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Bayesian Model Averaging. An Application to Forecast Inflation in Colombia

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  • Eliana González

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    Abstract

    An application of Bayesian Model Averaging, BMA, is implemented to construct combined forecasts for the colombian inflation for the short and medium run. A model selection algorithm is applied over a set of linear models with a large dataset of potencial predictors using marginal as well as predictive likelihood. The forecasts obtained when using predictive likelihood outperformed the ones obtained when using marginal likelihood. BMA forecasts reduce forecasting error compared to the individual forecasts, equal weighted average, dynamic factors model and random walk forecasts for most horizons. Additionally, the BMA outperformed for some horizons the frequentist Information theoretic model average, ITMA, when the weights of both methodologies are build based on the predictive ability of the models.

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    Paper provided by Banco de la Republica de Colombia in its series Borradores de Economia with number 604.

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    Handle: RePEc:bdr:borrec:604

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    Keywords: Bayesian model averaging; forecast combination; Inflation; Information theoretical model averaging. Classification JEL: C11; C15; C52; C53.;

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    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    2. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    3. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, Elsevier, vol. 100(2), pages 381-427, February.
    4. Kapetanios, G. & Labhard, V. & Price, S., 2007. "Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation," Working Papers, Department of Economics, City University London 07/15, Department of Economics, City University London.
    5. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Banco de Espa�a Working Papers, Banco de Espa�a 0112, Banco de Espa�a.
    6. Eklund, Jana & Karlsson, Sune, 2005. "Forecast Combination and Model Averaging Using Predictive Measures," CEPR Discussion Papers, C.E.P.R. Discussion Papers 5268, C.E.P.R. Discussion Papers.
    7. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, Elsevier, vol. 5(4), pages 559-583.
    8. Eliana González & . Luis F. Melo & Viviana Monroy & Brayan Rojas, . "A Dynamic Factor Model for the Colombian Inflation," Borradores de Economia, Banco de la Republica de Colombia 549, Banco de la Republica de Colombia.
    9. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository, ULB -- Universite Libre de Bruxelles 2013/10143, ULB -- Universite Libre de Bruxelles.
    10. Boivin, Jean & Ng, Serena, 2005. "Understanding and Comparing Factor-Based Forecasts," MPRA Paper 836, University Library of Munich, Germany.
    11. Jacobson, Tor & Karlsson, Sune, 2002. "Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach," Working Paper Series, Sveriges Riksbank (Central Bank of Sweden) 138, Sveriges Riksbank (Central Bank of Sweden).
    12. Luis Fernando Melo Velandia & Héctor M. Núñez Amortegui, 2004. "Combinación de pronósticos de la inflación en presencia de cambios estructurales," BORRADORES DE ECONOMIA, BANCO DE LA REPÚBLICA 002153, BANCO DE LA REPÚBLICA.
    13. George Kapetanios & Vincent Labhard & Simon Price, 2006. "Forecasting Using Predictive Likelihood Model Averaging," Working Papers, Queen Mary, University of London, School of Economics and Finance 567, Queen Mary, University of London, School of Economics and Finance.
    14. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 16(2), pages 254-59, April.
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