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Modelos FAVAR con factores estáticos y dinámicos para pronosticar la inflación en Costa Rica

Author

Listed:
  • Carlos Segura-Rodriguez

    (Departamento Investigación Económica, Banco Central de Costa Rica)

Abstract

This study presents a methodology for forecasting inflation in Costa Rica using a FAVAR model that combines data from 156 relevant time series. This approach consists of two stages: first, static and dynamic factors are estimated, which are then incorporated into a VAR model along with monthly inflation to project the annual variation of the Consumer Price Index. Automatic selection criteria are employed to choose which variables to include in the factors and to determine the number of factors, lags, and restrictions on the coefficients of the VAR model. Eight inflation forecasts are generated and combined using three averages: simple, inverse mean squared error weighted, and Bayesian. The results indicate that the Bayesian forecast is the most accurate for the period between 2021 and 2023, outperforming even the most accurate of traditional VAR models that consider only inflation and individually any of the 156 variables. This suggests that the FAVAR model can effectively integrate information from available variables without requiring prior knowledge of which ones are most relevant.

Suggested Citation

  • Carlos Segura-Rodriguez, 2024. "Modelos FAVAR con factores estáticos y dinámicos para pronosticar la inflación en Costa Rica," Documentos de Trabajo 2403, Banco Central de Costa Rica.
    • Handle: RePEc:apk:doctra:2403
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    File URL: https://repositorioinvestigaciones.bccr.fi.cr/handle/20.500.12506/397
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    References listed on IDEAS

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    1. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    2. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
    3. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    4. Döhrn, Roland, 2019. "Comparing forecast accuracy in small samples," Ruhr Economic Papers 833, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    5. Mandalinci, Zeyyad, 2017. "Forecasting inflation in emerging markets: An evaluation of alternative models," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
    6. Winkelried, Diego, 2003. "Hacia una meta explícita de inflación: Anticipando la inflación en el Perú," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 9, pages 11-40.
    7. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    9. repec:hal:journl:peer-00844811 is not listed on IDEAS
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    Cited by:

    1. Carlos Segura-Rodriguez, 2025. "Inflation Forecasting in Costa Rica: The Contribution of Exogenous Variables in Item-Level Disaggregated Models," Documentos de Trabajo 2509, Banco Central de Costa Rica.

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    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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