Time Series Analysis

Front Cover
Princeton University Press, 1994 - Business & Economics - 799 pages
12 Reviews
The last decade has brought dramatic changes in the way that researchers analyze time series data. This much-needed book synthesizes all of the major recent advances and develops a single, coherent presentation of the current state of the art of this increasingly important field. James Hamilton provides for the first time a thorough and detailed textbook account of important innovations such as vector autoregressions, estimation by generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, Hamilton presents traditional tools for analyzing dynamic systems, including linear representations, autocovariance, generating functions, spectral analysis, and the Kalman filter, illustrating their usefulness both for economic theory and for studying and interpreting real-world data. This book is intended to provide students, researchers, and forecasters with a definitive, self-contained survey of dynamic systems, econometrics, and time series analysis. Starting from first principles, Hamilton's lucid presentation makes both old and new developments accessible to first-year graduate students and nonspecialists. Moreover, the work's thoroughness and depth of coverage will make Time Series Analysis an invaluable reference for researchers at the frontiers of the field. Hamilton achieves these dual objectives by including numerous examples that illustrate exactly how the theoretical results are used and applied in practice, while relegating many details to mathematical appendixes at the end of chapters. As an intellectual roadmap of the field for students and researchers alike, this volume promises to bethe authoritative guide for years to come.

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Review: Time Series Analysis

User Review  - Joe Malicki - Goodreads

From simple ARIMA, spectral analysis for seasonality, cointegration, vector autoregression, Kalman filters, etc., this is the first stop to understand how to do time series analysis. Assumes familiarity with linear algebra and math stats. Read full review

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