IDENTIFYING TECHNOLOGY SHOCKS AT THE BUSINESS CYCLE VIA SPECTRAL VARIANCE DECOMPOSITIONS

Yuliya Lovcha and Alejandro Perez-Laborda

Macroeconomic Dynamics, 2021, vol. 25, issue 8, 1966-1992

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.

Date: 2021
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