Abstract
| Language | English |
|---|---|
| Pages | 45-69 |
| Number of pages | 25 |
| Journal | Advances in Econometrics |
| Volume | 34 |
| DOIs | |
| Publication status | Published - 2014 |
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Keywords
- model switching
- forecast combination
- switching state space model
- inflation forecasting
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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 journal › Article
TY - JOUR
T1 - Model switching and model averaging in time-varying parameter regression models
AU - Belmonte, Miguel
AU - Koop, Gary
N1 - This article is (c) Emerald Group Publishing and permission has been granted for this version to appear on http://strathprints.strath.ac.uk/ and http://pure.strath.ac.uk/portal/. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - model switching
KW - forecast combination
KW - switching state space model
KW - inflation forecasting
UR - http://www.emeraldinsight.com/series/aeco
U2 - 10.1108/S0731-905320140000034004
DO - 10.1108/S0731-905320140000034004
M3 - Article
VL - 34
SP - 45
EP - 69
JO - Advances in Econometrics
T2 - Advances in Econometrics
JF - Advances in Econometrics
SN - 0731-9053
ER -