyone preparing for ERM actuarial exams.Trade Review'Quantitative Enterprise Risk Management can be strongly recommended to anyone seeking to develop their skills in risk management. The book will be particularly useful for those seeking to master the more challenging technical aspects of risk management missing in other textbooks.' Andrew Cairns, Heriot-Watt University'This hits the sweet spot between overly abstract mathematical and overly 'math lean' presentations of enterprise risk management.' Gary Hatfield, University of Minnesota'Hardy and Saunders have written a masterpiece that not only explains [ERM] from a quantitative perspective, but also manages to bridge the gap between it and more qualitative approaches. It impressively covers the whole spectrum from risk taxonomy, risk modelling and measurement, risk mitigation, risk transfer up to (behavioural) risk, and crisis management. I highly recommend it to all those who want to get a deeper understanding of ERM.' Rudi Zagst, Technical University of MunichTable of ContentsPreface; 1. Introduction to enterprise risk management; 2. Risk taxonomy; 3. Risk measures; 4. Frequency-Severity analysis; 5. Extreme value theory; 6. Copulas; 7. Stress testing; 8. Market risk models; 9. Short term portfolio risk; 10. Economic scenario generators; 11. Interest rate risk; 12. Credit risk; 13. Liquidity risk; 14. Model risk and governance; 15. Risk mitigation using options and derivatives; 16. Risk transfer; 17. Regulation of financial institutions; 18. Risk adjusted measures of profit and capital allocation; 19. Behavioural risk management; 20. Crisis management; A. Probability and statistics review; References; Index.
£56.99
Cambridge University Press A First Course in Quantitative Finance
Book SynopsisThis new and exciting book offers a fresh approach to quantitative finance and utilises novel features, including stereoscopic images which permit 3D visualisation of complex subjects without the need for additional tools. Offering an integrated approach to the subject, A First Course in Quantitative Finance introduces students to the architecture of complete financial markets before exploring the concepts and models of modern portfolio theory, derivative pricing and fixed income products in both complete and incomplete market settings. Subjects are organised throughout in a way that encourages a gradual and parallel learning process of both the economic concepts and their mathematical descriptions, framed by additional perspectives from classical utility theory, financial economics and behavioural finance. Suitable for postgraduate students studying courses in quantitative finance, financial engineering and financial econometrics as part of an economics, finance, econometric or mathemTrade 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.
£48.44
Cambridge University Press Quantum Concepts in the Social Ecological and Biological Sciences
Book SynopsisQuantum mechanics is traditionally associated with microscopic systems; however, quantum concepts have also been successfully applied to a diverse range of macroscopic systems both within and outside of physics. This book describes how complex systems from a variety of fields can be modelled using quantum mechanical principles; from biology and ecology, to sociology and decision-making. The mathematical basis of these models is covered in detail, furnishing a self-contained and consistent approach. This book provides unique insight into the dynamics of these macroscopic systems and opens new interdisciplinary research frontiers. It will be an essential resource for students and researchers in applied mathematics or theoretical physics who are interested in applying quantum mechanics to dynamical systems in the social, biological or ecological sciences.Trade Review'The target audience for the book consists of scientists of the fields mentioned in the title (biologists,ecologists,sociologists); they could find - beyond the specific applications of the book - new directions and methods, through quantum mechanics, in approaching problems in their areas of expertise; even more so, they could open new application horizons. Prospective readers are also quantum experts, while finally, the volume could be of interest to those involved in physics, mathematics, and computer science.' Nikolaos E. Myridis, Contemporary PhysicsTable of ContentsPreface; 1. Introduction; Part I. The General Framework: 2. Some preliminaries; Part II. Applications: 3. Politics; 4. Desertification; 5. Escape strategies; 6. Closed ecosystems; 7. More on biological systems; 8. Quantum game of life and its (H; ρ)-induced dynamics; 9. Prehistoric data miming; 10. A simple model of information in stock markets; 11. Decision Making driven by the environment; 12. Compatible and incompatible questions; 13. This is not the end; References; Index.
£66.49
Oxford University Press Maths for Economics
Book SynopsisMaths for Economics provides a comprehensive and solid foundation in core mathematical principles and methods used in economics, beginning with revisiting basic skills in arithmetic, algebra, equation solving, and slowly building to more advanced topics. Suitable for those with a range of prior school-level expereince or more generally for those who feel they need to go back to the very basics, students can learn with confidence. Drawing on his extensive experience of teaching in the area, the author appreciates that maths can be a daunting topic for many. As such the text is fully supports the reader by using a combination of engaging learning features including summary sections, examples to show how theory is used in practice and progress exercises, which encourage independent study. Each chapter ends with a conclusion check list to allow students to reflect on topics as they master them.Digital formats and resourcesThe fifth edition is available for students and institutions to purchase in a variety of formats, and is supported by online resources. The e-book offers a mobile experience and convenient access along with functionality tools, navigation features, and links that offer extra learning support: www.oxfordtextbooks.co.uk/ebooks Online resources supporting the book include,For Students: - Ask the author forum - Excel tutorial - Maple tutorial - Further exercises - Answers to further questions - Expanded solutions to progress exercises For Lecturers: - Test exercises - Graphs from the book - Answers to test exercisesTable of ContentsPart One: Foundations 1: Arithmetic 2: Algebra 3: Linear equations 4: Quadratic equations 5: Some further equations and techniques Part Two: Optimization With One Independent Variable 6: Derivatives and differentiation 7: Derivatives in action 8: Economic applications of functions and derivatives 9: Elasticity Part Three: Mathematics Of Finance And Growth 10: Compound growth and present discounted value 11: The exponential function and logarithms 12: Continuous growth and the natural exponential function 13: Derivatives of exponential and logarithmic functions and their applications Part Four: Optimization With Two Or More Independent Variables 14: Functions of two or more independent variables 15: Maximum and minimum values, the total differential, and applications 16: Constrained maximum and minimum values 17: Returns to scale and homogenous functions; partial elasticities; growth accounting; logarithmic scales Part Five: Some Further Topics 18: Integration 19: Matrix algebra 20: Difference and differential equations 21: W21:Extensions and future directions
