Time Series Analysis
Опубликовано на портале: 02-03-2004
Princeton: Princeton University Press, 2002
The last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.
The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.
A Mathematical Review
B Statistical Tables
C Answers to Selected Exercises
D Greek Letters and Mathematical Symbols Used in the Text
asymptotic distribution theory bayesian analysis cointegration difference equations generalized method of moments heteroskedasticity kalman filter nonstationary time series spectral analysis unit root vector autoregression
Journal of Agricultural and Applied Economics. 2001. Vol. 33. No. 3.
Economics Letters. 1998. Vol. 58. No. 1. P. 17-29.
Essays in Econometrics Volume 1 , Spectral Analysis, Seasonality, Nonlinearity, Methodology, and Forecasting