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Beating the Simple Average: Egalitarian LASSO for Combining Economic Forecasts

Author

Listed:
  • Francis X. Diebold

    (Department of Economics, University of Pennsylvania)

  • Minchul Shin

    (Department of Economics, University of Illinois)

Abstract

Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found superiority of simple-average combinations, we propose LASSO-based procedures that select and shrink toward equal combining weights. We then provide an empirical assessment of the performance of our "egalitarian LASSO" procedures. The results indicate that simple averages are highly competitive, and that although out-of-sample RMSE improvements on simple averages are possible in principle using our methods, they are hard to achieve in real time, due to the intrinsic difficulty of small-sample real-time cross validation of the LASSO tuning parameter. We therefore propose alternative direct combination procedures, most notably "best average" combination, motivated by the structure of egalitarian LASSO and the lessons learned, which do not require choice of a tuning parameter yet outperform simple averages.

Suggested Citation

  • Francis X. Diebold & Minchul Shin, 2017. "Beating the Simple Average: Egalitarian LASSO for Combining Economic Forecasts," PIER Working Paper Archive 17-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 20 Aug 2017.
  • Handle: RePEc:pen:papers:17-017
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    References listed on IDEAS

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    1. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    2. 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.
    3. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
    4. S. Boragan Aruoba & Francis X. Diebold & Jeremy J. Nalewaik & Frank Schorfheide & Dongho Song, 2011. "Improving GDP measurement: a forecast combination perspective," Working Papers 11-41, Federal Reserve Bank of Philadelphia.
    5. Diebold, Francis X. & Pauly, Peter, 1990. "The use of prior information in forecast combination," International Journal of Forecasting, Elsevier, vol. 6(4), pages 503-508, December.
    6. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015. "Optimal combination of survey forecasts," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
    7. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1, December.
    8. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    10. Yeung Lewis Chan & James H. Stock & Mark W. Watson, 1999. "A dynamic factor model framework for forecast combination," Spanish Economic Review, Springer;Spanish Economic Association, vol. 1(2), pages 91-121.
    11. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
    12. Jeremy Smith & Kenneth F. Wallis, 2009. "A Simple Explanation of the Forecast Combination Puzzle," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 331-355, June.
    13. 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.
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    Cited by:

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    3. Nir Billfeld & Moshe Kim, 2019. "Semiparametric correction for endogenous truncation bias with Vox Populi based participation decision," Papers 1902.06286, arXiv.org.
    4. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    5. Camila Figueroa S. & Michael Pedersen, 2019. "A system for forecasting Chilean cash demand – the role of forecast combinations," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(2), pages 040-068, August.

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    More about this item

    Keywords

    Forecast combination; forecast surveys; shrinkage; model selection; LASSO; regularization;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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