Articles producció científica> Economia

IDENTIFYING TECHNOLOGY SHOCKS at the BUSINESS CYCLE VIA SPECTRAL VARIANCE DECOMPOSITIONS

  • Datos identificativos

    Identificador: imarina:6112156
    Handle: https://hdl.handle.net/20.500.11797/imarina6112156
    Autores:
    Lovcha, YuliyaPerez-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.

  • Otros:

    Autor según el artículo: Lovcha, Yuliya; Perez-Laborda, Alejandro
    Departamento: Economia
    Autor/es de la URV: Lovcha Lovcha, Yuliya / Perez Laborda, Alejandro
    Palabras clave: Technology shock Svar Rbc model Long-run restrictions Hours worked Frequency domain technology shock rbc model identification hours worked frequency domain employment
    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.
    Áreas temáticas: Economics and econometrics Economics Economia Ciencias sociales
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 13651005
    Direcció de correo del autor: yuliya.lovcha@urv.cat alejandro.perez@urv.cat
    Identificador del autor: 0000-0002-0481-7785 0000-0003-4247-598X
    Fecha de alta del registro: 2024-12-07
    Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Macroeconomic Dynamics. 25 (8): 1966-1992
    Referencia de l'ítem segons les normes 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
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2021
    Tipo de publicación: Journal Publications

  • Palabras clave:

    Economics,Economics and Econometrics
    Technology shock
    Svar
    Rbc model
    Long-run restrictions
    Hours worked
    Frequency domain
    technology shock
    rbc model
    identification
    hours worked
    frequency domain
    employment
    Economics and econometrics
    Economics
    Economia
    Ciencias sociales

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