Model uncertainty and Bayesian model averaging in vector autoregressive processes

Rodney Strachan and Herman van Dijk

No EI 2006-08, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: Economic forecasts and policy decisions are often informed by empirical analysis based on econometric models. However, inference based upon a single model, when several viable models exist, limits its usefulness. Taking 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 investigated. Measures on manifolds are employed in order to elicit uniform priors on subspaces defined 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 effects of permanent shocks in these series. The results obtained indicate the feasibility of the proposed method.

Keywords: Grassman manifold; cointegration; impulse response; model averaging; orthogonal group; posterior probability; stochastic trend; vector autoregressive model (search for similar items in EconPapers)
JEL-codes: C11 C32 C52 (search for similar items in EconPapers)
Date: 2006-02-03
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Model Uncertainty and Bayesian Model Averaging in Vector Autoregressive Processes (2006)
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