We use cookies to enhance your experience on our website. By continuing to use our website, you are agreeing to our use of cookies. You can change your cookie settings at any time. Find out more
Cover

Econometric Theory and Methods

Russell Davidson and James G. MacKinnon

Publication Date - October 2003

ISBN: 9780195123722

768 pages
Hardcover
6-1/8 x 9-1/4 inches

In Stock

Retail Price to Students: $120.95

This book provides a unified treatment of modern econometric theory and practical econometric methods.

Description

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.

FEATURES
BLUnified Approach: New concepts are linked to old ones whenever possible, and the notation is consistent both within and across chapters wherever possible.
BLGeometry of Ordinary Least Squares: Introduced in Chapter 2, this method provides students with valuable intuition and allows them to avoid a substantial amount of tedious algebra later in the text.
BLModern Concepts Introduced Early: These include the bootstrap (Chapter 4), sandwich covariance matrices (Chapter 5), and artificial regressions (Chapter 6).
BLInclusive Treatment of Mathematics: Mathematical and statistical concepts are introduced as they are needed, rather than isolated in appendices or introductory chapters not linked to the main body of the text.
BLAdvanced Topics: Among these are models for duration and count data, estimating equations, the method of simulated moments, methods for unbalanced panel data, a variety of unit root and cointegration tests, conditional moment tests, nonnested hypothesis tests, kernel density regression, and kernel regression.
BLChapter Exercises: Every chapter offers numerous exercises, all of which have been answered by the authors in the Instructor's Manual. Particularly challenging exercises are starred and their solutions are available at the authors' website, providing a way for instructors and interested students to cover advanced material.

About the Author(s)

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).

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

Related Titles