£55.09
Taylor & Francis Technical Analysis of Stock Trends
Book SynopsisTechnical Analysis of Stock Trends helps investors make smart, profitable trading decisions by providing proven long- and short-term stock trend analysis. It gets right to the heart of effective technical trading concepts, explaining technical theory such as The Dow Theory, reversal patterns, consolidation formations, trends and channels, technical analysis of commodity charts, and advances in investment technology. It also includes a comprehensive guide to trading tactics from long and short goals, stock selection, charting, low and high risk, trend recognition tools, balancing and diversifying the stock portfolio, application of capital, and risk management. This updated new edition includes patterns and modifiable charts that are tighter and more illustrative. Expanded material is also included on Pragmatic Portfolio Theory as a more elegant alternative to Modern Portfolio Theory; and a newer, simpler, and more powerful alternative to Dow Theory is presented.Table of ContentsPart I: Technical theory 1. The technical approach to trading and investing 2. Charts 3. The Dow Theory 4. The Dow Theory’s defects 5. Replacing Dow Theory with John Magee’s Basing points Procedure 6. Important Reversal Patterns 7. Important Reversal Patterns: continued 8. Important Reversal Patterns: the Triangles 9. More important Reversal Patterns 10. Other Reversal phenomena 11. Consolidation Formations 12. Gaps 13. Support and Resistance 14. Trendlines and Channels 15. Major Trendlines 16. Technical analysis of commodity charts 17. A summary and concluding comments Part II: Trading tactics 18. The tactical problem 19. The all-important details 20. The kind of stocks we want: the speculator’s viewpoint 21. Selection of stocks to chart 22. Selection of stocks to chart: continued 23. Choosing and managing high-risk stocks: tulip stocks, Internet sector, and speculative frenzies 24. The probable moves of your stocks 25. Two touchy questions 26. Round lots or odd lots? 27. Stop orders 28. What is a Bottom and what is a Top? 29. Trendlines in action 30. Use of Support and Resistance 31. Not all in one basket 32. Measuring implications in technical chart patterns 33. Tactical review of chart action 34. A quick summation of tactical methods 35. Effect of technical trading on market action 36. Automated trendline: the Moving Average 37. The same old patterns 38. Balanced and diversified 39. Trial and error 40. How much capital to use in trading 41. Application of capital in practice 42. Portfolio risk management 43. Stick to your guns
£34.19
Cambridge University Press InputOutput Analysis
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.
£54.14
Cambridge University Press Advances in Economics and Econometrics 2 Volumes Hardback Set
a huge range and FREE tracked UK delivery on ALL orders.
£218.50
Cambridge University Press The SkewNormal and Related Families 3 Institute of Mathematical Statistics Monographs Series Number 3
Book SynopsisInterest in the skew-normal and related families of distributions has grown enormously over recent years, as theory has advanced, challenges of data have grown, and computational tools have made substantial progress. This comprehensive treatment, blending theory and practice, will be the standard resource for statisticians and applied researchers. Assuming only basic knowledge of (non-measure-theoretic) probability and statistical inference, the book is accessible to the wide range of researchers who use statistical modelling techniques. Guiding readers through the main concepts and results, it covers both the probability and the statistics sides of the subject, in the univariate and multivariate settings. The theoretical development is complemented by numerous illustrations and applications to a range of fields including quantitative finance, medical statistics, environmental risk studies, and industrial and business efficiency. The author's freely available R package sn, available frTable of ContentsPreface; 1. Modulation of symmetric densities; 2. The skew-normal distribution: probability; 3. The skew-normal distribution: statistics; 4. Heavy and adaptive tails; 5. The multivariate skew-normal distribution; 6. Skew-elliptical distributions; 7. Further extensions and other directions; 8. Application-oriented work; Appendices; References.
£55.09
Cambridge University Press The Cambridge Dictionary of Probability and Its Applications
Book SynopsisProbability comes of age with this, the first dictionary of probability and its applications in English, which supplies a guide to the concepts and vocabulary of this rapidly expanding field. Besides the basic theory of probability and random processes, applications covered here include financial and insurance mathematics, operations research (including queueing, reliability, and inventories), decision and game theory, optimization, time series, networks, and communication theory, as well as classic problems and paradoxes. The dictionary is reliable, stable, concise, and cohesive. Each entry provides a rigorous definition, a sketch of the context, and a reference pointing the reader to the wider literature. Judicious use of figures makes complex concepts easier to follow without oversimplifying. As the only dictionary on the market, this will be a guiding reference for all those working in, or learning, probability together with its applications.Trade Review'To construct a dictionary about such an enormous field is a daunting task, and David Stirzaker deserves high praise, first for even attempting to do so, and second for the success he has achieved. A dictionary's usefulness depends on its organisation as well as on the quality of the individual entries, and this book's structure is simple and logical: two initial pages list the abbreviations and symbols, then the main body of 3000-odd entries with easy-to-use cross-referencing, ending with an appendix of probability distributions … I shall be delighted to possess this authoritative tome. It will sit alongside Abramowitz and Steguns' Handbook of Mathematical Functions as a reliable source of enlightenment.' John Haigh, University of SussexTable of ContentsPreface; Table of distributions; The dictionary.
