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Combinación de brechas del producto colombiano

Author

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  • Paulo M. Sánchez
  • Luis Fernando Melo

Abstract

Este documento combina estimaciones de 8 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 vectoriales autorregresivos que incluyen las diferentes brechas y la inflación, se construyen las densidades combinadas de pronósticos de la brecha mediante el uso de 3 esquemas de ponderación: logarítmicos, basados en puntuaciones de rango de probabilidad continuo y en el error cuadrático medio; estas densidades de la brecha resultan útiles porque proveen indicios de su tendencia central a la vez que caracterizan su incertidumbre. Los resultados sugieren que las densidades combinadas bajo estos 3 esquemas con horizontes de pronóstico de 1, 2, 3 y 4 trimestres adelante están bien especificadas. Adicionalmente, las puntuaciones logarítmicas calculadas sobre estas densidades muestran que las metodologías basadas en ponderadores logarítmicos son las que presentan mejor desempeno, y para horizontes de pronóstico de 2 y 3 trimestres, tienen significativamente una mayor puntuación que las calculadas por los ponderadores basados en puntuaciones de rango de probabilidad continuo y error cuadrático medio.

Suggested Citation

  • Paulo M. Sánchez & Luis Fernando Melo, 2013. "Combinación de brechas del producto colombiano," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 31(72), pages 74-82, December.
    • Handle: RePEc:col:000107:010896
    DOI: 10.1016/S0120-4483(13)70006-X
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    6. 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.
    7. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    8. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.
    9. 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.
    10. 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.
    11. James Mitchell & Stephen G. Hall, 2005. "Evaluating, Comparing and Combining Density Forecasts Using the KLIC with an Application to the Bank of England and NIESR ‘Fan’ Charts of Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 995-1033, December.
    12. 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.
    13. 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.
    14. 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.
    15. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    16. Eliana González Molano & Luis Fernando Melo Velandia & Anderson Grajales Olarte, 2007. "Pronósticos directos de la inflación colombiana," Borradores de Economia 458, Banco de la Republica de Colombia.
    17. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    18. Harvey, Andrew & Proietti, Tommaso (ed.), 2005. "Readings in Unobserved Components Models," OUP Catalogue, Oxford University Press, number 9780199278695, Decembrie.
    19. 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.
    20. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
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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 densidadesde 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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