Model switching and model averaging in time-varying parameter regression models

Miguel Belmonte, Gary Koop

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.
LanguageEnglish
Pages45-69
Number of pages25
JournalAdvances in Econometrics
Volume34
DOIs
Publication statusPublished - 2014

Fingerprint

Regression model
Model averaging
Time-varying parameters
Model selection
Switching models
Forgetting
Factors
State-space model
Time-varying
Usefulness
Inflation forecasting
Predictors
Approximation

Keywords

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

Cite this

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Model switching and model averaging in time-varying parameter regression models. / Belmonte, Miguel; Koop, Gary.

In: Advances in Econometrics, Vol. 34, 2014, p. 45-69.

Research output: Contribution to journalArticle

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