This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation.
This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.
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-Stationary 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.
'This book will be an excellent text for advanced undergraduate and postgraduate courses in econometric time series. The statistical theory is clearly presented and the many examples make the techniques readily accessible and illustrate their practical importance.' Andrew Harvey, University of Cambridge
Martin, Vance
Vance Martin is Professor of Econometrics at the University of Melbourne, Australia, a position he has held since 2000. He graduated with a PhD from Monash University in 1990. He was appointed Lecturer at the University of Melbourne in 1985 and became a Senior Lecturer in 1990.nnHurn, Stan
Stan Hurn is Professor of Economics and Finance at Queensland University of Technology, Australia, a position he has held since 1998. He graduated with a DPhil in Economics from St Edmund Hall, Oxford, in 1992. He was appointed Lecturer at the University of Glasgow in 1988 and became a Senior Lecturer in 1993 before being named Official Fellow in Economics at Brasenose College, Oxford, in 1996.nnHarris, David
David Harris is Professor of Econometrics at Monash University, Australia. He was awarded his PhD in Econometrics from Monash University in 1995. He was lecturer in econometrics from 1995 to 1997 at Monash University and from 1998 to 2010 at the University of Melbourne.
Über den Autor
Vance Martin is Professor of Econometrics at the University of Melbourne, Australia, a position he has held since 2000. He graduated with a PhD from Monash University in 1990. He was appointed Lecturer at the University of Melbourne in 1985 and became a Senior Lecturer in 1990.
Inhaltsverzeichnis
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-Stationary 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.
Klappentext
This book provides a general framework for specifying, estimating and testing time series econometric models.
