Hamilton J.D. Time Series Analysis

Princeton University Press, 1994. — 815 p.Much of economics is concerned with modeling dynamics. There has been an explosion of research in this area in the last decade, as "time series econometrics" has practically come to be synonymous with "empirical macroeconomics." Several texts provide good coverage of the advances in the economic analysis of dynamic systems, while others summarize the earlier literature on statistical inference for time series data. There seemed a use for a text that could integrate the theoretical and empirical issues as well as incorporate the many advances of the last decade, such as the analysis of vector autoregressions, estimation by gen- generalized method of moments, and statistical inference for nonstationary data. This is the goal of Time Series Analysis.
A principal anticipated use of the book would be as a textbook for a graduate econometrics course in time series analysis. The book aims for maximum flexibility through what might be described as an integrated modular structure.
Although the book is designed with an econometrics course in time series methods in mind, the book should be useful for several other purposes. It is completely self-contained, starting from basic principles accessible to first-year graduate students and including an extensive math review appendix. Thus the book would be quite suitable for a first-year graduate course in macroeconomics or dynamic methods that has no econometric content.
Yet another intended use for the book would be in a conventional econometrics course without an explicit time series focus. The popular econometrics texts do not have much discussion of such topics as numerical methods; asymptotic results for serially dependent, heterogeneously distributed observations; estimation of models with distributed lags; autocorrelation- and heteroskedasticity-consistent standard errors; Bayesian analysis; or generalized method of moments. All of these topics receive extensive treatment in Time Series Analysis.
Finally, the book attempts to provide a rigorous motivation for the methods and yet still be accessible for researchers with purely applied interests. This is achieved by relegation of many details to mathematical appendixes at the ends of chapters, and by inclusion of numerous examples that illustrate exactly how the theoretical results are used and applied in practice.Difference Equations
Lag Operators
Stationary ARMA Processes
Forecasting 72
Maximum Likelihood Estimation
Spectral Analysis
Asymptotic Distribution Theory
Linear Regression Models
Linear Systems of Simultaneous Equations
Covariance-Stationary Vector Processes
Vector Autoregressions
Bayesian Analysis
The Kalman Filter
Generalized Method of Moments
Models of Nonstationary Time Series
Processes with Deterministic Time Trends
Univariate Processes with Unit Roots
Unit Roots in Multivariate Time Series
Cointegration
Full-Information Maximum Likelihood Analysis of Cointegrated Systems
Time Series Models of Heteroskedasticity
Modeling Time Series with Changes in Regime