£144.40
Cambridge University Press Loss Coverage Why Insurance Works Better with Some Adverse Selection
Book SynopsisA novel book that argues that, contrary to received wisdom, some adverse selection in insurance markets is beneficial to society as a whole. It is for all those interested in public policy arguments about insurance and discrimination: policymakers, academics, actuaries, underwriters, disability activists, geneticists and other medical professionals.Trade Review'Guy Thomas challenges the orthodox views held by the insurance industry, actuaries, and economists concerning the problem of adverse selection. He makes his case that a little adverse selection is actually a good thing in a sensible, pragmatic, and compelling manner. His critical insights about the debates on restricting risk classification in insurance should be essential reading for policy makers.' Michael Hoy, University of Guelph, Canada'Despite dramatic warnings, insurance companies continue to prosper despite bans on gender rating, genetic testing, racial and other discriminations in setting policy terms. This thought-provoking book explains why. The author makes a convincing case for even tighter regulation of allowable risk classifications to enhance the welfare of society of a whole - especially timely now as 1-in-5 proposers for life insurance are not accepted at standard rates.' Shane Whelan, FFA, FSAI, Former Managing Editor of the British Actuarial Journal'Actuaries traditionally see nothing but danger in adverse selection. Guy Thomas, an actuary himself, sees opportunity. Using the concept of loss coverage, Thomas challenges the conventional wisdom of how economists model insurance markets, much of which, he sets out to show, is more myth than reality. Lucidly written and sure to get the reader thinking afresh.' Angus Macdonald, Heriot Watt University, Scotland'This is a serious book which challenges some of the conventional thinking of actuaries and economists about adverse selection in insurance, and does so with justification; they would do well to take the author's views into account. It can also be read with profit by others including insurance managers, academics, and those responsible for public policy.' David Wilkie, InQA Limited and Heriot Watt University, Scotland'This is a book that is full of common sense. Thomas provides important and, what will be to many, controversial recommendations to curtail [insurers'] use of certain characteristics of individuals for purposes of differential pricing. … It is important to take seriously the criticisms of both insiders and outsiders to strengthen both the application and development of economics or any other social science. His criticisms are very well thought out.' Michael Hoy, Annals of Actuarial Science'In summary, Loss Coverage offers policymakers, academics, professionals, students, and other interested parties useful insight into the 'problem' of adverse selection. Thomas employs simple and timely real-world examples to make the concepts of adverse selection, loss coverage, and risk classification more understandable and relevant for policy decisions, offering a path toward mitigating concerns over unfair discrimination while increasing insurance market efficiency.' William L. Ferguson, Journal of Risk and InsuranceTable of ContentsPart I. Introduction: 1. The central ideas of this book; 2. Adverse selection: a history of exaggeration; Part II. Loss Coverage: 3. Introduction to loss coverage; 4. Basic mathematics of loss coverage; 5. Further mathematics of loss coverage; 6. Partial risk classification, separation and inclusivity; Part III. Further Aspects of Risk Classification: 7. A taxonomy of objections to risk classification; 8. Empirical evidence on adverse selection; 9. Myths of insurance rhetoric; 10. Myths of insurance economics; 11. Contexts where adverse selection may be stronger; 12. Risk classification and moral hazard; 13. Risk classification and big data; Part IV. Conclusion: 14. Summary and suggestions; Appendix A. Alternative demand functions; Appendix B. Multiple equilibria: a technical curiosity; References; Index.
£57.00
Cambridge University Press The Rise and Fall of Business Firms
Book SynopsisAt the intersection between statistical physics and rigorous econometric analysis, this powerful new framework sheds light on how innovation and competition shape the growth and decline of companies and industries. Analyzing various sources of data including a unique micro level database which collects historic data on the sales of more than 3,000 firms and 50,000 products in 20 countries, the authors introduce and test a model of innovation and proportional growth, which relies on minimal assumptions and accounts for the empirically observed regularities. Through a combination of extensive stochastic simulations and statistical tests, the authors investigate to what extent their simple assumptions are falsified by empirically observable facts. Physicists looking for application of their mathematical and modelling skills to relevant economic problems as well as economists interested in the explorative analysis of extensive data sets and in a physics-orientated way of thinking will findTrade Review'This is a superb and fascinating book. The distribution of firms' growth rates exhibits a large number of regularities, including some that are very hard to explain. The authors are pioneers in that enterprise, combining empirical and theoretical work. This team of economists and physicists provides a model for a future way to do economics.' Xavier Gabaix, Pershing Square Professor of Economics and Finance, Harvard University'The Rise and Fall of Business Firms offers a lucid reconstruction and extension of the exciting developments that fundamentally reshaped our understanding of how firms grow and evolve, brought to you by the scientists responsible for the key discoveries. A must for anyone interested in the deep laws that govern economic processes.' Albert-László Barabási, Robert Gray Dodge Professor of Network Science, Northeastern University'There is a long tradition of physicists being interested in and contributing to economics. That tradition continues here in The Rise and Fall of Business Firms. The book is based on generalized proportional growth models for the dynamics and stochastics of the growth and decline of business firms. For further studies, the book points out where more detailed specific inter-related complexities (such as among products, markets, and technologies) can be incorporated. The theoretical analysis paired with empirical data provides valuable insight for firms to understand their past trajectory and future choices.' Michael F. Schlesinger, Office of Naval ResearchTable of ContentsPreface; 1. Introduction; 2. Empirical regularities; 3. Innovation and the growth of business firms: a stochastic framework; 4. Testing our predictions; 5. Testing our assumptions; 6. Conclusions; 7. Appendices; References; Author index; Subject index.
£41.79
Cambridge University Press On the Shoulders of Giants Colleagues Remember Suzanne Scotchmers Contributions to Economics 57 Econometric Society Monographs Series Number 57
Book SynopsisThis book presents eleven classic papers by the late Professor Suzanne Scotchmer with introductions by leading economists and legal scholars. This book introduces Scotchmer's life and work; analyses her pioneering contributions to the economics of patents and innovation incentives, with a special focus on the modern theory of cumulative innovation; and describes her pioneering work on law and economics, evolutionary game theory, and general equilibrium/club theory. This book also provides a self-contained introduction to students who want to learn more about the various fields that Professor Scotchmer worked in, with a particular focus on patent incentives and cumulative innovation.Trade Review'Suzanne Scotchmer left us a remarkable legacy: highly original, deep, rigorous work on the economics of innovation, club theory and game theory. This book is a fitting tribute to her scholarship and to Suzanne as a person. It should be read by everyone with an interest in microeconomic theory.' Jerry R. Green, John Leverett Professor in the University, Harvard University'This is an invaluable volume, which will be on the bookshelf of every IP economist and legal scholar. It contains many of Suzanne Scotchmer's classical papers, often with an introduction by leading scholars, as well as very touching personal souvenirs from her friends and colleagues. A fitting tribute to this exceptional woman I was lucky to count as a friend.' Jean Tirole, Chairman, Toulouse School of Economics, FranceTable of Contents1. Introduction; 2. Threads in the tapestry; 3. Innovation theory (I): cumulative innovation; 4. Innovation theory (II): law and economics; 5. Clubs; 6. Evolutionary game theory; 7. Public policy; 8. Living legacy; 9. Epilogue.
