Kohler, Karsten and Calvert Jump, Robert ORCID: https://orcid.org/0000-0002-2967-512X (2022) Estimating nonlinear business cycle mechanisms with linear vector autoregressions: a Monte Carlo study. Oxford Bulletin of Economics and Statistics, 84 (5). pp. 1077-1100. ISSN 0305-9049 (Print), 1468-0084 (Online) (doi:10.1111/obes.12498)

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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.

Item Type:	Article
Uncontrolled Keywords:	vector autoregression, limit cycles, endogenous cycles, business and financial cycles, cycle frequency
Subjects:	H Social Sciences > HB Economic Theory
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Department of International Business & Economics Faculty of Business > Institute of Political Economy, Governance, Finance and Accountability (IPEGFA) Greenwich Business School > Political Economy, Governance, Finance and Accountability (PEGFA)
Last Modified:	02 Dec 2024 16:09
URI:	http://gala.gre.ac.uk/id/eprint/36029

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