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

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.