£35.14
Cambridge University Press Processing Networks
Book SynopsisThis state-of-the-art account unifies material developed in journal articles over the last 35 years, with two central thrusts: It describes a broad class of system models that the authors call ''stochastic processing networks'' (SPNs), which include queueing networks and bandwidth sharing networks as prominent special cases; and in that context it explains and illustrates a method for stability analysis based on fluid models. The central mathematical result is a theorem that can be paraphrased as follows: If the fluid model derived from an SPN is stable, then the SPN itself is stable. Two topics discussed in detail are (a) the derivation of fluid models by means of fluid limit analysis, and (b) stability analysis for fluid models using Lyapunov functions. With regard to applications, there are chapters devoted to max-weight and back-pressure control, proportionally fair resource allocation, data center operations, and flow management in packet networks. Geared toward researchers and grTrade Review'The deep and rich theory of stochastic processing networks has served as the analytical foundation for the study of communication networks, cloud computing systems, and manufacturing networks. This book by two of the pioneers of the theory presents an authoritative and comprehensive treatment of the topic, and will serve as an important reference to researchers in the area.' R. Srikant, University of Illinois at Urbana-Champaign'A system of interconnected resources can become overloaded and unstable even though each of its individual resources has the capacity to meet the demands on it. This striking observation, first made thirty years ago, has stimulated a major field of research. This book, written by two of the pioneers and leading researchers in the field, is a clear and authoritative account of the state-of-the-art.' Frank Kelly, University of Cambridge'This book provides an elegant and unified exposition of the general modeling framework of stochastic processing networks (SPNs) and associated theory of stability using fluid models. Much of this material was only previously available in dispersed journal articles. Adopting a continuous-time Markov chain description for SPNs, valid under fairly general assumptions on arrivals, service times and controls, enables a self-contained, accessible treatment. An array of interesting examples and extensions, especially involving applications for telecommunication and data networks, enliven the volume. This monograph will be an invaluable premier resource for graduate students and researchers in computer science, electrical and industrial engineering, applied mathematics and operations management interested in theory and applications of stochastic processing networks.' Ruth J. Williams, University of California, San DiegoTable of Contents1. Introduction; 2. Stochastic processing networks; 3. Markov representations; 4. Extensions and complements; 5. Is stability achievable?; 6. Fluid limits, fluid equations and positive recurrence; 7. Fluid equations that characterize specific policies; 8. Proving fluid model stability using Lyapunov functions; 9. Max-weight and back-pressure control; 10. Proportionally fair resource allocation; 11. Task allocation in server farms; 12. Multi-hop packet networks; Appendix A. Selected topics in real analysis; Appendix B. Selected topics in probability; Appendix C. Discrete-time Markov chains; Appendix D. Continuous-time Markov chains and phase-type distributions; Appendix E. Markovian arrival processes; Appendix F. Convergent square matrices.
£47.49
Cambridge University Press The BlackScholesMerton Model as an Idealization of DiscreteTime Economies
Book SynopsisThis book examines whether continuous-time models in frictionless financial economies can be well approximated by discrete-time models. It specifically looks to answer the question: in what sense and to what extent does the famous Black-Scholes-Merton (BSM) continuous-time model of financial markets idealize more realistic discrete-time models of those markets? While it is well known that the BSM model is an idealization of discrete-time economies where the stock price process is driven by a binomial random walk, it is less known that the BSM model idealizes discrete-time economies whose stock price process is driven by more general random walks. Starting with the basic foundations of discrete-time and continuous-time models, David M. Kreps takes the reader through to this important insight with the goal of lowering the entry barrier for many mainstream financial economists, thus bringing less-technical readers to a better understanding of the connections between BSM and nearby discretTrade Review'He did it again - David M. Kreps, the unparalleled master of theory and exposition, now adds detailed discrete underpinnings to the Black-Scholes-Merton model. This beautifully written monograph forms bookends with the foundational Harrison-Kreps martingale theory of financial asset pricing. Every researcher and student in this field will want a copy!' Darrell Duffie, Dean Witter Distinguished Professor of Finance, Stanford University, California'In this monograph, David M. Kreps studies the question of how well, on economic grounds, classic models of Black, Scholes, and Merton idealize more comprehensible but less tractable discrete-time models. The book is a gold mine of mathematical tools for studying these issues.' Thomas J. Sargent, New York University and 2011 Nobel Laureate in Economics'David M. Kreps' previous work substantially generalized and clarified the Black-Scholes-Merton (BSM) model. In this superb monograph, he turns to another basic question: to what extent is the BSM model an idealization of models with discrete but fast trading opportunities? His elegant answer is bound to stimulate a large follow-up literature.' José Scheinkman, Charles and Lynn Zhang Professor of Economics, Columbia University, New York'Continuous-time finance involves conceptual and technical complexities, which are often swept under the rug when the material is taught to economists. This book cuts through the complexities while providing excellent economic intuition and insight. It helps the reader develop a deeper appreciation of the foundations of modern finance theory, and of the connections between continuous- and discrete-time models in economics more generally.' Dimitri Vayanos, Professor of Finance, London School of Economics and Political ScienceTable of Contents1. Introduction; 2. Finitely many states and dates; 3. Countinuous time and the Black-Scholes-Merton (BSM) Model; 4. BSM as an idealization of binomial-random-walk economies; 5. Random walks that are not binomial; 6. Barlow's example; 7. The Pötzelberger-Schlumprecht example and asymptotic arbitrage; 8. Concluding remarks, Part I: how robust an idealization is BSM?; 9. Concluding remarks, Part II: continuous-time models as idealizations of discrete time; Appendix.
