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

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  • Clements, Michael P.
  • Galvão, Ana Beatriz

Abstract

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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Bibliographic Info

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

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Web page: http://www.elsevier.com/locate/ijforecast

Related research

Keywords: Data revisions; Forecasting; Data uncertainty;

References

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Cited by:
  1. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.

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