Time Series Analysis

by James Douglas Hamilton

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.-- "Journal of Economics"

Genres: Economics, Textbooks, Finance, Mathematics, Reference, Technical, Science

816 pages, Hardcover

First published January 11, 1994

Reviews

[Joe Malicki · Rating 5 out of 5]

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.

[Amir]

A good combination of mathematical theorems and econometric methods and interpretations.

[Alex Lee · Rating 4 out of 5]

This is a pretty impressive book, although about a third through I got lost in all the math. Hamilton offers various models with different operant relationships available for different situations with very minimal explanations or background as to how to understand these relationships.

I would like to return to this book later on, after some more exposure to time series, with more handholding as to how to frame understanding states within an iterative process of change.

[Alice Zhu · Rating 2 out of 5]

Too outdated and not even covering multivariate timeseries.