39 Pages Posted: 21 Nov 2007 Last revised: 25 Feb 2009
Date Written: September 19, 2007
The difficulty of conducting relevant experiments has long been regarded as the central challenge to learning about the economy from data. The standard solution, going back to Haavelmo's famous "The Probability Approach in Econometrics" (1944), involved two elements: first, it placed substantial weight on a priori theory as a source of structural information, reducing econometric estimates to measurements of causally articulated systems; second, it emphasized the need for an appropriate statistical model of the data. These elements are usually seen as tightly linked. I argue that they are, to a large extent, separable. Careful attention to the role of an empirically justified statistical model in underwriting probability explains puzzles not only in economics, but more generally with respect to recent criticisms of Reichenbach's principle of the common cause, which lies behind graph-theoretic causal search algorithms. And it provides an antidote to the pessimistic understanding of the possibilities for passive observation of causal structure in econometrics and related areas of Nancy Cartwright and others.
Keywords: econometrics, causality, causal search, graph-theory, probability, principle of the common cause, identification, Haavelmo, Reichenbach, Cartwright
JEL Classification: B41, C10, C50
Suggested Citation: Suggested Citation
Hoover, Kevin D., Probability and Structure in Econometric Models (September 19, 2007). Economics Research Initiatives at Duke (ERID) Working Paper No. 18. Available at SSRN: https://ssrn.com/abstract=1031484 or http://dx.doi.org/10.2139/ssrn.1031484