Econometric Theory and Methods

Econometric Theory and Methods provides a unified treatment of modern econometric theory and practical econometric methods. The geometrical approach to least squares is emphasized, as is the method of moments, which is used to motivate a wide variety of estimators and tests. Simulation methods, including the bootstrap, are introduced early and used extensively.The book deals with a large number of modern topics. In addition to bootstrap and Monte Carlo tests, these include sandwich covariance matrix estimators, artificial regressions, estimating functions and the generalized method of moments, indirect inference, and kernel estimation. Every chapter incorporates numerous exercises, some theoretical, some empirical, and many involving simulation.Econometric Theory and Methods is designed for beginning graduate courses. The book is suitable for both one- and two-term courses at the Masters or Ph.D. level. It can also be used in a final-year undergraduate course for students with sufficient backgrounds in mathematics and statistics.  

Table of Contents:
Preface Data, Solutions, and Corrections 1. Regression Models 1.1. Introduction 1.2. Distributions, Densities, and Moments 1.3. The Specification of Regression Models 1.4. Matrix Algebra 1.5. Method-of-Moments Estimation 1.6. Notes on Exercises 1.7. Exercises 2. The Geometry of Linear Regression 2.1. Introduction 2.2. The Geometry of Vector Spaces 2.3. The Geometry of OLS Estimation 2.4. The Frisch-Waugh-Lowell Theorem 2.5. Applications of the FWL Theorem 2.6. Influential Observations and Leverage 2.7. Final Remarks 2.8. Exercises 3. The Statistical Properties of Ordinary Least Squares 3.1. Introduction 3.2. Are OLS Parameter Estimators Unbiased? 3.3. Are OLS Parameter Estimators Consistent? 3.4. The Covariance Matrix of the OLS Parameter Estimates 3.5. Efficiency of the OLS Estimator 3.6. Residuals and Error Terms 3.7. Misspecification of Linear Regression Models 3.8. Measures of Goodness of Fit 3.9. Final Remarks 3.10. Exercises 4. Hypothesis Testing in Linear Regression Models 4.1. Introduction 4.2. Basic Ideas 4.3. Some Common Distractions 4.4. Exact Tests in the Classical Normal Linear Model 4.5. Large-Sample Tests in Linear Regression Models 4.6. Simulation-Based Tests 4.7. The Power of Hypothesis Tests 4.8. Final Remarks 4.9. Exercises 5. Confidence Intervals 5.1. Introduction 5.2. Exact and Asymptotic Confidence Intervals 5.3. Bootstrap Confidence Intervals 5.4. Confidence Regions 5.5. Heteroskedasticity-Consistent Covariance Matrices 5.6. The Delta Method 5.7. Final Remarks 5.8. Exercises 6. Nonlinear Regression 6.1. Introduction 6.2. Method-of-Moments Estimators for Nonlinear Models 6.3. Nonlinear Least Squares 6.4. Computing NLS Estimates 6.5. The Gauss-Newton Regression 6.6. One-Step Estimation 6.7. Hypothesis Testing 6.8. Heteroskedasticity-Robust Tests 6.9. Final Remarks 6.10. Exercises 7. Generalized Least Squares and Related Topics 7.1. Introduction 7.2. The GLS Eliminator 7.3. Computing GLS Estimates 7.4. Feasible Generalized Least Squares 7.5. Heteroskedasticity 7.6. Autoregressive and Moving-Average Processes 7.7. Testing for Serial Correlation 7.8. Estimating Models with Autoregressive Errors 7.9. Specification Testing and Serial Correlation 7.10. Models for Panel Data 7.11. Final Remarks 7.12. Exercises 8. Instrumental Variables Estimation 8.1. Introduction 8.2. Correlation Between Error Terms and Regressors 8.3. Instrumental Variables Estimation 8.4. Finite-Sample Properties of IV Estimators 8.5. Hypothesis Testing 8.6. Testing Overidentifying Restrictions 8.7. Durbin-Wu-Hausman Tests 8.8. Bootstrap Tests 8.9. IV Estimation of