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Selecting predictors by using Bayesian model averaging in bridge models

Author

Listed:
  • Lorenzo Bencivelli

    () (Bank of Italy)

  • Massimiliano Marcellino

    () (European University Institute, Bocconi University and CEPR)

  • Gianluca Moretti

    () (UBS Global asset management)

Abstract

This paper proposes the use of Bayesian model averaging (BMA) as a tool to select the predictors' set for bridge models. BMA is a computationally feasible method that allows us to explore the model space even in the presence of a large set of candidate predictors. We test the performance of BMA in now-casting by means of a recursive experiment for the euro area and the three largest countries. This method allows flexibility in selecting the information set month by month. We find that BMA based bridge models produce smaller forecast error than fixed composition bridges. In an application to the euro area they perform at least as well as medium-scale factor models.

Suggested Citation

  • Lorenzo Bencivelli & Massimiliano Marcellino & Gianluca Moretti, 2012. "Selecting predictors by using Bayesian model averaging in bridge models," Temi di discussione (Economic working papers) 872, Bank of Italy, Economic Research and International Relations Area.
  • Handle: RePEc:bdi:wptemi:td_872_12
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    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2012/2012-0872/en_tema_872.pdf
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    Citations

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    Cited by:

    1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    3. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.

    More about this item

    Keywords

    business cycle analysis; forecasting; Bayesian model averaging; bridge models.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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