Nowcasting French GDP in Real-Time from Survey Opinions: Information or Forecast Combinations ?

Frédérique Bec and Matteo Mogliani

No 2013-21, Working Papers from Center for Research in Economics and Statistics

Abstract: This paper investigates the predictive accuracy of two alternative forecasting strategies, namely the forecast and information combinations. Theoretically, there should be no role for forecast combinations in a world where information sets can be instantaneously and costlessly combined. However, following some recent works which claim that this result holds in population but not necessarily in small samples, our paper questions this postulate empirically in a real-time and mixed-frequency framework. An application to the quarterly growth rate of French GDP reveals that, given a set of predictive models involving coincident indicators, a simple average of individual forecasts outperforms the individual forecasts, as long as no individual model encompasses the others. Furthermore, the simple average of individual forecasts outperforms, or it is statistically equivalent to, more sophisticated forecast combination schemes. However, when a predictive encompassing model is obtained by combining information sets, this model outperforms the most accurate forecast combination strategy

Keywords: Forecast Combinations; Pooling Information; Macroeconomic Nowcasting; Real-time data; Mixed-frequency data (search for similar items in EconPapers)
JEL-codes: C22 C52 E37 (search for similar items in EconPapers)
Pages: 34
Date: 2013-12
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information? (2015)
Working Paper: Nowcasting French GDP in Real-Time from Survey Opinions: Information or Forecast Combinations? (2013)
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