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Characterizing and Communicating the Balance of Risks of Macroeconomic Forecasts: A Predictive Density Approach for Colombia

Author

Listed:
  • Juan C. Méndez-Vizcaíno
  • Alexander Guarin
  • César Anzola-Bravo
  • Anderson Grajales-Olarte

Abstract

Since July 2021, Banco de la República strengthened its forecasting process and communication instruments, by involving predictive densities in the projections of its models, PATACON and 4GM. This paper presents the main theoretical and empirical elements of the predictive density approach for macroeconomic forecasting. This model-based methodology allows to characterize the balance of risks of the economy, and to quantify their effects through a joint probability distribution of forecasts. We estimate this distribution based on the simulation of DSGE models, preserving the general equilibrium relationships and their macroeconomic consistency. We also illustrate the technical criteria used to represent prospective factors of risk through the probability distributions of shocks. **** RESUMEN: Desde julio de 2021, el Banco de la República fortaleció su proceso de pronóstico y sus instrumentos de comunicación al incorporar densidades predictivas en las proyecciones de sus modelos, PATACON y 4GM. Este artículo presenta los principales elementos teóricos y empíricos del enfoque de densidad predictiva para los pronósticos macroeconómicos. Esta metodología basada en modelos permite caracterizar el balance de riesgos de la economía y cuantificar sus efectos mediante una distribución de probabilidad conjunta de los pronósticos. Esta distribución se estima mediante la simulación de los modelos DSGE, preservando las relaciones de equilibrio general y la coherencia macroeconómica. También se ilustran los criterios técnicos utilizados para representar los factores de riesgo prospectivos a través de las distribuciones de probabilidad de los choques.

Suggested Citation

  • Juan C. Méndez-Vizcaíno & Alexander Guarin & César Anzola-Bravo & Anderson Grajales-Olarte, 2021. "Characterizing and Communicating the Balance of Risks of Macroeconomic Forecasts: A Predictive Density Approach for Colombia," Borradores de Economia 1178, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:1178
    DOI: 10.32468/be.1178
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    References listed on IDEAS

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    1. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1405-1423, September.
    2. Andrés González & Lavan Mahadeva & Juan D. Prada & Diego Rodríguez, 2011. "Policy Analysis Tool Applied to Colombian Needs: Patacon Model Description," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República - ESPE, vol. 29(66), pages 222-245, December.
    3. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    4. Del Negro, Marco & Hasegawa, Raiden B. & Schorfheide, Frank, 2016. "Dynamic prediction pools: An investigation of financial frictions and forecasting performance," Journal of Econometrics, Elsevier, vol. 192(2), pages 391-405.
    5. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 1, pages 3-80, Elsevier.
    6. Malte Knüppel & Guido Schultefrankenfeld, 2012. "How Informative Are Central Bank Assessments of Macroeconomic Risks?," International Journal of Central Banking, International Journal of Central Banking, vol. 8(3), pages 87-139, September.
    7. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, November.
    8. Claeskens, Gerda & Magnus, Jan R. & Vasnev, Andrey L. & Wang, Wendun, 2016. "The forecast combination puzzle: A simple theoretical explanation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 754-762.
    9. Friedman, Milton, 1972. "Have Monetary Policies Failed?," American Economic Review, American Economic Association, vol. 62(2), pages 11-18, May.
    10. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
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    More about this item

    Keywords

    Macroeconomic forecasts; balance of risks; uncertainty; Bayesian forecasting; monetary policy models; Pronósticos macroeconómicos; balance de riesgos; incertidumbre; pronósticos bayesianos; modelos de política monetaria;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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