"This book provides a general framework for specifying, estimating, and testing time series econometric models"--

"Maximum likelihood estimation is a general method for estimating the parameters of econometric models from observed data. The principle of maximum likelihood plays a central role in the exposition of this book, since a number of estimators used in econometrics can be derived within this framework. Examples include ordinary least squares, generalized least squares and full-information maximum likelihood. In deriving the maximum likelihood estimator, a key concept is the joint probability density function(pdf) of the observed random variables, yt. Maximum likelihood estimation requires that the following conditions are satisfied. (1) The form of the joint pdf of yt is known. (2) The specification of the moments of the joint pdf are known. (3) The joint pdf can be evaluated for all values of the parameters, 9. Parts ONE and TWO of this book deal with models in which all these conditions are satisfied. Part THREE investigates models in which these conditions are not satisfied and considers four important cases. First, if the distribution of yt is misspecified, resulting in both conditions 1 and 2 being violated, estimation is by quasi-maximum likelihood (Chapter 9). Second, if condition 1 is not satisfied, a generalized method of moments estimator (Chapter10) is required. Third, if condition 2 is not satisfied, estimation relies on nonparametric methods (Chapter 11). Fourth, if condition 3 is violated, simulation-based estimation methods are used (Chapter 12). 1.2 Motivating Examples To highlight the roleof probability distributions in maximum likelihood estimation, this section emphasizes the link between observed sample data and 4 The Maximum Likelihood Principle the probability distribution from which they are drawn"--

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.