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Forecasting with vector autoregressive models of data vintages: US output growth and inflation

  • Clements, Michael P.
  • Galvão, Ana Beatriz

Vintage-based vector autoregressive models of a single macroeconomic variable are shown to be a useful vehicle for obtaining forecasts of different maturities of future and past observations, including estimates of post-revision values. The forecasting performance of models which include information on annual revisions is superior to that of models which only include the first two data releases. However, the empirical results indicate that a model which reflects the seasonal nature of data releases more closely does not offer much improvement over an unrestricted vintage-based model which includes three rounds of annual revisions.

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Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 29 (2013)
Issue (Month): 4 ()
Pages: 698-714

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Handle: RePEc:eee:intfor:v:29:y:2013:i:4:p:698-714
Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

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  20. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
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