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Abstract The paper explores the justification for, and application of, Bayesian priors in forecasting a vector autoregression. A nonsymmetric, random‐walk prior outperforms three alternative time‐series representations in forecasting five series of the U.S. hog market.
Bessler et al. (Sat,) studied this question.