The importance of nonlinearity in reproducing business cycle features

James Morley and Jeremy Piger

No 2004-032, Working Papers from Federal Reserve Bank of St. Louis

Abstract: This paper considers the ability of simulated data from linear and nonlinear time-series models to reproduce features in U.S. real GDP data related to business cycle phases. We focus our analysis on a number of linear ARIMA models and nonlinear Markov-switching models. To determine the timing of business cycle phases for the simulated data, we present a model-free algorithm that is more successful than previous methods at matching NBER dates and associated features in the postwar data. We find that both linear and Markov-switching models are able to reproduce business cycle features such as the average growth rate in recessions, the average length of recessions, and the total number of recessions. However, we find that Markov-switching models are better than linear models at reproducing the variability of growth rates in different business cycle phases. Furthermore, certain Markov-switching specifications are able to reproduce high-growth recoveries following recessions and a strong correlation between the severity of a recession and the strength of the subsequent recovery. Thus, we conclude that nonlinearity is important in reproducing business cycle features.

Keywords: Business; cycles (search for similar items in EconPapers)
Date: 2005
New Economics Papers: this item is included in nep-cmp and nep-mac
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Citations: View citations in EconPapers (15)

Published in Nonlinear Time Series Analysis of Business Cycles, edited by C. Milas, P. Rothman, and D. van Dijk. Elsevier Science, Amsterdam, 2006

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Related works:
Chapter: The Importance of Nonlinearity in Reproducing Business Cycle Features (2006)
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