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Econometric Theory and Methods - Russell Davidson

Econometric Theory and Methods

Hardcover

Published: 16th October 2003
Ships: 7 to 10 business days
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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
A-Unified Approach: New concepts are linked to old ones whenever possible, and the notation is consistent both within and across chapters wherever possible.
A-Geometry 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.
A-Modern Concepts Introduced Early: These include the bootstrap (Chapter 4), sandwich covariance matrices (Chapter 5), and artificial regressions (Chapter 6).
A-Inclusive 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.
A-Advanced 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.
A-Chapter 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.

"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

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

ISBN: 9780195123722
ISBN-10: 0195123727
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 768
Published: 16th October 2003
Dimensions (cm): 23.3 x 15.4  x 3.8
Weight (kg): 1.216