A BAYESIAN INTERPRETATION OF MULTIPLE POINT ESTIMATES
研究了大量计量经济研究中使用不同估计技术或数据子集得到的点估计的分布，在适当条件下，该分布与最小信息过程诱导的贝叶斯后验测度一致，从而便于综合实证结果进行统计决策。
Consider a large number of econometric investigations using different estimation techniques and/or different subsets of all available data to estimate a fixed set of parameters. The resulting empirical distribution of point estimates can be shown – under suitable conditions – to coincide with a Bayesian posterior measure on the parameter space induced by a minimum information procedure. This Bayesian interpretation makes it easier to combine the results of various empirical exercises for statistical decision making. The collection of estimators may be generated by one investigator to ensure the satisfaction of our conditions, or they may be collected from published works, where behavioral assumptions need to be made regarding the dependence structure of econometric studies.