Econometric Modelling with Time Series Specification, Estimation and Testing

Description: Econometric Modelling with Time Series Specification, Estimation and Testing This book provides a general framework for specifying, estimating and testing time series econometric models. Vance Martin (Author), Stan Hurn (Author), David Harris (Author) 9780521196604, Cambridge University Press Hardback, published 28 December 2012 924 pages 22.9 x 15.2 x 4.8 cm, 1.39 kg 'This textbook strikes an excellent balance between explaining the underlying concepts and intuition, containing the requisite amount of rigor, and providing sufficient guidance for students to be able to apply the methods described to a variety of time-series situations. It is extremely clearly written and should instantly find a wide audience. The book's emphasis on maximum-likelihood as a unifying guiding principle is well-justified, and provides the right context for students to understand how seemingly disparate econometric methods are fundamentally related.' Yacine Ait-Sahalia, Princeton University 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, generalised 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. 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-Stationary 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. Subject Areas: Computer science [UY], Economic statistics [KCHS], Econometrics [KCH], Social research & statistics [JHBC]

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