Dynamic model averaging in large model spaces using dynamic Occam׳s window

Luca Onorante and Adrian E. Raftery

European Economic Review, 2016, vol. 81, issue C, 2-14

Abstract: Bayesian model averaging has become a widely used approach to accounting for uncertainty about the structural form of the model generating the data. When data arrive sequentially and the generating model can change over time, Dynamic Model Averaging (DMA) extends model averaging to deal with this situation. Often in macroeconomics, however, many candidate explanatory variables are available and the number of possible models becomes too large for DMA to be applied in its original form. We propose a new method for this situation which allows us to perform DMA without considering the whole model space, but using a subset of models and dynamically optimizing the choice of models at each point in time. This yields a dynamic form of Occam׳s window. We evaluate the method in the context of the problem of nowcasting GDP in the Euro area. We find that its forecasting performance compares well with that of other methods.

Keywords: Bayesian model averaging; Model uncertainty; Nowcasting; Occam׳s window; Econometric Modeling (search for similar items in EconPapers)
JEL-codes: C5 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25)

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Working Paper: Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam's Window (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eecrev:v:81:y:2016:i:c:p:2-14

DOI: 10.1016/j.euroecorev.2015.07.013

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