IDENTIFICATION AND STATISTICAL DECISION THEORY
探讨识别分析对统计决策理论的用处，发现它能给出有限样本决策准则性能的上界，并在部分识别和模糊决策下通过随机化行动选择提升准则表现。
Econometricians have usefully separated study of estimation into identification and statistical components. Identification analysis, which assumes knowledge of the probability distribution generating observable data, places an upper bound on what may be learned about population parameters of interest with finite-sample data. Yet Wald’s statistical decision theory studies decision-making with sample data without reference to identification, indeed without reference to estimation. This paper asks if identification analysis is useful to statistical decision theory. The answer is positive, as it can yield an informative and tractable upper bound on the achievable finite-sample performance of decision criteria. The reasoning is simple when the decision-relevant parameter (true state of nature) is point-identified. It is more delicate when the true state is partially identified and a decision must be made under ambiguity. Then the performance of some criteria, such as minimax regret, is enhanced by randomizing choice of an action in a controlled manner. I find it useful to recast choice of a statistical decision function as selection of choice probabilities for the elements of the choice set.