Reproducing Business Cycle Features: Are Nonlinear Dynamics a Proxy for Multivariate Information?

James Morley, Jeremy Piger and Pao-Lin Tien

No 2012-23, Discussion Papers from School of Economics, The University of New South Wales

Abstract: We consider the extent to which different time-series models can generate simulated data with the same business cycle features that are evident in U.S. real GDP. We focus our analysis on whether multivariate linear models can improve on the previously documented failure of univariate linear models to replicate certain key business cycle features. We find that a particular nonlinear Markov-switching specification with an explicit “bounceback” effect continues to outperform linear models, even when the models incorporate variables such as the unemployment rate, inflation, interest rates, and the components of GDP. These results are robust to simulated data generated either using Normal disturbances or bootstrapped disturbances, as well as to allowing for a one-time structural break in the variance of shocks to real GDP growth.

Keywords: Business cycle features; nonlinear dynamics; multivariate models. (search for similar items in EconPapers)
JEL-codes: C52 E30 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2012-03
New Economics Papers: this item is included in nep-bec, nep-ets, nep-mac and nep-ore
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Citations: View citations in EconPapers (2)

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Journal Article: Reproducing business cycle features: are nonlinear dynamics a proxy for multivariate information? (2013)
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