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Real-time forecast averaging with ALFRED

Author

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  • Chanont Banternghansa
  • Michael W. McCracken

Abstract

This paper presents empirical evidence on the efficacy of forecast averaging using the ALFRED (ArchivaL Federal Reserve Economic Data) real-time database. We consider averages over a variety of bivariate vector autoregressive models. These models are distinguished from one another based on at least one of the following factors: (i) the choice of variables used as predictors, (ii) the number of lags, (iii) use of all available data or only data after the Great Moderation, (iv) the observation window used to estimate the model parameters and construct averaging weights, and (v) for the forecast horizons greater than one, the use of either iterated multistep or direct multistep methods. A variety of averaging methods are considered. The results indicate that the benefits of model averaging relative to Bayesian information criterion-based model selection are highly dependent on the class of models averaged The authors provide a novel decomposition of the forecast improvements that allows determination of the most (and least) helpful types of averaging methods and models averaged across.

Suggested Citation

  • Chanont Banternghansa & Michael W. McCracken, 2011. "Real-time forecast averaging with ALFRED," Review, Federal Reserve Bank of St. Louis, issue Jan, pages 49-66.
  • Handle: RePEc:fip:fedlrv:y:2011:i:jan:p:49-66:n:v.93no.1
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    References listed on IDEAS

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    1. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29.
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    3. Todd E. Clark & Michael W. McCracken, 2009. "Improving Forecast Accuracy By Combining Recursive And Rolling Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(2), pages 363-395, May.
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    Cited by:

    1. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    2. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," MPRA Paper 39452, University Library of Munich, Germany.

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    Keywords

    Economic forecasting ; Real-time data;

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