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Combinación de brechas del producto colombiano

Author

Listed:
  • Paulo Mauricio Sánchez Beltrán
  • Luis Fernando Melo Velandia
Abstract
Este documento combina estimaciones de ocho metodologías de la brecha del producto colombiano para el período comprendido entre el primer trimestre de 1994 y el tercer trimestre de 2012. A partir de modelos VAR que incluyen las diferentes brechas y la inflación se construyen las densidades combinadas de pronósticos de la brecha mediante el uso de tres esquemas de ponderación: logarítmicos, basados en puntuaciones de rango de probabilidad continuo (CRPS) y basados en el error cuadrático medio (MSE). Los resultados sugieren que las densidades combinadas bajo estos tres esquemas con horizontes de pronóstico de uno, dos, tres y cuatro trimestres adelante están bien especificadas. Adicionalmente, las puntuaciones logarítmicas calculadas sobre estas densidades muestran que las metodologías basadas en ponderadores logarítmicos para horizontes de pronóstico de dos y tres trimestres tienen significativamente un mejor desempeño que las calculadas por los ponderadores CRPS y MSE.

Suggested Citation

  • Paulo Mauricio Sánchez Beltrán & Luis Fernando Melo Velandia, 2013. "Combinación de brechas del producto colombiano," Borradores de Economia 775, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:borrec:775
    DOI: 10.32468/be.775
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    References listed on IDEAS

    as
    1. Dr. James Mitchell, 2009. "Measuring Output Gap Uncertainty," National Institute of Economic and Social Research (NIESR) Discussion Papers 342, National Institute of Economic and Social Research.
    2. Anne Sofie Jore & James Mitchell & Shaun P. Vahey, 2010. "Combining forecast densities from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 621-634.
    3. Chris McDonald & Leif Anders Thorsrud, 2011. "Evaluating density forecasts: model combination strategies versus the RBNZ," Reserve Bank of New Zealand Discussion Paper Series DP2011/03, Reserve Bank of New Zealand.
    4. Andrés González & Segio Ocampo & Julián Pérez & Diego Rodríguez, 2013. "Output Gap and Neutral Interest Measures of Colombia," Monetaria, CEMLA, vol. 0(2), pages 231-286, July-Dece.
    5. Bjørnland, Hilde C. & Gerdrup, Karsten & Jore, Anne Sofie & Smith, Christie & Thorsrud, Leif Anders, 2011. "Weights and pools for a Norwegian density combination," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 61-76, January.
    6. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    7. Eliana González Molano & Luis Fernando Melo Velnadia & Anderson Grajales Olarte, 2007. "Pronósticos directos de la inflación colombiana," Borradores de Economia 4247, Banco de la Republica.
    8. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
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    11. Dr. James Mitchell, 2005. "Evaluating, comparing and combining density forecasts using the KLIC with an application to the Bank of England and NIESR ÔfanÕ charts of inflation," National Institute of Economic and Social Research (NIESR) Discussion Papers 253, National Institute of Economic and Social Research.
    12. Norberto Rodríguez N & José Luis Torres & Andrés Velasco M., 2006. "La estimación de un indicador de brecha del producto a partir de encuestas y datos reales," Borradores de Economia 3000, Banco de la Republica.
    13. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    14. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
    15. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.
    16. Harvey, Andrew & Proietti, Tommaso (ed.), 2005. "Readings in Unobserved Components Models," OUP Catalogue, Oxford University Press, number 9780199278695.
    17. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
    18. Dr. James Mitchell, 2005. "Evaluating, comparing and combining density forecasts using the KLIC with an application to the Bank of England and NIESR ÔfanÕ charts of inflation," National Institute of Economic and Social Research (NIESR) Discussion Papers 253, National Institute of Economic and Social Research.
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    21. Dr. James Mitchell, 2009. "Measuring Output Gap Uncertainty," National Institute of Economic and Social Research (NIESR) Discussion Papers 342, National Institute of Economic and Social Research.
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    Cited by:

    1. Jorge Mario Uribe & Inés María Ulloa & Johanna Perea, 2015. "Reference financial cycle in Colombia," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 83, pages 33-62, Julio - D.
    2. Amador-Torres, J. Sebastián, 2017. "Finance-neutral potential output: An evaluation in an emerging market monetary policy context," Economic Systems, Elsevier, vol. 41(3), pages 389-407.

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    More about this item

    Keywords

    Combinación de densidades de pronóstico; brecha del producto; pronósticos directos; modelos VAR.;
    All these keywords.

    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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