Measuring Business Cycle Features

Gregory D. Hess and Shigeru Iwata
October 1995
RWP 95-10
Research Division
Federal Reserve Bank of Kansas City

ABSTRACT

Since the extensive work by Burns and Mitchell (1947), many economists have interpreted economic fluctuations in terms of business cycle phases. Given this, we argue that in addition to usual model selection criteria currently used in the profession, the adequacy of a univariate macroeconomic time series model should be based on its ability to replicate two most important business cycle features of the U.S. data--duration and amplitude. We propose a number of checks for whether univariate statistical models generate business cycle features observed in US GDP and find that many popular non-linear models for the log of real GDP are no better at replicating the duration and amplitude features of the data than a simple ARIMA(1,1,0).

Keywords: business cycles, random walk with drift.

Gregory D. Hess is an assistant professor of economics at the University of Kansas and a visiting scholar at the Federal Reserve Bank of Kansas City. Shigeru Iwata is an associate professor of economics at the University of Kansas. The authors wish to thank Craig Hakkio, Kajal Lahiri and Dan Sichel for comments, Jim Hamilton for providing us with his computer programs for estimating switching regime models, and seminar participants at the University of Kansas, Kansas State University, the Federal Reserve Bank of Kansas City, and the 7th World Congress of the Econometrica Society held in Tokyo. Part of this research was conducted when Gregory D. Hess was a visiting scholar at the Federal Reserve Bank of Kansas City. The opinions expressed are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Kansas City or of the Federal Reserve System.

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