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

Miguel Belmonte, Gary Koop

Research output: Working paperDiscussion paper

4 Downloads (Pure)


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 di§erent 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 di§erent predictors in an ináation forecasting application. We also compare di§erent ways of implementing DMA/DMS and investigate whether they lead to similar results.
Original languageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Number of pages26
Publication statusPublished - Jan 2013


  • model switching
  • forecast combination
  • switching state space model
  • inflation forecasting


Dive into the research topics of 'Model Switching and Model Averaging in Time-Varying Parameter Regression Models'. Together they form a unique fingerprint.

Cite this