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.
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