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Estimating Nonlinear Business Cycle Mechanisms with Linear Vector Autoregressions: A Monte Carlo Study

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  • Karsten Kohler
  • Robert Calvert Jump

Abstract

The paper investigates how well linear vector autoregressions (VARs) identify endogenous cycle mechanisms and cycle frequencies when the underlying process is a nonlinear limit cycle. We conduct Monte Carlo simulations with five nonlinear models in which cycles are driven by the interaction of two state variables. We find that while linear VARs quantitatively underestimate the strength of the interaction mechanism, they successfully identify the qualitative presence of a cycle mechanism in most cases (55%–100%). Our results further suggest that linear VARs are surprisingly successful at estimating cycle frequencies of nonlinear processes.

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  • Karsten Kohler & Robert Calvert Jump, 2022. "Estimating Nonlinear Business Cycle Mechanisms with Linear Vector Autoregressions: A Monte Carlo Study," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(5), pages 1077-1100, October.
    • Handle: RePEc:bla:obuest:v:84:y:2022:i:5:p:1077-1100
    DOI: 10.1111/obes.12498
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    References listed on IDEAS

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