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Econometric Theory and Methods

Authors:
Russell Davidson and James G. MacKinnon
Publisher: Oxford University Press
Copyright: 2004
ISBN-13: 978-0-19-512372-2
Pages: 750; hardcover
Price: $49.50

Comment from the Stata technical group

Davidson and MacKinnon’s Econometric Theory and Methods provides an excellent introduction to modern methods of estimation and inference used in econometrics. The first-principles approach, which allows readers new to the material to develop a deep understanding of the topics, makes this book a valuable resource for both students and applied researchers.

Over the years, Davidson and MacKinnon have become known for their geometrical explanations, use of augmented regressions, and attention to numerical methods. This new book lives up to their well-deserved reputation. For instance, in a refreshing departure from the usual presentation, Davidson and MacKinnon introduce bootstrap and simulation techniques early on in their presentation of inference, rather than waiting until a later chapter to introduce these now-common techniques.

Throughout the book, they present material at a rigorous yet very approachable level. For each topic considered, the authors provide an introduction and then ease the reader into advanced topics that are not always covered in other books. For instance, while hypothesis testing and confidence-interval estimation could be collapsed into a matter of pages, Davidson and MacKinnon take the time to explain such important concepts as the power of a test, exact versus approximate confidence regions, asymptotically pivotal statistics, and the advantages of bootstrapping studentized statistics. Also included in chapter five is a discussion of heteroskedasticity-consistent covariance matrices.

The authors provide detailed introductions to six of the major methods of estimation used in econometrics: ordinary least squares, nonlinear regression, generalized least squares, instrumental variables, the generalized method of moments (GMM), and maximum likelihood. Chapter nine contains one of the best introductions to GMM currently available, covering both simulated GMM and simulated maximum likelihood are covered in detail. Plenty of references are given so that readers can pursue more advanced topics; for example, chapter eight briefly discusses the “weak instruments” problem and then provides no fewer than fifteen references.

While this book provides an excellent introduction to the general methods of estimation and inference used in econometrics, the standard topics are covered very quickly. As in their 1993 book, Davidson and MacKinnon condense their treatment of limited dependent variables into a single chapter, and panel data get only a few pages. Readers who want a detailed discussion of specific models will want to supplement the material in this book with Jeffrey Wooldridge’s Econometric Analysis of Cross Section and Panel Data.


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 the 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-Lovell 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 Distributions
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 Estimator
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 Method 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 Remark
9.8 Exercises
10 The Method of Maximum Likelihood
10.1 Introduction
10.2 Basic Concepts of Maximum Likelihood Estimation
10.3 Asymptotic Properties 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 Regression
s 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 Estimation AR, MA, and ARMA Models
13.4 Single-Equation Dynamic Models
13.5 Seasonality
13.6 Autoregressive Conditional Heteroskedasticity
13.7 Vector Autoregressions
13.8 Final Remarks
13.9 Exercises
14 Unit Roots and Cointegration
14.1 Introduction
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 Models
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
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