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IDENTIFYING TECHNOLOGY SHOCKS AT THE BUSINESS CYCLE VIA SPECTRAL VARIANCE DECOMPOSITIONS

Published online by Cambridge University Press:  05 February 2020

Yuliya Lovcha  and
Alejandro Perez-Laborda
Yuliya Lovcha*
Affiliation:
Universitat Rovira-i-Virgili and CREIP
Alejandro Perez-Laborda
Affiliation:
Universitat Rovira-i-Virgili and CREIP
*
Address correspondence to: Yuliya Lovcha, Department of Economics, Universitat Rovira-i-Virgili. Av. Universitat 1, 43204Reus, Spain. e-mail: yuliya.lovcha@gmail.com. Phone: (+34) 977759847. Fax: (+34) 977758907.
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Abstract

In this paper, we identify the technology shock at business cycle frequencies to improve the performance of structural vector autoregression models in small samples. To this end, we propose a new identification method based on the spectral decomposition of the variance, which targets the contributions of the shock in theoretical models. Results from a Monte-Carlo assessment show that the proposed method can deliver a precise estimate of the response of hours in small samples. We illustrate the application of our methodology using US data and a standard Real Business Cycle model. We find a positive response of hours in the short run following a non-significant, near-zero impact. This result is robust to a large set of credible parameterizations of the theoretical model.

Keywords

SVARFrequency DomainRBC ModelHours WorkedTechnology Shock
Type
Articles
Information
Macroeconomic Dynamics , Volume 25 , Issue 8 , December 2021 , pp. 1966 - 1992
Copyright
© Cambridge University Press 2020

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Footnotes

This paper has benefited from numerous comments and suggestions from two anonymous referees. Perez-Laborda acknowledges financial support from the Spanish Ministry of Economy and Competitiveness through AEI/FEDER-EU (ECO2016-75410-P) grant. The usual disclaimers apply.

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