Russell Davidson and James G. MacKinnon

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

There are no Instructor/Student Resources available at this time.

RUSSELL DAVIDSON holds the Canada Research Chair in Econometrics at McGill University in Montreal. He also teaches at GREQAM in Marseille and previously taught for many years at Queen's University. He has a Ph.D. in Physics from the University of Glasgow and a Ph.D. in Economics from the
University of British Columbia. Professor Davidson is a Fellow of the Econometric Society and the author of many scientific papers. He is the coauthor of *Estimation and Inference in Econometrics* (OUP, 1993).

JAMES G. MACKINNON is the Sir Edward Peacock Professor of Econometrics and Head
of the Department at Queen's University in Kingston, Ontario, Canada, where he has taught since obtaining his Ph.D. from Princeton University in 1975. He is a Fellow of the Econometric Society and of the Royal Society of Canada and a past President of the Canadian Economics Association (2001-2002).
Professor MacKinnon has written more than seventy journal articles and book chapters, and he is the coauthor of *Estimation and Inference in Econometrics* (OUP, 1993).

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