Model Switching and Model Averaging in Time-Varying Parameter Regression Models
Abstract
This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selection (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 method for implementing 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 inflation forecasting application. We find strong evidence of model switching. We also compare different ways of implementing DMA/DMS and find forgetting factor approaches and approaches based on the switching Gaussian state space model to lead to similar results.
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Acknowledgements
Acknowledgments
This research was supported by the ESRC under grant RES-062-23-2646. Gary Koop is a Fellow at the Rimini Centre for Economic Analysis.
Citation
Belmonte, M. and Koop, G. (2014), "Model Switching and Model Averaging in Time-Varying Parameter Regression Models", Bayesian Model Comparison (Advances in Econometrics, Vol. 34), Emerald Group Publishing Limited, Leeds, pp. 45-69. https://doi.org/10.1108/S0731-905320140000034004
Publisher
:Emerald Group Publishing Limited
Copyright © 2014 Emerald Group Publishing Limited