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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

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.

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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 28 (2013)
Issue (Month): 1 (01)
Pages: 62-81

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Handle: RePEc:wly:japmet:v:28:y:2013:i:1:p:62-81
Contact details of provider: Web page: http://www.interscience.wiley.com/jpages/0883-7252/

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  1. Giordani, Paolo & Kohn, Robert, 2008. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 66-77, January.
  2. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, December.
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