Bayesian Estimates for Vector - Autoregressive Models
نویسندگان
چکیده
This paper examines frequentist risks of Bayesian estimates of VAR regression coefficient and error covariance matrices under competing loss functions, under a variety of non-informative priors, and in the normal and Student-t models. Simulation results show that for the regression coefficient matrix an asymmetric LINEX estimator does better overall than the posterior mean. For the error covariance matrix no dominating estimator emerges. We find that the choice of prior has a more significant effect on the estimates than the form of estimator. For the VAR regression coefficients, a shrinkage prior dominates a constant prior. For the error covariance matrix, Yang and Berger’s reference prior dominates the Jeffreys prior. Estimation of a VAR using U.S. macroeconomic data yields significantly different estimates under competing priors.
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