【摘要】Forecasting performance of spatial versus non-spatial Bayesian priors applied to a large vector autoregressive model that includes the 48 lower US states plus and the District of Columbia is explored. Accuracy of one- to six-quarter-ahead personal income forecasts is compared for a model based on the Minnesota prior used in macroeconomic forecasting and a spatial prior proposed by Krivelyova and LeSage (J Reg Sci 39(2):297-317; ). While the Minnesota prior emphasizes time dependence taking the form of a random walk; the spatial prior relies on past values of neighboring state income growth rates while ignoring own-state past income growth. Our findings indicate that forecast accuracy for longer future time horizons is improved by the spatial prior; while that for shorter horizons is better for the non-spatial prior. This motivated a hybrid approach that combines both spatial and time dependence in the prior restrictions placed on the model parameters. 

【文献来源】LeSage J P;Cashell B A.Annals of regional Science.2015（2）