This book surveys recent developments in the rapidly expanding field of asymptotic distribution theory, placing special emphasis on the problems of time-dependence and heterogeneity. It is technically self-contained, with all but the most basic mathematical prerequisites being explained in their context.
Table of Contents:
Figures xv
Symbols and Abbreviations xvi
Preface xx
Part I: Basic Regression Theory 1
1. The Linear Regression Model 3
2. Statistical Analysis of the Regression Model 17
3. Asymptotic Analysis of the Regression Model 37
Part II: Dynamic Regression Theory 57
4. Modelling Economic Time Series 59
5. Principles of Dynamic Modelling 84
6. Asymptotics for Dynamic Models 119
7. Estimation and Testing 140
8. Simultaneous Equations 172
Part III: Advanced Estimation Theory 197
9. Optimization Estimators I: Theor 199
10. Optimization Estimators II: Examples 234
11. The Method of Maximum Likelihood 262
12. Testing Hypotheses 283
13. System Estimation 308
Part IV: Cointegration Theory 335
14. Unit Roots 337
15. Cointegrating Regression 360
16. Cointegrated Systems 388
Part V: Technical Appendices 427
A. Matrix Algebra Basics 429
B. Probability and Distribution Theory 441
C. The Gaussian Distribution and Its Relatives 461
References 469
Author Index 485
Subject Index 489
About the Author :
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.
Review :
"Davidson's book is a well-written introduction to the state of the art in econometric theory. It will be useful both as a text for advanced econometrics courses and as a reference source for econometricians. It provides a thorough treatment of the asymptotic analysis of the linear regression model, time series models, nonlinear optimization estimators, unit roots, and cointegration." Bruce E. Hansen, University of Wisconsin-Madison "The systematic use of the conditional expectation approach to modelling throughout the text will provide readers with many useful insights. It is a very good and thought-provoking book. Much can be learnt from it, even by 'experts.' Leonard Gill, University of Manchester
"The book is stong on linear dynamic modelling of time series and has an excellent coverage of recent developments in econometrics for non-stationery time series. Cointegration theory is given a comprehensive and clear treatment, including an exposition of the underlying probability background - stockastic processes on function spaces, Brownian motion and so on - which I found to enhance understanding considerably. This will be a useful book, particularly to those teaching advanced courses in time-series econometrics. Overall, it is a fine and well-written piece of work.
Times Higher Education Supplement
