James D. Hamilton Time Series Analysis

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Novella Poinsett

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Aug 4, 2024, 9:21:44 PM8/4/24

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Iam a masters student studying economics. The program that I am attending is extremely quantitative with a heavy focus on econometrics. I am looking for a text on time series analysis. I really want something applied but rigorous enough to be considered for an introductory PhD course. I want a text that will be useful in the long run...a reference that I will continue to use later in my career as either an economic consultant or PhD student. Thanks for the help.

There's one more text: Greene, Econometric Analysis, 7th Edition. It's a standard text in econometrics. It has quite a bit of theory for time series too. One advantage of this text is that all examples are from economics, and that time series chapters can be seen in the context of the body of knowledge of econometrics together with cross-sectional and panel analysis. It can be beneficial in some cases to look at time series this way. It also has many examples, but they're not code examples. If you're specifically interested in time-series, I would not pick this text.

Using the same convention as in Hastie, Friedman, and Tibshirani (2001), the symbol ? indicates a technically difficult section which may be skipped without interrupting the flow of the discussion.

This is not the first (or the last) book that has been written on time series analysis. Indeed, this can be seen as a book that brings together and reorganizes information and material from other sources structuring and tailoring it to a course in basic time series analysis. The main and excellent references (which are far from being an exhaustive review of literature) that can be used to have a more in-depth view of different aspects treated in this book are Cochrane (2005), Hamilton (1994) and Shumway and Stoffer (2010).

The authors are particularly grateful to James Balamuta who introduced them to the use of the different tools provided by the RStudio environment and greatly contributed to an earlier version of this book:

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.

"I am extremely enthusiastic about this book. I think it will quickly become a classic. Like Sargent's and Varian's texts, it will be a centerpiece of the core cirriculum for graduate students."--John H. Cochrane, University of Chicago

I recommend reading Applied Time Series for the Social Sciences, by Richard McClearly and Richard Hay. It is a great introduction to the field and goes into depth about various time series analysis concepts (e.g., ARIMA models, non-linear estimation methods, etc.)

Zivot & Wang "Modeling Financial Time Series with S-PLUS" (2006) is my favourite. The authors are able to present rather advanced material in an accessible manner. The good news is, the textbook is freely available from the author's website (follow the link above).

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Time series analysis is used to analyze data that are organized chronologically. Much of our data in international relations have a time series element: events data of international interactions, data on economic flows between states, historical data on international conflict and cooperation between states, as well as newer forms of social media data. As practitioners of quantitative methods are well aware: to ignore the time-series element in data is to invite bad statistical inferences. Yet, treating time series properties as merely a nuisance misses an important modeling opportunity, namely to substantively interpret the nature of the time series element and use that to assist in the diagnosis of the data-generating process. This article samples important works using different techniques in the family of time series methods. We begin with univariate and diagnostic approaches commonly referred to as Box-Jenkins modeling. We continue with univariate intervention models before moving to multivariate vector autoregressive and error-correction models. While the later types of models remain common in international relations, the former are now less commonly used. The penultimate section deals with a host of time-series regression models. Many of these choose to treat the time series element of the data as a nuisance to be controlled for rather than explicitly modeled. A large increase in the types of approaches to time series regression (especially combined with cross-sectional data) has been registered, and here we provide a number of international relations examples. Our final section examines newer developments in time series and some of their applications to international relations questions. A final note: while most of the entries are applications of time series techniques applied to questions of international relations, a few important papers containing key methodological innovations are included as well.

Overviews of time series modeling in the social sciences are numerous. Box-Steffensmeier, et al. 2014 is a recent example heavy on political science and international relations examples. Pickup 2014 is a time series guide for a non-econometrics audience. Enders 2014 is a mid-level economics treatment of the approach. Hamilton 1994 is an advanced economics treatment. McCleary, et al. 2017 is an updated version of the classic McCleary and Hay 1980 that constitutes an early touchstone text for univariate models in international relations. Brandt and Williams 2007 is a shorter treatment focusing on multiple equation models with many political science and international relations examples.

List Price $105.00, Estima's Price $80.00It's not an accident that Hamilton's book is referenced extensively in the RATS manual, and in many journal articles published since its release in 1994. This is a detailed, in-depth treatment of modern time series analysis and econometrics that can serve both as a textbook for the student and an advanced reference for practicing researchers. Just under 800 pages, hardbound.

In the Spring of 2007, we will use the book Time Series Analysis and Its Applications (with R Examples) by Schumway and Stoffer. We will go through much of the book quickly, but some parts we will examing very carefully. The use of this text should give us a very efficient path through the basics of time series analysis. We'll need to go quickly so that we can spend a substantial amount of time on more specialized issues of modeling finacial time series.

I have tried to avoid giving a dry list of books that one can find in any relevant bibliography. To be sure, I include a few of the "usual suspects," but as the list grows I hope to give increasing attention to resources that offer more novel perspectives.

This is perhaps the most widely required texts for time series courses at the level of our course. It does not focus specifically on financial series, but it provides one will a good general basis. It strikes a sensible balance between theory and practice.

A straightforward text that develops the theory of time series a the level of our course. It is less encyclopedic than Zivot and Wang, and this makes it easier to read. This text is useful even though it does not fully engage the struggle required by an honest attempt to understand real-world financial time series.

For many, the "big green book" is their main resource. Weighing in at just under 800 pages, it is arguably the most complete treatment of the theory of time series as it is currently applied in economics and finance. It is more mathematical than our course, but for students who expect to make time series a serious part of their professional tool kit, it is worth the investment.