Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy

Markus Jochmann, Gary Koop, Roberto Leon-Gonzalez, Rodney W. Strachan

Research output: Working paperDiscussion paper

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Abstract

This paper develops methods for Stochastic Search Variable Selection
(currently popular with regression and Vector Autoregressive models)
for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model
selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic
model.
Original languageEnglish
Place of PublicationGlasgow
PublisherUniversity of Strathclyde
Pages1-46
Number of pages47
Volume04
Publication statusPublished - Sep 2009

Keywords

  • bayesian
  • cointegration
  • model averaging
  • model selection
  • Markov chain Monte Carlo

Cite this

Jochmann, M., Koop, G., Leon-Gonzalez, R., & Strachan, R. W. (2009). Stochastic Search Variable Selection in Vector Error Correction Models with an Application to a Model of the UK Macroeconomy. (19 ed.) (pp. 1-46). University of Strathclyde.