Model Switching and Model Averaging in Time-Varying Parameter Regression Models
Miguel Angel Gonzalez Belmonte and
Gary Koop ()
No 1302, Working Papers from University of Strathclyde Business School, Department of Economics
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 inÃ‚â€¡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; inflÃ‚â€¡ation forecasting (search for similar items in EconPapers)
JEL-codes: C11 C52 E37 E47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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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:str:wpaper:1302
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