Valuing structure, model uncertainty and model averaging in vector autoregressive processes

Economic policy decisions are often informed by empirical analysis based on accurate econometric modeling. However, a decision-maker is usually only interested in good estimates of outcomes, while an analyst must also be interested in estimating the model. Accurate inference on structural features of a model improves policy analysis as it improves estimation, inference and forecast efficiency. In this paper a Bayesian inferential procedure is presented which allows for unconditional inference on structural features of vector autoregressive (VAR) processes. We employ measures on manifolds in order to elicit uniform priors on subspaces defined by particular structural features of VARs. The features considered are cointegration, exogeneity, deterministic processes and overidentification. Posterior probabilities of these features are used in a model averaging approach for forecasting and impulse response analysis. The methods are applied to three empirical economic issues: stability of Australian money demand; relative weights of permanent and transitory shocks in a US real business cycle model; and possible evidence on an inflationary oil price shock and a liquidity trap in a UK macroeconomic model. The results obtained illustrate the feasibility of the proposed methods.