Projects per year
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.
Original language | English |
---|---|
Pages (from-to) | 45-69 |
Number of pages | 25 |
Journal | Advances in Econometrics |
Volume | 34 |
DOIs | |
Publication status | Published - 2014 |
Keywords
- model switching
- forecast combination
- switching state space model
- inflation forecasting
Fingerprint
Dive into the research topics of 'Model switching and model averaging in time-varying 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