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

Author

Listed:
  • 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.

Suggested Citation

  • Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data," Tinbergen Institute Discussion Papers 11-172/4, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20110172
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, Elsevier.
    2. Andrés M. Alonso & Guadalupe Bastos & Carolina García-Martos, 2016. "Electricity Price Forecasting by Averaging Dynamic Factor Models," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-21, July.
    3. Miguel Belmonte & Gary Koop, 2014. "Model Switching and Model Averaging in Time-Varying Parameter Regression Models," Advances in Econometrics,in: Bayesian Model Comparison, volume 34, pages 45-69 Emerald Publishing Ltd.
    4. 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.

    More about this item

    Keywords

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

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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