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.