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来源类型Working Paper
规范类型报告
DOI10.3386/w26596
来源IDWorking Paper 26596
Econometrics For Decision Making: Building Foundations Sketched By Haavelmo And Wald
Charles F. Manski
发表日期2019-12-30
出版年2019
语种英语
摘要In the early 1940s, Haavelmo proposed a probabilistic structure for econometric modeling, aiming to make econometrics useful for public decision making. His fundamental contribution has become thoroughly embedded in subsequent econometric research, yet it could not fully 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 initiated his own seminal development of statistical decision theory. Haavelmo favorably cited Wald, but econometrics subsequently did not embrace statistical decision theory. Instead, it focused on study of identification, estimation, and statistical inference. This paper proposes statistical decision theory as a framework for evaluation of the performance of models in decision making. I particularly 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, not the model space. I use prediction and treatment choice to illustrate. Statistical decision theory is conceptually simple, but application is often challenging. Advancement of computation is the primary task to continue building the foundations sketched by Haavelmo and Wald.
主题Other ; History of Economic Thought ; Econometrics ; Estimation Methods
URLhttps://www.nber.org/papers/w26596
来源智库National Bureau of Economic Research (United States)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/584270
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Charles F. Manski. Econometrics For Decision Making: Building Foundations Sketched By Haavelmo And Wald. 2019.
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