Chapter 1. Introduction
Part 1. Sampling Theory and Bayesian Approaches to Inference
Chapter 2. The Classical Inference Approach for the General Linear Model
Chapter 3. Statistical Decision Theory and Biased Estimation
Chapter 4. The Bayesian Approach to Inference
Part 2. Inference in General Statistical Models and Time Series
Chapter 5. Some Asymptotic Theory and Other General Results for the Linear Statistical Model
Chapter 6. Nonlinear Statistical Models
Chapter 7. Time Series
Part 3. Dynamic Specifications
Chapter 8. Autocorrelation
Chapter 9. Finite Distributed Lags
Chapter 10. Infinite Distributed Lags
Part 4. Some Alternative Covariance Structures
Chapter 11. Heteroscedasticity
Chapter 12. Disturbance-related Sets of Regression Equations
Chapter 13. Inference in Models That Combine Time Series and Cross-sectional Data
Part 5. Inference in Simultaneous Equation Models
Chapter 14. Specification and Identification in Simultaneous Equation Models
Chapter 15. Estimation and Inference in a System of Simultaneous Equations
Chapter 16. Multiple Time Series and Systems of Dynamic Simultaneous Equations
Part 6. Further Model Extensions
Chapter 17. Unobservable Variables
Chapter 18. Qualitative and Limited Dependent Variable Models
Chapter 19. Varying and Random Coefficient Models
Chapter 20. Nonnormal Disturbances
Chapter 21. On Selecting the Set of Regressors
Chapter 22. Multicollinearity
Appendixes
Author Index
Subject Index
Chapter 14.
