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Estimación de la variación del precio de los alimentos con modelos de frecuencias mixtas

Author

Listed:
  • Julián Alonso Cárdenas-Cárdenas

    (Banco de la República de Colombia)

  • Edgar Caicedo-García

    (Banco de la República de Colombia)

  • Eliana R. González Molano

    (Banco de la República de Colombia)

Abstract

El comportamiento de los precios de los alimentos en Colombia ha sido un factor que inquieta a la autoridad monetaria por su volatilidad, alta ponderación en la canasta de IPC y en ocasiones recurrentes altos niveles debido a su reacción a choques de oferta como el clima, lo cual dificulta la tarea de estabilizar la inflación alrededor de la meta. De lo anterior, se desprende la necesidad de tener pronósticos insesgados y más oportunos de los cambios en el precio de los alimentos en el corto plazo. En este documento se desarrolla una metodología que aprovecha la información disponible con alta frecuencia de precios y abastecimiento de alimentos y permite combinar información observada en varias frecuencias para generar pronósticos alternativos de la variación de los precios de los alimentos y sus diferentes componentes. Los resultados encontrados indican que los modelos propuestos de frecuencias mixtas, producen mejores pronósticos que los tradicionales que utilizan solamente información de precios del Sistema de Información de Precios del Sector Agropecuario (SIPSA-DANE).. **** ABSTRACT: The behavior of food prices is a big issue for the monetary authority, due to the high volatility as well as the big weight it has in the CPI basket and because it reacts temporarily to supply shocks, such as climate conditions, what makes difficult the task of keeping total inflation around the target. Thus, it is needed to count with more accurate and timely forecasts of food inflation for the short run in order to guide the macroeconomic model for monetary policy and help the authority in the decision making process. For that purpose, in this document we apply a methodology that combines information of different frequencies (MIDAS) to produce forecasts for food inflation. In particular, information about food prices at a very disaggregate level and an indicator for food supply, which are available in a weekly basis, may help to generate a more accurate nowcast of total food inflation and its components: perishable and processed food. Compared to a naïve nowcast generated every week as the weighted average change of food prices taken by SIPSA, the results show an improvement in the nowcast, generated by the mixed frequency data models that includes not only high frequency variables as explanatory but also some other determinants of food price changes such as unemployment, climate conditions and international commodity prices. Thus, MIDAS models are a promising alternative to generate forecasts in the short run.

Suggested Citation

  • Julián Alonso Cárdenas-Cárdenas & Edgar Caicedo-García & Eliana R. González Molano, 2020. "Estimación de la variación del precio de los alimentos con modelos de frecuencias mixtas," Borradores de Economia 1109, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:borrec:1109
    DOI: https://doi.org/10.32468/be.1109
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    References listed on IDEAS

    as
    1. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    2. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    3. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    4. Ghysels, Eric & Kvedaras, Virmantas & Zemlys, Vaidotas, 2016. "Mixed Frequency Data Sampling Regression Models: The R Package midasr," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i04).
    5. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.
    6. Chunpeng Yang & Rengui Zhang, 2014. "Does mixed-frequency investor sentiment impact stock returns? Based on the empirical study of MIDAS regression model," Applied Economics, Taylor & Francis Journals, vol. 46(9), pages 966-972, March.
    7. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
    8. JÖrg Breitung & Christoph Roling, 2015. "Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 588-603, November.
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    JEL classification:

    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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