Based on lectures originally given to graduates at the London School of Economics, the book applies recent developments in asymptotic theory to derive the properties of estimators when the model is only partially specified. Topics covered in depth include the linear regression model, dynamic modeling, simultaneous equations, optimization estimators, hypothesis testing, and the theory of nonstationary time series and cointegration.
Symbols and Abbreviations.
Part I: Basic Regression Theory.
1. The Linear Regression Model.
2. Statistical Analysis of the Regression Model.
3. Asymptotic Analysis of the Regression Model.
Part II: Dynamic Regression Theory.
4. Modelling Economic Time Series.
5. Principles of Dynamic Modelling.
6. Asymptotics for Dynamic Models.
7. Estimation and Testing.
8. Simultaneous Equations.
Part III: Advanced Estimation Theory.
9. Optimization Estimators I: Theory.
10. Optimization Estimators II: Examples.
11. The Method of Maximum Likelihood.
12. Testing Hypotheses.
13. System Estimation.
Part IV: Cointegration Theory.
14. Unit Roots.
15. Cointegrating Regression.
16. Cointegrated Systems.
Part V: Technical Appendices.
A. Matrix Algebra Basics.
B. Probability and Distribution Theory.
C. The Gaussian Distribution and Its Relatives.