£32.29
Cambridge University Press The Probability Companion for Engineering and Computer Science
Book SynopsisThis friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overvTrade Review'In addition to the usual topics of probability theory, a large portion of the book is devoted to presenting modern applications including Bayesian inference and MCMC. Students will appreciate the detailed derivations of formulas and the full solutions of problems. The text is interspersed with personal viewpoints and advice, which gives the book the flavour of a lively lecture by an enthusiastic teacher.' Robert Piché, Tampereen yliopisto, Finland'Adam Prügel-Bennett has created a great toolbox for all scientists working with models that take into account the uncertainty of the real world.' Wolfram Burgard, Albert-Ludwigs-Universität Freiburg, Germany'This is a wonderful book, one that I wish I'd had when learning about probability. Indeed, there are lots of gems in there that I'm looking forward to reading about myself! The book is beautifully illustrated and refreshingly full of insight, without overly formal mathematical jargon. This book would appeal to students and researchers that are competent in mathematics and delight in gaining a deeper understanding of the subject, both from an intuitive and mathematical standpoint. It excels in demonstrating the wide applicability of probabilistic approaches to problem solving and modelling. This book deserves to be on the shelf of any researcher that uses probability to solve problems.' David Barber, University College London'The book can be very recommended all readers, who are interested in this field.' Ludwig Paditz, Theatre and Performance TheoryTable of Contents1. Introduction; 2. Survey of distributions; 3. Monte Carlo; 4. Discrete random variables; 5. The normal distribution; 6. Handling experimental data; 7. Mathematics of random variables; 8. Bayes; 9. Entropy; 10. Collective behavior; 11. Markov chains; 12. Stochastic processes; Appendix A. Answers to exercises; Appendix B. Probability distributions.
£44.64
Cambridge University Press Probability Theory and Statistical Inference
Book SynopsisDoubt over the trustworthiness of published empirical results is not unwarranted and is often a result of statistical mis-specification: invalid probabilistic assumptions imposed on data. Now in its second edition, this bestselling textbook offers a comprehensive course in empirical research methods, teaching the probabilistic and statistical foundations that enable the specification and validation of statistical models, providing the basis for an informed implementation of statistical procedure to secure the trustworthiness of evidence. Each chapter has been thoroughly updated, accounting for developments in the field and the author''s own research. The comprehensive scope of the textbook has been expanded by the addition of a new chapter on the Linear Regression and related statistical models. This new edition is now more accessible to students of disciplines beyond economics and includes more pedagogical features, with an increased number of examples as well as review questions and Table of Contents1. An introduction to empirical modeling; 2. Probability theory as a modeling framework; 3. The concept of a probability model; 4. A simple statistical model; 5. Chance regularities and probabilistic concepts; 6. Statistical models and dependence; 7. Regression models; 8. Introduction to stochastic processes; 9. Limit theorems in probability; 10. From probability theory to statistical inference; 11. Estimation I: properties of estimators; 12. Estimation II: methods of estimation; 13. Hypothesis testing; 14. Linear regression and related models; 15. Mis-specification (M-S) testing.
£999.99
Constable & Robinson Statistical Ten Easy Ways to Avoid Being Misled
Book SynopsisAn accessible guide to interrogating the many statistics we are bombarded by every day.
£11.24
Nova Science Publishers Inc Modelling Multidisciplinary Causes of the Greek
Book Synopsis
£163.19
De Gruyter Central Bank Balance Sheet and Real Business
Book SynopsisCentral Bank Balance Sheet and Real Business Cycles argues that a deeper comprehension of changes to the central bank balance sheet can lead to more effective policymaking. Any transaction engaged in by the central bank—issuing currency, conducting foreign exchange operations, investing its own funds, intervening to provide emergency liquidity assistance and carrying out monetary policy operations—influences its balance sheet. Despite this, many central banks throughout the world have largely ignored balance sheet movements, and have instead focused on implementing interest rates. In this book, Mustapha Abiodun Akinkunmi highlights the challenges and controversies faced by central banks in the past and present when implementing policies, and analyzes the links between these policies, the central bank balance sheet, and the consequences to economies as a whole. He argues that the composition and evolution of the central bank balance sheet provides a valuable basis for understanding the needs of an economy, and is an important tool in developing strategies that would most effectively achieve policy goals. This book is an important resource for anyone interested in monetary policy or whose work is affected by the actions of the policies of central banks.Table of ContentsChapter 1: Global Genesis of the Central Bank 1 1.1 Origin of the Central Bank 1 1.2 Roles of the Central Bank 1 Questions 3 Chapter 2: Relevance of the Central Bank Balance Sheet 5 2.1 Understanding Relevance of Central Bank Balance Sheet in Functions of Economy 5 2.1.1 Similarities and Differences between a Company’s Balance Sheet and a Central Bank Balance Sheet 5 2.2 Trajectory Relevance of the Central Bank Balance Sheet 8 2.3 Externality of the Central Bank Balance Sheet Size 12 Questions 13 Chapter 3: Components of Central Bank Balance Sheets 15 3.1 Factors Influence the Reporting Frequency of Central Bank Balance Sheets 17 3.2 Components of Central Bank Assets and their Composition Analysis 18 3.2.1 Foreign Assets 18 3.3 Components of Central Bank Liabilities and their Composition Analysis 20 3.3.1 Banknotes 21 3.3.2 Commercial Bank Reserves 23 3.3.3 Capital 23 Questions 25 Chapter 4: Analytical Framework of Central Bank Balance Sheets 27 4.1 Structure of Central Bank Balance Sheets 27 4.2 Balance Sheet Indicators 28 4.2.1 Computation of Indicators 28 4.2.2 Significance of these Indicators 29 4.2.3 Underlying Assumptions of Four Indicators 29 4.3 Determinants of Central Bank Balance Sheet Composition 30 4.4 Classification of Central Bank Balance Sheet Components 31 4.5 Theoretical Landscape: Quantity Theory of Money versus Quality Theory of Money 32 4.5.1 Quantity Theory of Money 32 4.5.2 Quality Theory of Money 34 Questions 39 Chapter 5: Evolution of Central Bank Balance Sheets and Their Heterogeneous Dimensions 41 5.1 What Makes Central Bank Balance Sheets Special? 