Includes Classical, Bayesian, GMM, maximum likelihood, cross section, and more time series and panel data, covering applications and numerical examples. This book gives solutions to applications that show students how to do the computations. It gives attention to both linear and nonlinear techniques.

1. Introduction. 2. The Classical Multiple Linear Regression Model. 3. Least Squares. 4. Finite Sample Properties of the Least Squares Estimator. 5. Large Sample Properties of the Least Squares and Instrumental Variables Estimators. 6. Inference and Prediction. 7. Functional Form and Structural Change. 8. Specification Analysis and Model Selection. 9. Nonlinear Regression Models. 10. Nonspherical Disturbances-The Generalized Regression Model. 11. Heteroscedasticity. 12. Serial Correlation. 13. Models for Panel Data. 14. Systems of Regression Equations. 15. Simultaneous-Equations Models. 16. Estimation Frameworks in Econometrics. 17. Maximum Likelihood Estimation. 18. The Generalized Method of Moments. 19. Models with Lagged Variables. 20. Time-Series Models. 21. Models for Discrete Choice. 22. Limited Dependent Variable and Duration Models. Appendix A: Matrix Algebra. Appendix B: Probability and Distribution Theory. Appendix C: Estimation and Inference. Appendix D: Large Sample Distribution Theory. Appendix E: Computation and Optimization. Appendix F: Data Sets Used in Applications. Appendix G. Statistical Tables. References. Author Index. Subject Index.