Forecasting economic activity by Bayesian bridge model averaging

Lorenzo Bencivelli, Massimiliano Marcellino and Gianluca Moretti ()
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Gianluca Moretti: Bank of America Merril Lynch

Empirical Economics, 2017, vol. 53, issue 1, No 3, 40 pages

Abstract: Abstract This paper proposes the use of Bayesian model averaging (BMA) as an alternative tool to forecast GDP relative to simple bridge models and factor 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 forecasts produce smaller forecast errors than standard bridge model when forecasting GDP in Germany, France and Italy. At the same time, it also performs as well as medium-scale factor models when forecasting Eurozone GDP.

Keywords: Now-casting; Forecasting; Bridge models; Bayesian model averaging (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s00181-016-1199-9

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