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Selecting predictors by using Bayesian model averaging in bridge models

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Author Info

  • 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.

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Bibliographic Info

Paper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 872.

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Date of creation: Jul 2012
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Handle: RePEc:bdi:wptemi:td_872_12

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Related research

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

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Cited by:
  1. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2013. "Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility," CEPR Discussion Papers 9312, C.E.P.R. Discussion Papers.

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