Articles producció científica> Economia

IDENTIFYING TECHNOLOGY SHOCKS at the BUSINESS CYCLE VIA SPECTRAL VARIANCE DECOMPOSITIONS

  • Identification data

    Identifier: imarina:6112156
    Handle: https://hdl.handle.net/20.500.11797/imarina6112156
    Authors:
    Lovcha YPerez-Laborda A
    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.

  • Others:

    Author, as appears in the article.: Lovcha Y; Perez-Laborda A
    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: 2024-07-27
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://www.cambridge.org/core/journals/macroeconomic-dynamics/article/abs/identifying-technology-shocks-at-the-business-cycle-via-spectral-variance-decompositions/A53F3F7EF66EAD06EC63A3D909AC30B8
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Macroeconomic Dynamics. 25 (8): 1966-1992
    APA: Lovcha Y; Perez-Laborda A (2021). IDENTIFYING TECHNOLOGY SHOCKS at the BUSINESS CYCLE VIA SPECTRAL VARIANCE DECOMPOSITIONS. Macroeconomic Dynamics, 25(8), 1966-1992. DOI: 10.1017/S1365100519000932
    Article's DOI: 10.1017/S1365100519000932
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2021
    Publication Type: Journal Publications

  • Keywords:

    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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