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This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.
| ISBN | 0521196604 | | DEWEY | 330.015195 | | ISBN13 | 9780521196604
(What's this?) | | DEWEY edition | DC23 | | Publisher | Cambridge University Press | | Pages | 960 | | Imprint | Cambridge University Press | | Published in | Cambridge | | Format | Hardback | | Series title | Themes in Modern Econometrics | | Publication date | 31 Jul 2012 | | Academic level | Tertiary education |
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Part I. Maximum Likelihood: 1. The maximum likelihood principle; 2. Properties of maximum likelihood estimators; 3. Numerical estimation methods; 4. Hypothesis testing; Part II. Regression Models: 5. Linear regression models; 6. Nonlinear regression models; 7. Autocorrelated regression models; 8. Heteroskedastic regression models; Part III. Other Estimation Methods: 9. Quasi-maximum likelihood estimation; 10. Generalized method of moments; 11. Nonparametric estimation; 12. Estimation by stimulation; Part IV. Stationary Time Series: 13. Linear time series models; 14. Structural vector autoregressions; 15. Latent factor models; Part V. Non-Station Time Series: 16. Nonstationary distribution theory; 17. Unit root testing; 18. Cointegration; Part VI. Nonlinear Time Series: 19. Nonlinearities in mean; 20. Nonlinearities in variance; 21. Discrete time series models; Appendix A. Change in variable in probability density functions; Appendix B. The lag operator; Appendix C. FIML estimation of a structural model; Appendix D. Additional nonparametric results.
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