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Haavelmo (1944) proposed a probabilistic structure for econometric modeling,aiming to make econometrics useful for decision making. His fundamentalcontribution has become thoroughly embedded in subsequent 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 thefact that models can at most approximate actuality. In the same period, Wald(1939, 1945) initiated his own seminal development of statistical decisiontheory. Haavelmo favorably cited Wald, but econometrics did not embracestatistical decision theory. Instead, it focused on study of identification,estimation, and statistical inference. This paper proposes statistical decisiontheory as a framework for evaluation of the performance of models in decisionmaking. I particularly consider the common practice of as-if optimization:specification of a model, point estimation of its parameters, and use of thepoint estimate to make a decision that would be optimal if the estimate wereaccurate. A central theme is that one should evaluate as-if optimization or anyother model-based decision rule by its performance across the state space,listing all states of nature that one believes feasible, not across the modelspace. I apply the theme to prediction and treatment choice. Statisticaldecision theory is conceptually simple, but application is often challenging.Advancement of computation is the primary task to continue building thefoundations sketched by Haavelmo and Wald.

Econometrics For Decision Making: Building Foundations Sketched By Haavelmo And Wald