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Real-time forecast averaging with ALFRED

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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.

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Bibliographic Info

Article provided by Federal Reserve Bank of St. Louis in its journal Review.

Volume (Year): (2011)
Issue (Month): Jan ()
Pages: 49-66

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Handle: RePEc:fip:fedlrv:y:2011:i:jan:p:49-66:n:v.93no.1

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Keywords: Economic forecasting ; Real-time data;

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  1. Antonello D'Agostino & Domenico Giannone & Paolo Surico, 2005. "(Un)Predictability and Macroeconomic Stability," Macroeconomics 0510024, EconWPA.
  2. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-69, June.
  3. 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, 06.
  4. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
  5. 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, 05.
  6. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
  7. 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.
  8. Elliott, Graham & Timmermann, Allan, 2002. "Optimal Forecast Combination Under General Loss Functions and Forecast Error Distributions," University of California at San Diego, Economics Working Paper Series qt15r9t2q2, Department of Economics, UC San Diego.
  9. Domenico Giannone & Lucrezia Reichlin & David Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
  10. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
  11. Levin, Andrew T. & Piger, Jeremy M., 2004. "Is inflation persistence intrinsic in industrial economies?," Working Paper Series 0334, European Central Bank.
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  13. repec:fip:fedlwp:2011-005 is not listed on IDEAS
  14. Anthony Garratt & Gary Koop & ShaunP. Vahey, 2008. "Forecasting Substantial Data Revisions in the Presence of Model Uncertainty," Economic Journal, Royal Economic Society, vol. 118(530), pages 1128-1144, 07.
  15. Hansen, Bruce E., 2008. "Least-squares forecast averaging," Journal of Econometrics, Elsevier, vol. 146(2), pages 342-350, October.
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Cited by:
  1. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar, 2013. "Forecasting Aggregate Retail Sales: The Case of South Africa," Working Papers 201312, University of Pretoria, Department of Economics.
  2. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," MPRA Paper 39452, University Library of Munich, Germany.

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