FORECASTING VECTOR AUTOREGRESSIONS WITH BAYESIAN PRIORS
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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. 1986 American Agricultural Economics Association.