41 5.2 Historic Uses of Central Bank Balance Sheets 43 5.3 Composition of Central Bank Balance Sheet Liabilities 44 5.3.1 Central Bank Liabilities in Normal Times 45 5.3.2 Snapshot of Selected Economies’ Performance before the 2007–008 Crisis 46 5.3.3 Country-by-Country Snapshots of Economic Structure Since 2005 47 5.3.4 A Look at the Balance Sheets: 2005 and 2006 56 5.3.5 Snapshot of Selected Economies’ Performance during the 2007–008 Crisis 65 5.3.6 Snapshot of Selected Economies’ Performance Today 74 5.3.7 Central Bank Liabilities Today 78 Conclusion 86 Questions 86 Chapter 6: Composition of Central Bank Balance Sheet Assets 87 6.1 Central Bank Assets in Normal Times 87 6.1.1 Central Bank Assets of Emerging Economies 95 6.2 Central Bank Assets During the 2007–2008 Crisis 96 6.2.1 Central Bank Assets of Developing and Emerging Economies during the 2007–2008 Crisis 104 6.3 Central Bank Assets Today 104 6.3.1 Central Bank Assets of Emerging Economies Today 112 6.4 Asset-Side Composition and Economic Growth Nexus 115 6.4.1 Asset-Growth Nexus before the 2007–2008 Crisis 115 6.4.2 Asset-Growth Nexus during the 2007–2008 Crisis 116 6.4.3 Asset-Growth Nexus after the 2007–2008 Crisis 116 6.5 The Evolution of Central Bank Balance Sheets in the Future 117 Questions 119 Chapter 7: Financial Ratios of the Central Bank Balance Sheet 121 7.1 International Strength 121 7.2 External Strength Ratio 122 7.3 External Impact Ratio 123 7.4 Liquidity Ratios I, II, and III 123 7.4.1 Liquidity Ratio I 124 7.4.2 Liquidity Ratio II 124 7.4.3 Liquidity Ratio III 124 7.5 Equity Ratio 125 Questions 126 Chapter 8: Central Bank Operations 127 8.1 Types of Central Bank Operations 127 8.1.1 Supply Liquidity 127 8.1.2 Absorb Excess Liquidity 127 8.1.3 Asset Securities 128 8.1.4 Off Balance Swap 131 8.2 Central Bank Policy Instruments 131 8.2.1 Open Market Operations 131 8.2.2 Reserve Requirements 132 8.2.3 Discount Rate 134 8.2.4 Money Market Investor Funding Facility 135 8.2.5 Term Auction Facility 136 8.2.6 Commercial Paper Funding Facility 137 8.2.7 Primary Dealer Credit Facility 140 8.2.8 Recap of Policy Instruments 141 Questions 141 Chapter 9: Real Business Cycles 143 9.1 The RBC Model 143 9.1.1 Features of RBC Models 144 9.1.2 Basic Economic Factors 145 9.1.3 Fluctuations in the Business Cycle 145 9.1.4 Boom and Recession 145 9.2 Concept of RBCs Applied to Economic Policy 149 9.3 Techniques of Estimating RBCs 150 9.4 Methods to Estimate Potential Output and Output Gaps 151 9.4.1 Trending Methods 152 9.4.2 Univariate Filters Method 153 9.4.3 Multivariate Filters 156 9.4.4 Production Function Approaches 157 9.4.5 Criteria for Evaluating Different Methods of Estimating Potential Output 161 Questions 165 Chapter 10: Central Bank Balance Sheets and Real Business Cycles 167 10.1 Linkages between Central Bank Balance Sheets and Real Business Cycles 167 10.1.1 Monetary Policies and Business Cycles 168 10.1.2 Identifying an Acceptable Range of Values 169 10.1.3 Asset Transparency 170 10.2 Impact Evaluation of Central Bank Balance Sheets on Economic Environment 171 10.2.1 Central Bank Balance Sheets and Debt Management 172 10.2.2 Modeling Challenges Confronting Central Bank Balance Sheets 173 Questions 177 Chapter 11: Conclusion 179 Questions Left Unanswered: Areas for Future Research 180 Policy Debates 181 Appendix I: Central Bank Balance Sheets of Different Countries 183 Asia Region 183 Malaysia’s Central Bank Balance Sheets for the Month of February 2018 183 Monetary Authority of Singapore 184 Africa Region 185 Nigeria’s Central Bank Balance Sheets for the Month of November 2017 185 South America 186 Central Reserve Bank of Peru 186 Brazil 188 Chile 189 Argentina 190 North America 193 Mexico 193 Canada 194 United States 195 Appendix II: Abbreviations 197 References 199 Index 205
£16.00
Technics Publications LLC Data Science: Mindset, Methodologies &
Book SynopsisMaster the concepts and strategies underlying success and progress in data science. From the author of the bestsellers, Data Scientist and Julia for Data Science, this book covers four foundational areas of data science. The first area is the data science pipeline including methodologies and the data scientists toolbox. The second are essential practices needed in understanding the data including questions and hypotheses. The third are pitfalls to avoid in the data science process. The fourth is an awareness of future trends and how modern technologies like Artificial Intelligence (AI) fit into the data science framework. Targeted towards data science learners of all levels, this book aims to help the reader go beyond data science techniques and obtain a more holistic and deeper understanding of what data science entails. With a focus on the problems data science tries to solve, this book challenges the reader to become a self-sufficient player in the field.
£39.09
De Gruyter The Mystery of Wealth: Capitalism. Democracy.
Book SynopsisThe purpose of this book is to demystify the causes of wealth and poverty like never before done. It is the seminal comprehensive presentation of the CDR index. The CDR index is a mathematical model that shows how capitalism (C), democracy (D) and rule of law (R) jointly with natural resources and geography explain almost all economic growth. As it turns out, capitalism, democracy, and rule of law are intangible policy variables that are at the disposal of all countries and explain almost all gross domestic production of tangible products and services. There is also a minor contribution from non-policy variables such as natural resources and geography. These are all that countries require at their disposal and choice in order to enjoy their desired standard of living. The CDR economic growth model is a new paradigm.
