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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 timevarying 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 timevarying parameter regression model to change over time. DMA will carry out model averaging in a timevarying 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 language  English 

Place of Publication  Glasgow 
Publisher  University of Strathclyde 
Pages  125 
Number of pages  26 
Volume  13 
Publication status  Published  Jan 2013 
Keywords
 model switching
 forecast combination
 switching state space model
 inflation forecasting
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Dive into the research topics of 'Model Switching and Model Averaging in TimeVarying Parameter Regression Models'. Together they form a unique fingerprint.Projects
 1 Finished

Macroeconomic Forecasting in Turbulent Times
ESRC (Economic and Social Research Council)
1/10/10 → 30/09/13
Project: Research