Author, as appears in the article.: Lovcha, Yuliya; Perez-Laborda, Alejandro
Department: Economia
URV's Author/s: Lovcha Lovcha, Yuliya / Perez Laborda, Alejandro
Keywords: Technology shock Svar Rbc model Long-run restrictions Hours worked Frequency domain technology shock rbc model identification hours worked frequency domain employment
Abstract: © 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.
Thematic Areas: Economics and econometrics Economics Economia Ciencias sociales
licence for use: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 13651005
Author's mail: yuliya.lovcha@urv.cat alejandro.perez@urv.cat
Author identifier: 0000-0002-0481-7785 0000-0003-4247-598X
Record's date: 2024-12-07
Papper version: info:eu-repo/semantics/acceptedVersion
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Macroeconomic Dynamics. 25 (8): 1966-1992
APA: Lovcha, Yuliya; Perez-Laborda, Alejandro (2021). IDENTIFYING TECHNOLOGY SHOCKS at the BUSINESS CYCLE VIA SPECTRAL VARIANCE DECOMPOSITIONS. Macroeconomic Dynamics, 25(8), 1966-1992. DOI: 10.1017/S1365100519000932
Entity: Universitat Rovira i Virgili
Journal publication year: 2021
Publication Type: Journal Publications
Repositori URV