Estimating nonlinear business cycle mechanisms with linear vector autoregressions: a Monte Carlo study
Kohler, Karsten and Calvert Jump, Robert ORCID: 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:https://doi.org/10.1111/obes.12498)
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PDF (Author's Accepted Manuscript)
36029 CALVERT_JUMP_Estimating_Nonlinear_Business_Cycle_Mechanisms_With_Linear_Vector_Autoregressions_(AAM)_2022.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
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 |
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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) |
Last Modified: | 06 Oct 2022 11:38 |
URI: | http://gala.gre.ac.uk/id/eprint/36029 |
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