Model Switching and Model Averaging in Time- Varying Parameter Regression Models

Belmonte Miguel and Gary Koop

No 2013-34, SIRE Discussion Papers from Scottish Institute for Research in Economics (SIRE)

Abstract: This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

Keywords: Model switching; forecast combination; switching state space model; inflation forecasting (search for similar items in EconPapers)
Date: 2013
New Economics Papers: this item is included in nep-ets and nep-for
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Citations: View citations in EconPapers (1)

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Related works:
Chapter: Model Switching and Model Averaging in Time-Varying Parameter Regression Models (2014)
Working Paper: Model Switching and Model Averaging in Time-Varying Parameter Regression Models (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:edn:sirdps:440

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