Econometric Theory and Methods

Russell Davidson, James G. MacKinnon9780195123722, 0195123727

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 matrixestimators, 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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Table of contents :
Introduction……Page 1
Distributions, Densities, and Moments……Page 3
The Specification of Regression Models……Page 15
Matrix Algebra……Page 22
Method of Moments Estimation……Page 30
Notes on the Exercises……Page 36
Exercises……Page 37
chp15big.pdf……Page 0
Introduction……Page 41
The Geometry of Vector Spaces……Page 42
The Geometry of OLS Estimation……Page 53
The Frisch-kern -.08333emWaugh-Lovell Theorem……Page 62
Applications of the FWL Theorem……Page 68
Influential Observations and Leverage……Page 75
Final Remarks……Page 80
Exercises……Page 81
Introduction……Page 85
Are OLS Parameter Estimators Unbiased?……Page 87
Are OLS Parameter Estimators Consistent?……Page 91
The Covariance Matrix of the OLS Parameter Estimates……Page 96
Efficiency of the OLS Estimator……Page 103
Residuals and Error Terms……Page 106
Misspecification of Linear Regression Models……Page 110
Measures of Goodness of Fit……Page 114
Exercises……Page 117
Basic Ideas……Page 121
Some Common Distributions……Page 128
Exact Tests in the Classical Normal Linear Model……Page 137
Largekern .08333em-Sample Tests in Linear Regression Models……Page 145
Simulation-Based Tests……Page 154
The Power of Hypothesis Tests……Page 165
Exercises……Page 171
Introduction……Page 175
Exact and Asymptotic Confidence Intervals……Page 176
Bootstrap Confidence Intervals……Page 183
Confidence Regions……Page 187
Heteroskedasticity-Consistent Covariance Matrices……Page 194
The Delta Method……Page 200
Exercises……Page 207
Introduction……Page 210
Method of Moments Estimators for Nonlinear Models……Page 212
Nonlinear Least Squares……Page 221
Computing NLS Estimates……Page 225
The Gausskern .08333em-Newton Regression……Page 232
Onekern .08333em-Step Estimation……Page 237
Hypothesis Testing……Page 240
Heteroskedasticity-Robust Tests……Page 247
Final Remarks……Page 249
Exercises……Page 250
Introduction……Page 254
The GLS Estimator……Page 255
Computing GLS Estimates……Page 257
Feasible Generalized Least Squares……Page 261
Heteroskedasticity……Page 263
Autoregressive and Moving Average Processes……Page 267
Testing for Serial Correlation……Page 272
Estimating Models with Autoregressive Errors……Page 281
Specification Testing and Serial Correlation……Page 289
Models for Panel Data……Page 295
Final Remarks……Page 302
Exercises……Page 303
Introduction……Page 308
Correlation Between Error Terms and Regressors……Page 309
Instrumental Variables Estimation……Page 312
Finitekern .04166em-Sample Properties of IV Estimators……Page 321
Hypothesis Testing……Page 327
Testing Overidentifying Restrictions……Page 333
Durbin-{kern -.10em}Wu-Hausman Tests……Page 335
Bootstrap Tests……Page 339
IV Estimation of Nonlinear Models……Page 342
Exercises……Page 344
Introduction……Page 349
GMM Estimators for Linear Regression Models……Page 350
HAC Covariance Matrix Estimation……Page 359
Tests Based on the GMM Criterion Function……Page 362
GMM Estimators for Nonlinear Models……Page 366
The Method of Simulated Moments……Page 380
Final Remarks……Page 390
Exercises……Page 391
Basic Concepts of Maximum Likelihood Estimation……Page 396
Asymptotic Properties of ML Estimators……Page 405
The Covariance Matrix of the ML Estimator……Page 412
Hypothesis Testing……Page 417
The Asymptotic Theory of the Three Classical Tests……Page 426
ML Estimation of Models with Autoregressive Errors……Page 430
Transformations of the Dependent Variable……Page 432
Final Remarks……Page 438
Exercises……Page 439
Introduction……Page 446
Binary Response Models: Estimation……Page 447
Binary Response Models: Inference……Page 455
Models for More than Two Discrete Responses……Page 461
Models for Count Data……Page 470
Models for Censored and Truncated Data……Page 476
Sample Selectivity……Page 481
Duration Models……Page 484
Exercises……Page 490
Seemingly Unrelated Linear Regressions……Page 496
Systems of Nonlinear Regressions……Page 513
Linear Simultaneous Equations Models……Page 517
Maximum Likelihood Estimation……Page 527
Nonlinear Simultaneous Equations Models……Page 535
Final Remarks……Page 538
Appendix: Detailed Results on FIML and LIML……Page 539
Exercises……Page 545
Introduction……Page 551
Autoregressive and Moving Average Processes……Page 552
Estimating AR, MA, and ARMA Models……Page 560
Singlekern .04166em-Equation Dynamic Models……Page 569
Seasonality……Page 574
Autoregressive Conditional Heteroskedasticity……Page 581
Vector Autoregressions……Page 589
Final Remarks……Page 593
Exercises……Page 594
Random Walks and Unit Roots……Page 599
Unit Root Tests……Page 607
Serial Correlation and Unit Root Tests……Page 614
Cointegration……Page 618
Testing for Cointegration……Page 630
Exercises……Page 638
Introduction……Page 644
Specification Tests Based on Artificial Regressions……Page 645
Nonnested Hypothesis Tests……Page 659
Model Selection Based on Information Criteria……Page 669
Nonparametric Estimation……Page 671
Final Remarks……Page 683
Appendix: Test Regressors in Artificial Regressions……Page 684
Exercises……Page 686