Kohler, K orcid.org/0000-0002-6876-0538 and Calvert Jump, R (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
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Oxford Bulletin of Economics and Statistics published by Oxford University and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Economics Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 May 2022 15:25 |
Last Modified: | 17 Nov 2022 14:58 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1111/obes.12498 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:186789 |