  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 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 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 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,
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.
Advancement of computation is the primary task to continue building the
foundations sketched by Haavelmo and Wald.
