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

Belmonte, Miguel and Koop, Gary (2014) Model switching and model averaging in time-varying parameter regression models. Advances in Econometrics, 34. pp. 45-69. ISSN 0731-9053 (https://doi.org/10.1108/S0731-905320140000034004)

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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.

ORCID iDs

Belmonte, Miguel and Koop, Gary ORCID logoORCID: https://orcid.org/0000-0002-6091-378X;
  • Item type:Article
    ID code:53176
    Dates:
    Date
    Event
    2014
    Published
    14 August 2014
    Accepted
    Notes: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.
    Subjects:Social Sciences > Economic Theory
    Department:Strathclyde Business School > Economics
    Depositing user: Pure Administrator
    Date deposited:02 Jun 2015 09:36
    Last modified:11 Nov 2024 10:48
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/53176
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