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Model uncertainty and Bayesian model averaging in vector autoregressive processes

By Rodney W. Strachan and Herman K. van Dijk


Economic forecasts and policy decisions are often informed by empiri-\ud cal analysis based on econometric models. However, inference based upon\ud a single model, when several viable models exist, limits its usefulness. Tak-\ud ing account of model uncertainty, a Bayesian model averaging procedure is\ud presented which allows for unconditional inference within the class of vector\ud autoregressive (VAR) processes. Several features of VAR process are investi-\ud gated. Measures on manifolds are employed in order to elicit uniform priors\ud on subspaces de ned by particular structural features of VARs. The features\ud considered are the number and form of the equilibrium economic relations\ud and deterministic processes. Posterior probabilities of these features are used\ud in a model averaging approach for forecasting and impulse response analysis.\ud The methods are applied to investigate stability of the Great Ratios in\ud U.S. consumption, investment and income, and the presence and e¤ects of\ud permanent shocks in these series. The results obtained indicate the feasibility\ud of the proposed method

Topics: Posterior probability, Grassman manifold, Orthogonal group, Cointegration, Model averaging, Stochastic trend, Impulse response, Vector autoregressive model.
Publisher: Dept. of Economics, University of Leicester
Year: 2006
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