Nonlinear Models 8.10. Final Remarks 8.11. Exercises 9. The Generalized Methods of Moments 9.1. Introduction 9.2. GMM Estimators for Linear Regression Models 9.3. HAC Covariance Matrix Estimation 9.4. Tests Based on the GMM Criterion Function 9.5. GMM Estimators for Nonlinear Models 9.6. The Method of Simulated Moments 9.7. Final Remarks 9.8. Exercises 10. The Method of Maximum Likelihood 10.1. Introduction 10.2. Basic Concepts of Maximum Likelihood Estimation 10.3. Asymptotic Propertied of ML Estimators 10.4. The Covariance Matrix of the ML Estimator 10.5. Hypothesis Testing 10.6. The Asymptotic Theory of the Three Classical Tests 10.7. ML Estimation of Models with Autoregressive Errors 10.8. Transformations of the Dependent Variable 10.9. Final Remarks 10.10. Exercises 11. Discrete and Limited Dependent Variables 11.1. Introduction 11.2. Binary Response Models: Estimation 11.3. Binary Response Models: Inference 11.4. Models for More than Two Discrete Responses 11.5. Models for Count Data 11.6. Models for Censored and Truncated Data 11.7. Sample Selectivity 11.8. Duration Models 11.9. Final Remarks 11.10. Exercises 12. Multivariate Models 12.1. Introduction 12.2. Seemingly Unrelated Linear Regressions 12.3. Systems of Nonlinear Regressions 12.4. Linear Simultaneous Equations Models 12.5. Maximum Likelihood Estimation 12.6. Nonlinear Simultaneous Equations Models 12.7. Final Remarks 12.8. Appendix: Detailed Results on FIML and LIML 12.9. Exercises 13. Methods for Stationary Time-Series Data 13.1. Introduction 13.2. Autoregressive and Moving-Average Processes 13.3. Estimating AR, MA, and ARMA Models 13.4. Single-Equation Dynamic Models 13.5. Seasonality 13.6. Autoregressive Conditional Heteroskedasticity 13.7. Vector Autoregression 13.8. Final Remarks 13.9. Exercises 14. Unit Roots and Cointegration 14.1. Exercises 14.2. Random Walks and Unit Roots 14.3. Unit Root Tests 14.4. Serial Correlation and Unit Root Tests 14.5. Cointegration 14.6. Testing for Cointegration 14.7. Final Remarks 14.8. Exercises 15. Testing the Specification of Econometric Methods 15.1. Introduction 15.2. Specification Tests Based on Artificial Regressions 15.3. Nonnested Hypothesis Tests 15.4. Model Selection Based on Information Criteria 15.5. Nonparametric Estimation 15.6. Final Remarks 15.7. Appendix: Test Regressors in Artificial Regressions 15.8. Exercises References Author Index Subject Index

About the Author :
RUSSELL DAVIDSON holds the Canada Research Chair in Econometrics at McGill University in Montreal. He also teaches at GREQAM in Marseille and previously taught for many years at Queen's University. He has a Ph.D. in Physics from the University of Glasgow and a Ph.D. in Economics from the University of British Columbia. Professor Davidson is a Fellow of the Econometric Society and the author of many scientific papers. He is the coauthor of Estimation and Inference in Econometrics (OUP, 1993). JAMES G. MACKINNON is the Sir Edward Peacock Professor of Econometrics and Head of the Department at Queen's University in Kingston, Ontario, Canada, where he has taught since obtaining his Ph.D. from Princeton University in 1975. He is a Fellow of the Econometric Society and of the Royal Society of Canada and a past President of the Canadian Economics Association (2001-2002). Professor MacKinnon has written more than seventy journal articles and book chapters, and he is the coauthor of Estimation and Inference in Econometrics (OUP, 1993).

Review :
"This is a first class book, modern in conception and flawless in execution. The coverage is superb and it does things that many other books do not do or do not do adequately."--Richard E Quandt, Princeton University