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Forecasting With Many Predictors. An Empirical Comparison

  • Eliana González

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    Three methodologies of estimation of models with many predictors are implemented to forecast Colombian inflation. Two factor models, based on principal components, and partial least squares, as well as a Bayesian regression, known as Ridge regression are estimated. The methodologies are compared in terms of out-sample RMSE relative to two benchmark forecasts, a random walk and an autoregressive model. It was found, that the models that contain many predictors outperformed the benchmarks for most horizons up to 12 months ahead, however the reduction in RMSE is only statistically significant for the short run. Partial least squares outperformed the other approaches based on large datasets.

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    File URL: http://www.banrep.gov.co/docum/ftp/borra643.pdf
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    Paper provided by Banco de la Republica de Colombia in its series Borradores de Economia with number 643.

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    Handle: RePEc:bdr:borrec:643
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    1. Eliana González & Luis F. Melo & Viviana Monroy & Brayan Rojas, 2009. "A Dynamic Factor Model For The Colombian Inflation," BORRADORES DE ECONOMIA 005273, BANCO DE LA REPÚBLICA.
    2. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2003. "The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting," LEM Papers Series 2003/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Breitung, Jörg & Eickmeier, Sandra, 2005. "Dynamic factor models," Discussion Paper Series 1: Economic Studies 2005,38, Deutsche Bundesbank, Research Centre.
    4. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
    5. Kapetanios, George & Marcellino, Massimiliano, 2006. "A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions," CEPR Discussion Papers 5620, C.E.P.R. Discussion Papers.
    6. 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.
    7. Ziegler, Christina & Eickmeier, Sandra, 2006. "How good are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Discussion Paper Series 1: Economic Studies 2006,42, Deutsche Bundesbank, Research Centre.
    8. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
    9. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Banco de Espa�a Working Papers 0112, Banco de Espa�a.
    10. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    11. Francisco Marcos Rodrigues Figueiredo, 2010. "Forecasting Brazilian Inflation Using a Large Data Set," Working Papers Series 228, Central Bank of Brazil, Research Department.
    12. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
    13. Jan J.J. Groen & George Kapetanios, 2008. "Revisiting Useful Approaches to Data-Rich Macroeconomic Forecasting," Working Papers 624, Queen Mary University of London, School of Economics and Finance.
    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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