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 provides a general framework for specifying, estimating and testing time series econometric models.
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. 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. 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.
'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
'This book takes an important step forward relative to existing
time-series econometrics texts, with, for example, significant
coverage of numerical optimization, quasi-maximum-likelihood
estimation, nonparametric and simulation-based estimation,
latent-factor models, and volatility models. In addition, readers
will benefit immensely from the complete sets of included R and
Matlab routines. Well done!' Francis X. Diebold, University of
Pennsylvania
'This book is exceptionally well done. The blending of theory,
application and computation is sublimely done throughout. [It] will
be a must-have for advanced graduate students working with economic
and financial time series data, and will also form a definitive and
up-to-date reference source for both academic and academic-related
researchers in the field.' Robert Taylor, University of
Nottingham
'This book gave me excitement and sensations similar to visiting
Australian wineries: tantalizing vitality, pronounced yet balanced
flavours, exposing exhilarating progressive developments, produced
by excellent and tasteful craftsmanship, and well-matured and
extremely consumer-friendly with its many recipes in various
computer codes, thus it is strongly recommended to both young
graduates and experienced connoisseurs.' Jan F. Kiviet, Nanyang
Technological University and University of Amsterdam
'This textbook strikes an excellent balance between explaining the
underlying concepts and intuition, containing the requisite amount
of rigor, and providing sufficient guidance for students to be able
to apply the methods described to a variety of time-series
situations. It is extremely clearly written and should instantly
find a wide audience. The book's emphasis on maximum-likelihood as
a unifying guiding principle is well-justified, and provides the
right context for students to understand how seemingly disparate
econometric methods are fundamentally related.' Yacine Ait-Sahalia,
Princeton University
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