IDENTIFYING TECHNOLOGY SHOCKS at the BUSINESS CYCLE VIA SPECTRAL VARIANCE DECOMPOSITIONS

Identificador:  imarina:6112156

Handle: https://hdl.handle.net/20.500.11797/imarina6112156

Autores:  Lovcha, Yuliya; Perez-Laborda, Alejandro

Resumen:
© Cambridge University Press 2020. 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.