IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

Real-time forecast averaging with ALFRED

  • Chanont Banternghansa
  • Michael W. McCracken

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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://research.stlouisfed.org/publications/review/11/01/37-48Basu.pdf
Download Restriction: no

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

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

as
in new window

Handle: RePEc:fip:fedlrv:y:2011:i:jan:p:49-66:n:v.93no.1
Contact details of provider: Postal: P.O. Box 442, St. Louis, MO 63166
Fax: (314)444-8753
Web page: http://www.stlouisfed.org/

More information through EDIRC

Order Information: Web: http://www.stls.frb.org/research/order/pubform.html Email:


References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
  2. Kapetanios, G. & Labhard, V. & Price, S., 2007. "Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation," Working Papers 07/15, Department of Economics, City University London.
  3. Levin, Andrew T. & Piger, Jeremy M., 2004. "Is inflation persistence intrinsic in industrial economies?," Working Paper Series 0334, European Central Bank.
  4. Antonello D'Agostino & Domenico Giannone & Paolo Surico, 2005. "(Un)Predictability and Macroeconomic Stability," Macroeconomics 0510024, EconWPA.
  5. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
  6. Kilian, Lutz, 2006. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," CEPR Discussion Papers 5994, C.E.P.R. Discussion Papers.
  7. Anthony Garratt & Gary Koop & Shaun P. Vahey, 2006. "Forecasting Substantial Data Revisions in the Presence of Model Uncertainty," Birkbeck Working Papers in Economics and Finance 0617, Birkbeck, Department of Economics, Mathematics & Statistics.
  8. Todd E. Clark & Michael W. McCracken, 2006. "Averaging forecasts from VARs with uncertain instabilities," Research Working Paper RWP 06-12, Federal Reserve Bank of Kansas City.
  9. Hansen, Bruce E., 2008. "Least-squares forecast averaging," Journal of Econometrics, Elsevier, vol. 146(2), pages 342-350, October.
  10. Todd E. Clark & Michael W. McCracken, 2004. "Improving forecast accuracy by combining recursive and rolling forecasts," Research Working Paper RWP 04-10, Federal Reserve Bank of Kansas City.
  11. 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.
  12. 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.
  13. 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.
  14. Aiolfi, Marco & Timmermann, Allan, 2006. "Persistence in forecasting performance and conditional combination strategies," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 31-53.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:fip:fedlrv:y:2011:i:jan:p:49-66:n:v.93no.1. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anna Xiao)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.