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Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance

Author

Listed:
  • Roberto Casarin

    (University Ca' Foscari of Venice)

  • Stefano Grassi

    (University of Kent, United Kingdom)

  • Francesco Ravazzolo

    (Norges Bank, Norway)

  • Herman K. van Dijk

    (VU University Amsterdam, Erasmus University Rotterdam, the Netherlands)

Abstract

A Bayesian semi-parametric dynamic model combination is proposed in order to deal with a large set of predictive densities. It extends the mixture of experts and the smoothly mixing regression models by allowing combination weight dependence between models as well as over time. It introduces an information reduction step by using a clustering mechanism that allocates the large set of predictive densities into a smaller number of mutually exclusive subsets. The complexity of the approach is further reduced by making use of the class-preserving property of the logistic-normal distribution that is specified in the compositional dynamic factor model for the weight dynamics with latent factors defined on a reduced dimension simplex. The whole model is represented as a nonlinear state space model that allows groups of predictive models with corresponding combination weights to be updated with parallel clustering and sequential Monte Carlo filters. The approach is applied to predict Standard & Poor’s 500 index using more than 7000 predictive densities based on US individual stocks and finds substantial forecast and economic gains. Similar forecast gains are obtained in point and density forecasting of US real GDP, Inflation, Treasury Bill yield and employment using a large data set.

Suggested Citation

  • Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2016. "Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance," Tinbergen Institute Discussion Papers 15-084/III, Tinbergen Institute, revised 03 Jul 2017.
  • Handle: RePEc:tin:wpaper:20150084
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).
    2. Leopoldo Catania, 2016. "Dynamic Adaptive Mixture Models," Papers 1603.01308, arXiv.org.
    3. repec:gam:jecnmx:v:4:y:2016:i:1:p:17:d:65855 is not listed on IDEAS
    4. Nalan Baştürk & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2016. "Computational Complexity and Parallelization in Bayesian Econometric Analysis," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-3, February.
    5. Roberto Casarin & Giulia Mantoan & Francesco Ravazzolo, 2016. "Bayesian Calibration of Generalized Pools of Predictive Distributions," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-24, March.
    6. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Time-varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies," Tinbergen Institute Discussion Papers 16-099/III, Tinbergen Institute.
    7. repec:gam:jecnmx:v:4:y:2016:i:1:p:9:d:64209 is not listed on IDEAS

    More about this item

    Keywords

    Density Combination; Large Set of Predictive Densities; Compositional Factor Models; Nonlinear State Space; Bayesian Inference; GPU Computing;

    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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