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.
The material covered in the book includes concepts of linear regression, univariate and multivariate time series modelling and their implementation in EViews. Chapter 1 briefly introduces commands, structure and programming language of the EViews package. Chapter 2 provides an overview of the regression analysis and its inference. Chapters 3 to 5 cover some topics of univariate time series analysis including linear models, GARCH models of volatility, unit root tests. Chapter 6 introduces modelling of multivariate time series.
This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application —forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
This is a graduate level work covering the economic principles of security markets. Interested readers include students and researchers in economics and finance, as well as financial analysts following the latest theoretical developments in capital asset pricing.
Elements of Financial Time Series fills a gap in the market in the area of financial time series analysis by giving both conceptual and practical illustrations. Examples and discussions in the later chapters of the book make recent developments in time series more accessible. Examples from finance are maximized as much as possible throughout the book.