£34.88
De Gruyter Smart Analysis of Tourism Policy Efficiency in
Book Synopsis The purpose of this study is to determine the role of tourism in the economy of Bulgaria. In this paper, we present the history of the Bulgarian tourism industry trends from the beginning to its contemporary policy patterns. We apply an econometric methodology consisting of unit root test, cointegration analysis, linear regression, correlation analysis, Granger causality test and 3-D visualizations by IBM Watson Studio based on the statistics for the period 1980-2017. Exploring the link between tourism and the economic development of Bulgaria, the tourism – led - growth hypothesis about Bulgaria is validated for the post-communism period. Our findings show that a relationship between tourism and Bulgaria’s economic development exists. We can conclude that tourism is in part an endogenous growth process. ABSTRACTING & INDEXING Smart Analysis of Tourism Policy Efficiency in Bulgaria for the Period 1980-2017 is covered by the following services: Baidu ScholarBarnes & NobleBayerische StaatsbibliothekBDSBoDBowker Book DataCiandoCNKI Scholar (China National Knowledge Infrastructure)DimensionsEBSCOExLibrisGoogle BooksGoogle ScholarNavigaReadCubeSemantic ScholarTDOne (TDNet)WorldCat (OCLC)X-MOLAdditionally, the proceedings volume is registered and indexed in the Crossref database and accessible on Amazon.
£13.50
Emerald Publishing Limited Asymptotic Theory for Econometricians
Book SynopsisAn econometric estimator is a solution to an optimization problem. This book provides the tools and concepts necessary to study the behavior of econometric estimators and test statistics in large samples.Table of ContentsThe Linear Model and Instrumental Variables Estimators. Consistency. Laws of Large Numbers. Asymptotic Normality. Central Limit Theory. Estimating Asymptotic Covariance Matrices. Functional Central Limit Theory and Applications. Directions for Further Study. Solution Set. References. Index.
£96.06
Oxford University Press Reproducible Econometrics Using R
a huge range and FREE tracked UK delivery on ALL orders.
£56.05
Oxford University Press Applied Macroeconometrics
Book SynopsisThis text provides graduate students of macroeconomics, econometrics, and monetary economics with discussion and practical illustrations of the techniques used in applied macroeconometrics. Until the 1970s, there was consensus regarding both the theoretical foundations and the empirical specification of applied macroeconometric modelling, commonly known as the Cowles Commission approach. This is no longer the case: the Cowles Commission approach broke down in the 1970s, to be replaced by a number of prominent competing methods--the LSE (London School of Economics) approach, the VAR approach, and the intertemporal optimization/Real Business Cycle approach. Applied Macroeconometrics examines the empirical research strategy of these alternatives by interpreting them as attempts to solve the problems observed in the Cowles Commission approach. The different research strategies are illustrated with specific reference to real-world examples, particularly with respect to the monetary transmisTrade ReviewThis book provides an extremely useful and accessible description of a wide range of techniques and approaches currently used in applied macroeconometrics * Journal of Applied Econometrics, Vol.16, No.5 *
£150.00
Oxford University Press Financial and Macroeconomic Connectedness
Book SynopsisConnections among different assets, asset classes, portfolios, and the stocks of individual institutions are critical in examining financial markets. Interest in financial markets implies interest in underlying macroeconomic fundamentals. In Financial and Macroeconomic Connectedness, Frank Diebold and Kamil Yilmaz propose a simple framework for defining, measuring, and monitoring connectedness, which is central to finance and macroeconomics. These measures of connectedness are theoretically rigorous yet empirically relevant. The approach to connectedness proposed by the authors is intimately related to the familiar econometric notion of variance decomposition. The full set of variance decompositions from vector auto-regressions produces the core of the ''connectedness table.'' The connectedness table makes clear how one can begin with the most disaggregated pair-wise directional connectedness measures and aggregate them in various ways to obtain total connectedness measures. The authorTrade ReviewDiebold and Yilmaz's timely book develops powerful new network tools for understanding the inter-dependence of risks in large-scale financial systems. These tools shed important new light on past financial crises and will fill an important gap in the monitoring of systemic risk going forward. * Peter Christoffersen, Professor of Finance, Rotman School of Management, University of Toronto. *The aftermath of the Lehman bankruptcy revealed that economists lacked understanding of the linkages within the financial industry and across the different sectors of the economy. The book by Frank Diebold and Kamil Yilmaz has many fresh ideas and new tools to study this very important topic. It is a must-read for anybody interested in this burgeoning area of research. * Eric Ghysels, Bernstein Distinguished Professor of Economics and Professor of Finance, University of North Carolina, Chapel Hill *We live in a highly integrated world economy with much stronger cross-border connections today than any other time in history. We need to have a better grasp of these connections as they are now at the heart of everyday macroeconomics and finance. Diebold and Yilmaz provide a masterful framework that greatly enhances our understanding of these connections. They also open new research avenues by showing practical applications of their framework in different contexts. And all of these make their book a classical reference on the topic. * Ayhan Kose, Director, Development Prospects Group, The World Bank *Table of ContentsChapter 1: Measuring and Monitoring Connectedness ; Chapter 2: U.S. Asset Classes ; Chapter 3: Major U.S. Financial Institutions ; Chapter 4: Global Stock Markets ; Chapter 5: Sovereign Bond Markets ; Chapter 6: Foreign Exchange Markets ; Chapter 7: Assets Across Countries ; Chapter 8: Global Business Cycles
£49.40
OUP Oxford The Oxford Handbook of Bayesian Econometrics
Book SynopsisBayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as Trade ReviewThis Handbook is an excellent piece of scholarly work that displays the full power of the Bayesian method. * Gael Martin *Table of ContentsPART I: PRINCIPLES ; PART II: METHODS ; PART III: APPLICATIONS
£142.50
Oxford University Press, USA Econometric Methods for Labour Economics
Book SynopsisThis book provides an accessible presentation of the standard statistical techniques used by labour economists. It emphasises both the input and the output of empirical analysis and covers five major topics concerning econometric methods used in labour economics: regression and related methods, choice modelling, selectivity issues, duration analysis, and policy evaluation techniques. Each of these is presented in terms of model specification, possible estimation problems, diagnostic checking, and interpretation of the output. It aims to provide guidance to practitioners on how to use the techniques and how to make sense of the results that are produced. It covers methods that are considered to be ''standard'' tools in labour economics, but which are often given only a brief and highly technical treatment in econometrics textbooks. It will be a useful reference for postgraduates and advanced undergraduates, researchers embarking on empirical labour market analysis, and for more experienTable of ContentsIntroduction ; 1. The Use of Linear Regression in Labour Economics ; 2. Further Regression Issues in Labour Economics ; 3. Dummy and Ordinal Dependent Variables ; 4. Selectivity ; 5. Duration Models ; 6. Evaluation of Policy Measures ; Conclusion
