Model uncertainty and Bayesian model averaging in vector autoregressive processes

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