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For graduate-level courses in Introduction to Econometrics. A standard text/reference in courses that include basic techniques in regression analysis and extensions used when linear models prove inadequate or inappropriate. Areas of application include Economics, Sociology, Political Science, Medical Research, Transport Research, and Environmental Economics. This book introduces students to the broad field of applied econometrics. An effective bridge to both on-the-job problems and to the professional literature, it features extensive applications and presents sufficient theoretical background to enable students to recognize new variants of the models that they learn about here as merely natural extensions that fit within a common body of principles.
Table of Contents
1. Introduction. 2. Matrix Algebra. 3. Probability and Distribution Theory. 4. Statistical Inference. 5. Computation and Optimization. 6. The Classical Multiple Linear Regression Model: Specification and Estimation. 7. Inference and Prediction. 8. Functional Form, Nonlinearity, and Specification. 9. Large-Sample Results and Alternative Estimators for the Classical Regression Model. 10. Nonlinear Regression Models. 11. Nonspherical Disturbances, Generalized Regression, and GMM Estimation. 12. Heteroscedasticity. 13. Autocorrelated Disturbances. 14. Models for Panel Data. 15. Systems of Regression Equations. 16. Simultaneous Equations Models. 17. Regressions with Lagged Variables. 18. Time-Series Models. 19. Models with Discrete Dependent Variables. 20. Limited Dependent Variable and Duration Models. Appendix A. Data Sets Used in Applications. Appendix B. Statistical Tables. References. Author Index. Subject Index.