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Selecting and combining experts from survey forecasts

  • Julieta Fuentes

    ()

  • Pilar Poncela

    ()

  • Julio Rodríguez

    ()

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    Combining multiple forecasts provides gains in prediction accuracy. Therefore, with the aim of finding an optimal weighting scheme, several combination techniques have been proposed in the forecasting literature. In this paper we propose the use of sparse partial least squares (SPLS) as a method to combine selected individual forecasts from economic surveys. SPLS chooses the forecasters with more predictive power about the target variable, discarding the panelists with redundant information. We employ the Survey of Professional Forecasters dataset to explore the performance of different methods for combining forecasts: average forecasts, trimmed mean, regression based methods and regularized methods also in regression. The results show that selecting and combining forecasts yields to improvements in forecasting accuracy compared to the hard to beat average of forecasters.

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    File URL: http://e-archivo.uc3m.es/bitstream/handle/10016/18708/ws140905.pdf?sequence=1
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    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws140905.

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    Date of creation: Mar 2014
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    Handle: RePEc:cte:wsrepe:ws140905
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    1. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York.
    2. Poncela, Pilar & Rodríguez, Julio & Sánchez-Mangas, Rocío & Senra, Eva, 2011. "Forecast combination through dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 27(2), pages 224-237.
    3. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," CREATES Research Papers 2010-21, School of Economics and Management, University of Aarhus.
    4. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    5. Jon D. Samuels & Rodrigo Sekkel, 2013. "Forecasting with Many Models: Model Confidence Sets and Forecast Combination," Working Papers 13-11, Bank of Canada.
    6. Massimiliano Marcellino, 2004. "Forecast Pooling for European Macroeconomic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 91-112, 02.
    7. Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan, 2013. "Combining expert forecasts: Can anything beat the simple average?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 108-121.
    8. Hyonho Chun & Sündüz Keleş, 2010. "Sparse partial least squares regression for simultaneous dimension reduction and variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 3-25.
    9. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2012. "Optimal Combination of Survey Forecasts," CEPR Discussion Papers 9096, C.E.P.R. Discussion Papers.
    10. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46 National Bureau of Economic Research, Inc.
    11. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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