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Econometric Theory


Econometric Theory presents a modern approach to the theory of econometric estimation and inference, with particular applications to time series. An ideal reference for practitioners and researchers, the book is also suited for advanced two-semester econometrics courses and one-semester regression courses.

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.

James Davidson is Professor of Econometrics at Cardiff University. Contributor and referee for a number of leading research journals, Davidson is the author of Stochastic Limit Theory (1994). With an MSc in Econometrics and Mathematical Economics from the London School of Economics, he has taught at the University of Warwick, the London School of Economics, the University of California-San Diego, and the University of Wales, Aberystwyth.

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.


Author Index.

Subject Index.