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Forecasting Data Vintages

  • Tara M. Sinclair

    ()

    (George Washington University)

This article provides a discussion of Clements and Galvão’s “Forecasting with Vector Autoregressive Models of Data Vintages: US output growth and inflation.” Clements and Galvão argue that a multiple-vintage VAR model can be useful for forecasting data that are subject to revisions. Clements and Galvão draw a “distinction between forecasting future observations and revisions to past data,” which brings yet another real time data issue to the attention of forecasters. This comment discusses the importance of taking data revisions into consideration and compares the multiple-vintage VAR approach of Clements and Galvão to a state-space approach.

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File URL: http://www.gwu.edu/~forcpgm/2012-001.pdf
File Function: First version, 2012
Download Restriction: no

Paper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2012-001.

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Length: 8 pages
Date of creation: Jan 2012
Date of revision:
Handle: RePEc:gwc:wpaper:2012-001
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  1. Evan Koenig & Sheila Dolmas & Jeremy M. Piger, 2002. "The use and abuse of 'real-time' data in economic forecasting," Working Papers 2001-015, Federal Reserve Bank of St. Louis.
  2. N. Kundan Kishor & Evan F. Koenig, 2009. "VAR Estimation and Forecasting When Data Are Subject to Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 181-190, July.
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