This is a very good introductory econometrics textbook for the mathematically well-prepared. No prior knowledge of econometrics or statistics is assumed, and the discussion of the necessary probability and statistics concepts is integrated into the main text rather than being relegated to appendices. All you need to read this book is a good knowledge of linear algebra and calculus. Once you finish it you will have a firm grasp of the basic methods and models used by econometricians and be prepared for going to more advanced sources like Wooldridge's Econometric Analysis of Cross Section and Panel Data or Hamilton's Time Series AnalysisThroughout the book Davidson and MacKinnon focus on developing intuition rather than on mechanical calculation. In particular, their geometric approach to ordinary least squares estimation is a must read. By focussing on the geometry and making clever use of the Frisch-Waugh-Lovell theorem, they make the properties of OLS very intuitive. Many of the standard results usually proved by opaque matrix algebra in other books, become clear and easy to prove in this framework.The book also has the advantage of covering topics like GMM estimation, the bootstrap and numerical methods that cannot be found in older textbooks.Yet, I have three quibbles with this book.The first, minor one, is that its treatment of time series methods is too short, and unlike the rest of the book tries to trade off depth for breadth. The second, bigger problem with this book is that it is entirely about econometric 'theory'. It teaches you how to find estimators and test statistics with good properties for particular models. But it does not train the student at all in the applied/methodological aspects of econometrics: given that I have a vague question about economic phenomena in mind, and given a bunch of data, how do I proceed? What questions can be meaningfully asked, how to choose between alternative models, how to present and interpret results, are questions that are given a short shrift in this book. Even data-based exercises are few and seem to have been reluctantly included.The third problem with this book is that it completely ignores the Bayesian approach to econometrics. Though this is in line with the general frequentist dominance of the econometrics profession, I feel that without at least an introduction to the Bayesian approach, the training of an econometrician will remain one-sided.The first two shortcomings of this book can be addressed by complementing it with Hayashi's Econometrics. Many interesting papers on methodology can be found in the book Modelling Economic Series edited by Granger.
Author(s): Russell Davidson, James G. MacKinnon
Edition: illustrated edition
Publisher: Oxford University Press, USA
Year: 2003
Language: English
Pages: 692
1.1 Introduction......Page 1
1.2 Distributions, Densities, and Moments......Page 3
1.3 The Specification of Regression Models......Page 15
1.4 Matrix Algebra......Page 22
1.5 Method of Moments Estimation......Page 30
1.6 Notes on the Exercises......Page 36
1.7 Exercises......Page 37
2.1 Introduction......Page 41
2.2 The Geometry of Vector Spaces......Page 42
2.3 The Geometry of OLS Estimation......Page 53
2.4 The Frisch- Waugh- Lovell Theorem......Page 62
2.5 Applications of the FWL Theorem......Page 68
2.6 Influential Observations and Leverage......Page 75
2.7 Final Remarks......Page 80
2.8 Exercises......Page 81
3.1 Introduction......Page 85
3.2 Are OLS Parameter Estimators Unbiased?......Page 87
3.3 Are OLS Parameter Estimators Consistent?......Page 91
3.4 The Covariance Matrix of the OLS Parameter Estimates......Page 96
3.5 Efficiency of the OLS Estimator......Page 103
3.6 Residuals and Error Terms......Page 106
3.7 Misspecification of Linear Regression Models......Page 110
3.8 Measures of Goodness of Fit......Page 114
3.10 Exercises......Page 117
4.2 Basic Ideas......Page 121
4.3 Some Common Distributions......Page 128
4.4 Exact Tests in the Classical Normal Linear Model......Page 137
4.5 Large- Sample Tests in Linear Regression Models......Page 145
4.6 Simulation- Based Tests......Page 154
4.7 The Power of Hypothesis Tests......Page 165
4.9 Exercises......Page 171
5.1 Introduction......Page 175
5.2 Exact and Asymptotic Confidence Intervals......Page 176
