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 ·Unified Approach: New concepts are linked to old ones whenever possible, and the notation is consistent both within and across chapters wherever possible. ·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. ·Modern Concepts Introduced Early: These include the bootstrap (Chapter 4), sandwich covariance matrices (Chapter 5), and artificial regressions (Chapter 6). ·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. ·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. ·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.
Author(s): Russell Davidson, James G. MacKinnon
Publisher: Oxford University Press, USA
Year: 2003
Language: English
Pages: 712
Tags: Финансово-экономические дисциплины;Эконометрика;
0195123727_01__SCLZZZZZZZ_......Page 1
Introduction......Page 2
Distributions, Densities, and Moments......Page 4
The Specification of Regression Models......Page 16
Matrix Algebra......Page 23
Method of Moments Estimation......Page 31
Notes on the Exercises......Page 37
Exercises......Page 38
Introduction......Page 42
The Geometry of Vector Spaces......Page 43
The Geometry of OLS Estimation......Page 54
The Frisch-kern -.08333emWaugh-Lovell Theorem......Page 63
Applications of the FWL Theorem......Page 69
Influential Observations and Leverage......Page 76
Final Remarks......Page 81
Exercises......Page 82
Introduction......Page 86
Are OLS Parameter Estimators Unbiased?......Page 88
Are OLS Parameter Estimators Consistent?......Page 92
The Covariance Matrix of the OLS Parameter Estimates......Page 97
Efficiency of the OLS Estimator......Page 104
Residuals and Error Terms......Page 107
Misspecification of Linear Regression Models......Page 111
Measures of Goodness of Fit......Page 115
Exercises......Page 118
Basic Ideas......Page 122
Some Common Distributions......Page 129
Exact Tests in the Classical Normal Linear Model......Page 138
Largekern .08333em-Sample Tests in Linear Regression Models......Page 146
Simulation-Based Tests......Page 155
The Power of Hypothesis Tests......Page 166
Exercises......Page 172
Introduction......Page 176
Exact and Asymptotic Confidence Intervals......Page 177
Bootstrap Confidence Intervals......Page 184
Confidence Regions......Page 188
Heteroskedasticity-Consistent Covariance Matrices......Page 195
The Delta Method......Page 201
Exercises......Page 208
Introduction......Page 211
Method of Moments Estimators for Nonlinear Models......Page 213
Nonlinear Least Squares......Page 222
Computing NLS Estimates......Page 226
The Gausskern .08333em-Newton Regression......Page 233
Onekern .08333em-Step Estimation......Page 238
Hypothesis Testing......Page 241
Heteroskedasticity-Robust Tests......Page 248
Final Remarks......Page 250
Exercises......Page 251
Introduction......Page 255
The GLS Estimator......Page 256
Computing GLS Estimates......Page 258
Feasible Generalized Least Squares......Page 262
Heteroskedasticity......Page 264
Autoregressive and Moving Average Processes......Page 268
Testing for Serial Correlation......Page 273
Estimating Models with Autoregressive Errors......Page 282
Specification Testing and Serial Correlation......Page 290
Models for Panel Data......Page 296
Final Remarks......Page 303
Exercises......Page 304
Introduction......Page 309
Correlation Between Error Terms and Regressors......Page 310
Instrumental Variables Estimation......Page 313
Finitekern .04166em-Sample Properties of IV Estimators......Page 322
Hypothesis Testing......Page 328
Testing Overidentifying Restrictions......Page 334
Durbin-{kern -.10em}Wu-Hausman Tests......Page 336
Bootstrap Tests......Page 340
IV Estimation of Nonlinear Models......Page 343
Exercises......Page 345
Introduction......Page 350
GMM Estimators for Linear Regression Models......Page 351
HAC Covariance Matrix Estimation......Page 360
Tests Based on the GMM Criterion Function......Page 363
GMM Estimators for Nonlinear Models......Page 367
The Method of Simulated Moments......Page 381
Final Remarks......Page 391
Exercises......Page 392
Basic Concepts of Maximum Likelihood Estimation......Page 397
Asymptotic Properties of ML Estimators......Page 406
The Covariance Matrix of the ML Estimator......Page 413
Hypothesis Testing......Page 418
The Asymptotic Theory of the Three Classical Tests......Page 427
ML Estimation of Models with Autoregressive Errors......Page 431
Transformations of the Dependent Variable......Page 433
Final Remarks......Page 439
Exercises......Page 440
Introduction......Page 447
Binary Response Models: Estimation......Page 448
Binary Response Models: Inference......Page 456
Models for More than Two Discrete Responses......Page 462
Models for Count Data......Page 471
Models for Censored and Truncated Data......Page 477
Sample Selectivity......Page 482
Duration Models......Page 485
Exercises......Page 491
Seemingly Unrelated Linear Regressions......Page 497
Systems of Nonlinear Regressions......Page 514
Linear Simultaneous Equations Models......Page 518
Maximum Likelihood Estimation......Page 528
Nonlinear Simultaneous Equations Models......Page 536
Final Remarks......Page 539
Appendix: Detailed Results on FIML and LIML......Page 540
Exercises......Page 546
Introduction......Page 552
Autoregressive and Moving Average Processes......Page 553
Estimating AR, MA, and ARMA Models......Page 561
Singlekern .04166em-Equation Dynamic Models......Page 570
Seasonality......Page 575
Autoregressive Conditional Heteroskedasticity......Page 582
Vector Autoregressions......Page 590
Final Remarks......Page 594
Exercises......Page 595
Random Walks and Unit Roots......Page 600
Unit Root Tests......Page 608
Serial Correlation and Unit Root Tests......Page 615
Cointegration......Page 619
Testing for Cointegration......Page 631
Exercises......Page 639
Introduction......Page 645
Specification Tests Based on Artificial Regressions......Page 646
Nonnested Hypothesis Tests......Page 660
Model Selection Based on Information Criteria......Page 670
Nonparametric Estimation......Page 672
Final Remarks......Page 684
Appendix: Test Regressors in Artificial Regressions......Page 685
Exercises......Page 687
references......Page 694
