Hamilton 1994 Time Series Analysis

[Kayleen]

Usingthe 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:

Professor of Economics, UCSD

E-mail: jham...@ucsd.edu

Address:Department of Economics, 0508

University of California, San Diego

9500 Gilman Drive

La Jolla, CA 92093-0508Biographical informationGoogle Scholar profileCurriculum Vitae Econbrowser blog Current working papers Publications Chronologically Publications in macroeconomics, monetary policy, and labor markets Publications in econometrics and measurement Publications in energy economics and commodity marketsPublications in financeSlides from recent conference presentationsDownload computer software and data sets Links to some of my former studentsTeaching materials Disclosure of outside compensated activitiesCurrent Working Papers Uncovering Disaggregated Oil Market Dynamics: A Full-Information Approach to Granular Instrumental Variables, coauthored with Christiane Baumeister.The world price of oil is determined by the interactions of multiple producers and consumers who face different constraints and shocks. We show how this feature of the oil market can be used to estimate local and global elasticities of supply and demand and provide a rich set of testable restrictions. We develop a novel approach to estimation based on full-information maximum likelihood that generalizes the insights from granular instrumental variables. We conclude that the supply responses of Saudi Arabia and adjustments of inventories have historically played a key role in stabilizing the price of oil. We illustrate how our structural model can be used to analyze how individual producers and consumers would dynamically adapt to a geopolitical event such as a major disruption in the supply of oil from Russia. Links to replication data and code,presentation slides, and video of presentation at NBER 2024 Summer Institute (talk starts at 4:00).

Principal Component Analysis for Nonstationary Series, coauthored with Jin Xi. This paper develops a procedure for uncovering the common cyclical factors that drive a mix of stationary and nonstationary variables. The method does not require knowing which variables are nonstationary or the nature of the nonstationarity. Applications to the term structure of interest rates and to the FRED-MD macroeconomic dataset demonstrate that the approach offers similar benefits to those of traditional principal component analysis with some added advantages.Download data, replication code, and recent values of the macroeconomic indexes or presentation slides.Supply, Demand, and Specialized Production. This paper develops a unified model of economic fluctuations and growth characterized by long-run equilibrium unemployment and sustained monopoly power. The level of demand is a key factor in deviations from the steady-state growth path with a Keynesian-type spending multiplier despite the absence of any nominal rigidities. The key friction in the model is the technological requirement that production of certain goods requires a dedicated team of workers that takes time to assemble and train.

Measuring the Credit Gap, coauthored with Daniel Leff. We revisit the analysis by Drehmann and Yetman (2018) and conclude that measuring the credit gap based on the 5-year growth rate of the credit-to-GDP ratio produces a more reliable and robust predictor of financial crises than does the Hodrick-Prescott filtered series. We also conclude that estimating the credit gap based on the forecast error of a 5-year-ahead regression can be even more useful, provided a sufficiently long sample is available to estimate coefficients of the regression.

Structural Interpretation of Vector Autoregressions with Incomplete Identification: Setting the Record Straight, coauthored with Christiane Baumeister. A recent paper by Kilian and Zhou (2019) mischaracterizes our 2019 paper in American Economic Review and much of the related literature. They misstate our contribution to the literature on identification, mischaracterize the role of prior information about supply elasticity in our analysis, inaccurately describe the relation between structural elasticities and the impacts of shocks, and mischaracterize the literature on supply elasticity. Our purpose in this paper is to set the record straight. Download data and code to replicate.

Advances in Using Vector Autoregressions to Estimate Structural Magnitudes, Econometric Theory, Volume 40, Issue 3, June 2024, pp. 472-510. Coauthored with Christiane Baumeister. This paper surveys recent advances in drawing structural conclusions from vector autoregressions, providing a unified perspective on the role of prior knowledge. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns from both a frequentist and a Bayesian perspective about the way that results are typically reported for VARs that are set-identified using sign and other restrictions. We call attention to a common but previously unrecognized error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one only knows the effects of a single structural shock.Replication code.

Structural Vector Autoregressions with Imperfect Identifying Information, American Economic Association Papers and Proceedings, May 2022, 112, pp. 466-470. Coauthored with Christiane Baumeister. Prepared for the AEA Papers & Proceedings. The problem of identification is often the core challenge of empirical economic research. The traditional approach to identification is to bring in additional information in the form of identifying assumptions, such as restrictions that certain magnitudes have to be zero. In this paper we suggest that what are usually thought of as identifying assumptions should more generally be described as information that the analyst had about the economic structure before seeing the data. Such information is most naturally represented as a Bayesian prior distribution over certain features of the economic structure. replication code

Measuring Labor-Force Participation and the Incidence and Duration of Unemployment, Review of Economic Dynamics, April 2022 (4):1-32. Coauthored with Hie Joo Ahn.The underlying data from which the U.S. unemployment rate, labor-force participation rate, and duration of unemployment are calculated contain numerous internal contradictions. This paper catalogs these inconsistencies and proposes a unified reconciliation. We find that the usual statistics understate the unemployment rate and the labor-force participation rate by about two percentage points on average and that the bias in the latter has increased over time. The BLS estimate of the average duration of unemployment substantially overstates the true duration of uninterrupted spells of unemployment and misrepresents what happened to average durations during the Great Recession and its recovery. Online appendix here.Revised data series developed in the paper available here. Also available are data and code for complete replication.

Measuring Global Economic Activity, Journal of Applied Econometrics, April/May 2021, 36(3), pp. 293-303. A number of economic studies have used a proxy for world real economic activity derived from shipping costs. This measure turns out to depend on a normalization that has substantive consequences of which users of the index had been unaware prior to this paper. This paper further evaluates this and alternative measures in terms of treatment of trends, coherence with world output, and ability to predict commodity prices. I conclude that measures derived from world industrial production offer a better indicator of global real economic activity. Nontechnical summary. Replication data and code. Presentation slides. Updated data on the world industrial production index developed by Baumeister and Hamilton, AER 2019 and on nominal and real shipping costs.

Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions, Journal of International Money and Finance, 2020, volume 109, article 102250. Coauthored with Christiane Baumeister. This paper discusses the problems associated with using information about the signs of certain magnitudes as a basis for drawing structural conclusions in vector autoregressions. We also review available tools to solve these problems. For illustration we use Dahlhaus and Vasishtha's (2019) study of the effects of a U.S. monetary contraction on capital flows to emerging markets. We explain why sign restrictions alone are not enough to allow us to answer the question and suggest alternative approaches that could be used.

Perspectives on U.S. Monetary Policy Tools and Instruments, in Strategies for Monetary Policy, pp. 173-210, edited by John H. Cochrane and John B. Taylor, Hoover Institution Press, 2020. Click here for a video of my presentation of the paper.

3a8082e126