Does the use of a big data variable improve monetary policy estimates? Evidence from Mexico

Autores/as

  • Luis Alberto Delgado-de-la-Garza Departamento de Economía, Universidad de Monterrey (UDEM, Mexico)
  • Gonzalo Adolfo Garza-Rodríguez Departamento de Economía, Universidad de Monterrey (UDEM, Mexico)
  • Daniel Alejandro Jacques-Osuna Departamento de Economía, Universidad de Monterrey (UDEM, Mexico)
  • Alejandro Múgica-Lara Departamento de Economía, Universidad de Monterrey (UDEM, Mexico)
  • Carlos Alberto Carrasco Departamento de Economía, Universidad de Monterrey (UDEM, Mexico) http://orcid.org/0000-0002-5439-4960

DOI:

https://doi.org/10.17811/ebl.10.4.2021.383-393

Resumen

We analyse the performance improvement on a monetary policy model of introducing non-conventional market attention (NCMA) indices generated using big data. To address this aim, we extracted top keywords by text mining Banco de Mexico’s minutes. Then, we used Google search information according to the top keywords and related queries to generate NCMA indices. Finally, we introduce as covariates the NCMA indices into a bivariate probit model of monetary policy and contrast several specifications to examine the improvement in the model estimates. Our results show evidence of the statistical significance of the NCMA indices where the expanded model performed better than models only including conventional economic and financial variables.

Biografía del autor/a

Carlos Alberto Carrasco, Departamento de Economía, Universidad de Monterrey (UDEM, Mexico)

Associate Professor of Economics, Departamento de Economía, Universidad de Monterrey (UDEM, Mexico) and member of the National System of Researcher (SNI-CONACyT, Mexico)

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Publicado

2021-12-09

Cómo citar

Delgado-de-la-Garza, L. A., Garza-Rodríguez, G. A., Jacques-Osuna, D. A., Múgica-Lara, A., & Carrasco, C. A. (2021). Does the use of a big data variable improve monetary policy estimates? Evidence from Mexico. Economics and Business Letters, 10(4), 383–393. https://doi.org/10.17811/ebl.10.4.2021.383-393

Número

Vol. 10 Núm. 4 (2021): December

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Artículos

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