£69.35
Oxford University Press Time Series Analysis by State Space Methods
Book SynopsisThis new edition updates Durbin & Koopman''s important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series.Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations.Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.Trade ReviewReview from previous edition ...provides an up-to-date exposition and comprehensive treatment of state space models in time series analysis...This book will be helpful to graduate students and applied statisticians working in the area of econometric modelling as well as researchers in the areas of engineering, medicine and biology where state space models are used. * Journal of the Royal Statistical Society *Table of ContentsPART I: THE LINEAR STATE SPACE MODEL; PART II: NON-GAUSSIAN AND NONLINEAR STATE SPACE MODELS
£109.25
OUP Oxford Oxford Handbook of Bayesian Econometrics
Book SynopsisBayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as Trade ReviewThis Handbook is an excellent piece of scholarly work that displays the full power of the Bayesian method. All chapters, whilst reasonably self-contained, also serve as springboards to the broader literature, with extensive referencing being a feature of all. Access to specialized computer code is provided in some cases, serving to aid in the wider dissemination and use of the paradigm. * Gael Martin, The Econometrics Journal *Table of ContentsPART I: PRINCIPLES ; PART II: METHODS ; PART III: APPLICATIONS
£37.99
Palgrave Macmillan Microeconometrics The New Palgrave Economics Collection
Book SynopsisSpecially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.Trade ReviewPraise for the 8-volume edition: Winner of the 2008 PROSE Award: Best Mutlivolume Reference Work in the Humanitiesand Social Sciences (from the Professional and Scholarly Division of the Association of American Publishers) CHOICE Outstanding Academic Title 2008 "Much has changed in the latest incarnation of this dictionary. ...More than 1,500 economists contributed almost 1,900 signed entries; more than 1,000 of the entries are new or 'heavily revised' and expanded. Along with the descriptions of economic method from earlier editions, this edition includes much information on 'what those methods have found.' It also offers new emphasis on advances that have occurred in microeconomics, Bayesian theory, game theory, and behavioral, international, and experimental economics. ...A regularly updated online version of the dictionary is available www.dictionaryofeconomics.com, with site license pricing based on institution type and FTE. Summing Up: Highly recommended." - CHOICETable of ContentsContents General Preface Introduction List of Contributors List of Entries Entries A-Z Index
£76.49
Penguin Random House LLC StateSpace Models with Regime Switching
£65.05
Springer New York Partial Identification of Probability Distributions Springer Series in Statistics
Book SynopsisThe book presents in a rigorous and thorough manner the main elements of Charles Manski's research on partial identification of probability distributions. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric.Trade ReviewFrom the reviews: "Charles Manski has produced a nice and compact text written with extreme care, providing technical detail, and mathematical proofs where needed." Biometrics, March 2005 "This book is an excellent and rigorous presentation of the state of research in the area of partial identification of populations and credible inference, in which the author has made many important contributions. … The overall quality of the book is very good. … The main part of each chapter is written in a textbook style. … Clearly, both methodology and the applications presented are intended to provide statisticians with a good foundation for further study in the subject … ." (Evdokia Xekalaki, Zentralblatt MATH, Vol. 1047 (22), 2004) "I found the material very pertinent, departing, as it does, from the usual parametric approach in which the conclusions depend rather critically on the probability model adopted. Given a chance, it will make the traditionalist, like me, stop and think and perhaps, try to mend their ways a little. The main part of each chapter is written in textbook style, but fairly formally and rigorously … . At the end of each chapter appear ‘Complements’, giving examples and extensions, and ‘Endnotes’… ." (M. J. Crowder, Short Book Reviews, Vol. 23 (3), 2003) "This book, containing ten chapters, is the first comprehensive presentation of the theory of partial identification of probability distributions. It gives an overview of the research into this topic." (M. Riedel, Mathematical Reviews, Issue 2006 g) "The book is carefully and thoughtfully written. Some chapters start with a cogent section on the "anatomy of the problem," and all end with complements addressing specific contexts." (Alan F. Kaar, Journal of the American Statistical Association, Vol. 102, No. 477, 2007)Table of ContentsMissing Outcomes * Instrumental Variables * Conditional Prediction with Missing Data * Contaminated Outcomes * Regressions, Short and Long * Response-Based Sampling * Analysis of Treatment Response * Monotone Treatment Response * Monotone Instrumental Variables * The Mixing Problem
£113.99
Springer Country Risk Evaluation
a huge range and FREE tracked UK delivery on ALL orders.
£85.49
Springer Mathematical Optimization and Economic Analysis
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£104.49
Springer MicroEconometrics Methods of Moments and Limited Dependent Variables
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£152.99
Springer New York Time Series Theory and Methods Vol 2 Springer Series in Statistics
Book Synopsis1 Stationary Time Series.- 2 Hilbert Spaces.- 3 Stationary ARMA Processes.- 4 The Spectral Representation of a Stationary Process.- 5 Prediction of Stationary Processes.- 6* Asymptotic Theory.- 7 Estimation of the Mean and the Autocovariance Function.- 8 Estimation for ARMA Models.- 9 Model Building and Forecasting with ARIMA Processes.- 10 Inference for the Spectrum of a Stationary Process.- 11 Multivariate Time Series.- 12 State-Space Models and the Kalman Recursions.- 13 Further Topics.- Appendix: Data Sets.Table of Contents1 Stationary Time Series.- 2 Hilbert Spaces.- 3 Stationary ARMA Processes.- 4 The Spectral Representation of a Stationary Process.- 5 Prediction of Stationary Processes.- 6* Asymptotic Theory.- 7 Estimation of the Mean and the Autocovariance Function.- 8 Estimation for ARMA Models.- 9 Model Building and Forecasting with ARIMA Processes.- 10 Inference for the Spectrum of a Stationary Process.- 11 Multivariate Time Series.- 12 State-Space Models and the Kalman Recursions.- 13 Further Topics.- Appendix: Data Sets.
£104.49