5.3 Bootstrap Confidence Intervals......Page 183
5.4 Confidence Regions......Page 187
5.5 Heteroskedasticity- Consistent Covariance Matrices......Page 194
5.6 The Delta Method......Page 200
5.8 Exercises......Page 207
6.1 Introduction......Page 210
6.2 Method of Moments Estimators for Nonlinear Models......Page 212
6.3 Nonlinear Least Squares......Page 221
6.4 Computing NLS Estimates......Page 225
6.5 The Gauss- Newton Regression......Page 232
6.6 One- Step Estimation......Page 237
6.7 Hypothesis Testing......Page 240
6.8 Heteroskedasticity- Robust Tests......Page 247
6.9 Final Remarks......Page 249
6.10 Exercises......Page 250
7.1 Introduction......Page 254
7.2 The GLS Estimator......Page 255
7.3 Computing GLS Estimates......Page 257
7.4 Feasible Generalized Least Squares......Page 261
7.5 Heteroskedasticity......Page 263
7.6 Autoregressive and Moving Average Processes......Page 267
7.7 Testing for Serial Correlation......Page 272
7.8 Estimating Models with Autoregressive Errors......Page 281
7.9 Specification Testing and Serial Correlation......Page 289
7.10 Models for Panel Data......Page 295
7.11 Final Remarks......Page 302
7.12 Exercises......Page 303
8.1 Introduction......Page 308
8.2 Correlation Between Error Terms and Regressors......Page 309
8.3 Instrumental Variables Estimation......Page 312
8.4 Finite- Sample Properties of IV Estimators......Page 321
8.5 Hypothesis Testing......Page 327
8.6 Testing Overidentifying Restrictions......Page 333
8.7 Durbin- Wu- Hausman Tests......Page 335
8.8 Bootstrap Tests......Page 339
8.9 IV Estimation of Nonlinear Models......Page 342
8.11 Exercises......Page 344
9.1 Introduction......Page 349
9.2 GMM Estimators for Linear Regression Models......Page 350
9.3 HAC Covariance Matrix Estimation......Page 359
9.4 Tests Based on the GMM Criterion Function......Page 362
9.5 GMM Estimators for Nonlinear Models......Page 366
9.6 The Method of Simulated Moments......Page 380
9.7 Final Remarks......Page 390
9.8 Exercises......Page 391
10.2 Basic Concepts of Maximum Likelihood Estimation......Page 396
10.3 Asymptotic Properties of ML Estimators......Page 405
10.4 The Covariance Matrix of the ML Estimator......Page 412
10.5 Hypothesis Testing......Page 417
10.6 The Asymptotic Theory of the Three Classical Tests......Page 426
10.7 ML Estimation of Models with Autoregressive Errors......Page 430
10.8 Transformations of the Dependent Variable......Page 432
10.9 Final Remarks......Page 438
10.10 Exercises......Page 439
11.1 Introduction......Page 446
11.2 Binary Response Models: Estimation......Page 447
11.3 Binary Response Models: Inference......Page 455
11.4 Models for More than Two Discrete Responses......Page 461
11.5 Models for Count Data......Page 470
11.6 Models for Censored and Truncated Data......Page 476
11.7 Sample Selectivity......Page 481
11.8 Duration Models......Page 484
11.10 Exercises......Page 490
12.2 Seemingly Unrelated Linear Regressions......Page 496
12.3 Systems of Nonlinear Regressions......Page 513
12.4 Linear Simultaneous Equations Models......Page 517
12.5 Maximum Likelihood Estimation......Page 527
12.6 Nonlinear Simultaneous Equations Models......Page 535
12.7 Final Remarks......Page 538
12.8 Appendix: Detailed Results on FIML and LIML......Page 539
12.9 Exercises......Page 545
13.1 Introduction......Page 551
13.2 Autoregressive and Moving Average Processes......Page 552
13.3 Estimating AR, MA, and ARMA Models......Page 560
13.4 Single- Equation Dynamic Models......Page 569
13.5 Seasonality......Page 574
13.6 Autoregressive Conditional Heteroskedasticity......Page 581
13.7 Vector Autoregressions......Page 589
13.8 Final Remarks......Page 593
13.9 Exercises......Page 594
14.2 Random Walks and Unit Roots......Page 599
14.3 Unit Root Tests......Page 607
14.4 Serial Correlation and Unit Root Tests......Page 614
14.5 Cointegration......Page 618
14.6 Testing for Cointegration......Page 630
14.8 Exercises......Page 638
15.1 Introduction......Page 644
15.2 Speci cation Tests Based on Arti cial Regressions......Page 645
15.3 Nonnested Hypothesis Tests......Page 659
15.4 Model Selection Based on Information Criteria......Page 669
15.5 Nonparametric Estimation......Page 671
15.6 Final Remarks......Page 683
15.7 Appendix: Test Regressors in Arti cial Regressions......Page 684
15.8 Exercises......Page 686
