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Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data

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Author Info

  • Monica Billio

    (University of Venice)

  • Roberto Casarin

    (University of Venice)

  • Francesco Ravazzolo

    (Norges Bank)

  • Herman K. van Dijk

    (Erasmus University Rotterdam, VU University Amsterdam)

Abstract

We propose a multivariate combination approach to prediction based on a distributional state space representation of the weights belonging to a set of Bayesian predictive densities which have been obtained from alternative models. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models are individually misspecified. The approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of US macroeconomic time series and surveys of stock market prices. For the macro series we find that incompleteness of the models is relatively large in the 70's, the beginning of the 80's and during the recent financial crisis; structural changes like the Great Moderation are empirically identified by our model combination and the predicted probabilities of recession accurately compare with the NBER business cycle dating. Model weights have substantial uncertainty attached and neglecting this may seriously affect results. With respect to returns of the S&P 500 series, we find that an investment strategy using a combination of predictions from professional forecasters and from a white noise model puts more weight on the white noise model in the beginning of the 90's and switches to giving more weight to the left tail of the professional forecasts during the start of the financial crisis around 2008.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 11-172/4.

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Date of creation: 01 Dec 2011
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Handle: RePEc:dgr:uvatin:20110172

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Web page: http://www.tinbergen.nl

Related research

Keywords: Density Forecast Combination; Survey Forecast; Nonlinear Filtering; Sequential Monte Carlo;

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References

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Citations

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Cited by:
  1. Miguel, Belmonte & Gary, Koop, 2013. "Model Switching and Model Averaging in Time- Varying Parameter Regression Models," SIRE Discussion Papers 2013-34, Scottish Institute for Research in Economics (SIRE).
  2. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2012. "Combination schemes for turning point predictions," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 402-412.

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