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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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    Bibliographic Info

    Paper provided by BANCO DE LA REPÚBLICA in its series BORRADORES DE ECONOMIA with number 007013.

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    Length: 49
    Date of creation: 23 May 2010
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    Handle: RePEc:col:000094:007013

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    Related research

    Keywords: Bayesian model averaging; forecast combination; Inflation; Information theoretical model averaging.;

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    1. Sune Karlsson & Tor Jacobson, 2004. "Finding good predictors for inflation: a Bayesian model averaging approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(7), pages 479-496.
    2. Carmen Fernández & Eduardo Ley & Mark F. J. Steel, . "Benchmark priors for Bayesian Model averaging," Working Papers 98-06, FEDEA.
    3. Eliana González & . Luis F. Melo & Viviana Monroy & Brayan Rojas, . "A Dynamic Factor Model for the Colombian Inflation," Borradores de Economia 549, Banco de la Republica de Colombia.
    4. Luis Fernando Melo Velandia & Héctor M. Núñez Amortegui, 2004. "Combinación de pronósticos de la inflación en presencia de cambios estructurales," BORRADORES DE ECONOMIA 002153, BANCO DE LA REPÚBLICA.
    5. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," NBER Working Papers 11285, National Bureau of Economic Research, Inc.
    6. Kapetanios, George & Labhard, Vincent & Price, Simon, 2006. "Forecasting using predictive likelihood model averaging," Economics Letters, Elsevier, vol. 91(3), pages 373-379, June.
    7. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
    8. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecasting Using Bayesian and Information-Theoretic Model Averaging: An Application to U.K. Inflation," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 33-41, January.
    9. Eklund, Jana & Karlsson, Sune, 2005. "Forecast Combination and Model Averaging using Predictive Measures," Working Paper Series 191, Sveriges Riksbank (Central Bank of Sweden).
    10. Forni M. & Hallin M., 2003. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Computing in Economics and Finance 2003 143, Society for Computational Economics.
    11. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Banco de Espa�a Working Papers 0112, Banco de Espa�a.
    12. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    13. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    14. 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, vol. 97, pages 1167-1179, December.
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