Forecasting Data Vintages
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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- Koenig, Evan F. & Dolmas, Sheila & Piger, Jeremy M., 2000.
"The use and abuse of "real-time" data in economic forecasting,"
0004, Federal Reserve Bank of Dallas.
- Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2003. "The Use and Abuse of Real-Time Data in Economic Forecasting," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 618-628, August.
- Evan F. Koenig & Sheila Dolmas & Jeremy M. Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," International Finance Discussion Papers 684, Board of Governors of the Federal Reserve System (U.S.).
- Evan F. 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.
- Kishor, N. Kundan & Koenig, Evan F., 2005.
"VAR estimation and forecasting when data are subject to revision,"
0501, Federal Reserve Bank of Dallas.
- 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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