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.
(NOTE: I have read the topic re "books for self-studying time series analysis," this question is intended to be different in a very specific way, and I am looking for answers that would not be relevant to that topic's OP).
Many books on the subject fall into two categories: classic texts with the basic theories and fundamentals of time series analysis, and revised editions of academic textbooks with real-world examples and exercises. We picked an array that covers the initial introduction to references and guides along with your time series analysis self-study.
This book is a basic introduction to time series and the open-source software R, and is intended for readers who have little to no R knowledge. It gives step-by-step instructions for getting started with time series analysis and how to use R to make it all happen. Each module features practical applications and data to test the analysis. The co-author Paul Cowpertwait also features the data sets on a companion website.
The book gives a good overview of time series analysis without being overwhelming. It covers the basics, including methods, forecasting models, systems, and ARIMA probability models that include studying seasonality. It also includes examples and practical advice and comes with a free online appendix.
Time series analysis is a complex subject, and even these books barely scratch the surface of its uses and evolution. In order to utilize the analysis to its fullest, you have to stay current with new trends and theories, as well as continue to deepen your understanding. To learn more about theories and read real customer stories, check out our time series analysis resources page.
Many economic time series occasionally exhibit dramatic breaks in their behaviour, associated with events such as financial crises (Jeanne and Masson, 2000; Cerra and Saxena, 2005; Hamilton, 2005) or abrupt changes in government policy (Hamilton, 1988; Sims and Zha, 2006; Davig, 2004). Of particular interest to economists is the apparent tendency of many economic variables to behave quite differently during economic downturns, when underutilization of factors of production rather than their long-run tendency to grow governs economic dynamics (Hamilton, 1989; Chauvet and Hamilton, 2006). Abrupt changes are also a prevalent feature of financial data, and the approach described below is quite amenable to theoretical calculations for how such abrupt changes in fundamentals should show up in asset prices (Ang and Bekaert, 2002a; 2000b; Garcia, Luger and Renault, 2003; Dai, Singleton and Yang, 2003).
Time series plots for about 1 hour showing heart rate (HR), respiratory rate (RR) and pulse oximetry (SpO2). The shaded portion represents incident cardiorespiratory instability (CRI) which this patient developed due to crossing of HR threshold limits, with changes in RR and SpO2 occurring prior to CRI. Vector autoregressive (VAR) models are able to look at these evolving patterns over time for this specific patient to assess VS changes prior to incident CRI.
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.