Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald
面向决策的计量经济学：完善Haavelmo与Wald勾勒的基础AI 解读 ↓
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面向决策的计量经济学：完善Haavelmo与Wald勾勒的基础
- 研究问题
- 当计量模型只能近似现实时，如何评价基于模型作出的决策？作者关注点估计后直接优化的做法能否服务于可靠决策。
- 方法与思路
- 作者借助统计决策理论，讨论先设定模型、点估计参数再据此优化的做法，强调在所有被认为可行的自然状态中评价决策规则。
- 材料陈述的贡献
- 作者主张按可行自然状态而非仅按模型空间评价决策规则，并将这一原则用于预测与治疗选择；摘要将计算推进列为后续关键任务。
- 阅读关注点
- 核对可行自然状态、行动和损失函数如何界定，基于点估计的优化怎样接受跨状态评估，以及预测与治疗选择中的计算方案。
- 阅读边界
- 摘要未说明具体评价准则、计算算法或应用结果；不能据此判断某种决策规则普遍更优，也不能将提出的评价原则视为已完成的实证验证。
原文摘要
Haavelmo (1944) proposed a probabilistic structure for econometric modeling, aiming to make econometrics useful for decision making. His fundamental contribution has become thoroughly embedded in econometric research, yet it could not answer all the deep issues that the author raised. Notably, Haavelmo struggled to formalize the implications for decision making of the fact that models can at most approximate actuality. In the same period, Wald (1939, 1945) initiated his own seminal development of statistical decision theory. Haavelmo favorably cited Wald, but econometrics did not embrace statistical decision theory. Instead, it focused on study of identification, estimation, and statistical inference. This paper proposes use of statistical decision theory to evaluate the performance of models in decision making. I consider the common practice of as‐if optimization : specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate. A central theme is that one should evaluate as‐if optimization or any other model‐based decision rule by its performance across the state space, listing all states of nature that one believes feasible, not across the model space. I apply the theme to prediction and treatment choice. Statistical decision theory is conceptually simple, but application is often challenging. Advancing computation is the primary task to complete the foundations sketched by Haavelmo and Wald.