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Econometric Theory and Methods

ISBN-10: 0195123727

ISBN-13: 9780195123722

Edition: 2004

Authors: Russell Davidson, James G. MacKinnon

List price: $163.95
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This text 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.
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Book details

List price: $163.95
Copyright year: 2004
Publisher: Oxford University Press, Incorporated
Publication date: 10/16/2003
Binding: Hardcover
Pages: 768
Size: 6.25" wide x 9.25" long x 1.50" tall
Weight: 2.640
Language: English

Data, Solutions, and Corrections
Regression Models
Distributions, Densities, and Moments
The Specification of Regression Models
Matrix Algebra
Method-of-Moments Estimation
Notes on Exercises
The Geometry of Linear Regression
The Geometry of Vector Spaces
The Geometry of OLS Estimation
The Frisch-Waugh-Lowell Theorem
Applications of the FWL Theorem
Influential Observations and Leverage
Final Remarks
The Statistical Properties of Ordinary Least Squares
Are OLS Parameter Estimators Unbiased?
Are OLS Parameter Estimators Consistent?
The Covariance Matrix of the OLS Parameter Estimates
Efficiency of the OLS Estimator
Residuals and Error Terms
Misspecification of Linear Regression Models
Measures of Goodness of Fit
Final Remarks
Hypothesis Testing in Linear Regression Models
Basic Ideas
Some Common Distractions
Exact Tests in the Classical Normal Linear Model
Large-Sample Tests in Linear Regression Models
Simulation-Based Tests
The Power of Hypothesis Tests
Final Remarks
Confidence Intervals
Exact and Asymptotic Confidence Intervals
Bootstrap Confidence Intervals
Confidence Regions
Heteroskedasticity-Consistent Covariance Matrices
The Delta Method
Final Remarks
Nonlinear Regression
Method-of-Moments Estimators for Nonlinear Models
Nonlinear Least Squares
Computing NLS Estimates
The Gauss-Newton Regression
One-Step Estimation
Hypothesis Testing
Heteroskedasticity-Robust Tests
Final Remarks
Generalized Least Squares and Related Topics
The GLS Eliminator
Computing GLS Estimates
Feasible Generalized Least Squares
Autoregressive and Moving-Average Processes
Testing for Serial Correlation
Estimating Models with Autoregressive Errors
Specification Testing and Serial Correlation
Models for Panel Data
Final Remarks
Instrumental Variables Estimation
Correlation Between Error Terms and Regressors
Instrumental Variables Estimation
Finite-Sample Properties of IV Estimators
Hypothesis Testing
Testing Overidentifying Restrictions
Durbin-Wu-Hausman Tests
Bootstrap Tests
IV Estimation of Nonlinear Models
Final Remarks
The Generalized Methods of Moments
GMM Estimators for Linear Regression Models
HAC Covariance Matrix Estimation
Tests Based on the GMM Criterion Function
GMM Estimators for Nonlinear Models
The Method of Simulated Moments
Final Remarks
The Method of Maximum Likelihood
Basic Concepts of Maximum Likelihood Estimation
Asymptotic Propertied of ML Estimators
The Covariance Matrix of the ML Estimator
Hypothesis Testing
The Asymptotic Theory of the Three Classical Tests
ML Estimation of Models with Autoregressive Errors
Transformations of the Dependent Variable
Final Remarks
Discrete and Limited Dependent Variables
Binary Response Models: Estimation1
Binary Response Models: Inference
Models for More than Two Discrete Responses
Models for Count Data
Models for Censored and Truncated Data
Sample Selectivity
Duration Models
Final Remarks
Multivariate Models
Seemingly Unrelated Linear Regressions
Systems of Nonlinear Regressions
Linear Simultaneous Equations Models
Maximum Likelihood Estimation
Nonlinear Simultaneous Equations Models
Final Remarks
Appendix: Detailed Results on FIML and LIML
Methods for Stationary Time-Series Data