Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam's Window.

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.

• Publication year

  2014

• Venue

  European Economic Review

• Publication date

  2014-10-28

• Fields of study

  Mathematics, Medicine, Economics, Computer Science

• Identifiers
  DOI 10.1016/J.EUROECOREV.2015.07.013arXiv 1410.7799PMID 26917859PMCID PMC4762062
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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