IDEAS home Printed from https://ideas.repec.org/p/rbp/wpaper/2022-008.html
   My bibliography  Save this paper

Evaluando el modelo Growth-at-Risk como herramienta para vigilar los riesgos macrofiancieros en la economía peruana

Author

Listed:
  • Chicama, Diego
  • Nivin, Rafael

    (Banco Central de Reserva del Perú)

Abstract

Este artículo evalúa la metodología de Growth-at-Risk desarrollada por Adrian et al. (2019) para la economía peruana. Con este propósito se evalúa la precisión de las distintas técnicas para estimar la densidad de los pronósticos de crecimiento del PBI, condicionados a un conjunto de variables que caracterizar las condiciones macrofinancieras actuales de la economía peruana. Con la mejor estimación de la distribución condicionada del pronóstico del PBI, se evalúa el impacto del programa gubernamental de apoyo al crédito, Reactiva Perú, en la estabilidad macroeconómica y financiera doméstica. Nuestros resultados muestran que Reactiva Perú tuvo un impacto importante en la estabilidad macroeconómica y financiera, ya que evitó una disminución mucho más profunda de la actividad económica durante la crisis Covid-19.

Suggested Citation

  • Chicama, Diego & Nivin, Rafael, 2022. "Evaluando el modelo Growth-at-Risk como herramienta para vigilar los riesgos macrofiancieros en la economía peruana," Working Papers 2022-008, Banco Central de Reserva del Perú.
    • Handle: RePEc:rbp:wpaper:2022-008
    as

    Download full text from publisher

    File URL: https://www.bcrp.gob.pe/docs/Publicaciones/Documentos-de-Trabajo/2022/documento-de-trabajo-008-2022.pdf
    File Function: Application/pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Growth-at-Risk; financial stability; quantile regression;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rbp:wpaper:2022-008. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Research Unit (email available below). General contact details of provider: https://edirc.repec.org/data/bcrgvpe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.