Tuesday, April 8, 2014

Ambitious vs. Ambiguous Modeling


Some people believe that a conclusion reached based on solid statistically sound principles is a gold standard. But, we seldom prove anything in applied empirical analysis. This is disappointing to those that desire definitive answers.Rather than seeking proof, or absolute truth, the best we can do is inform: 

"Social scientists and policymakers alike seem driven to draw sharp conclusions, even when these can be generated only by imposing much stronger assumptions than can be defended. We need to develop a greater tolerance for ambiguity. We must face up to the fact that we cannot answer all of the questions that we ask." (Manski, 1995) 


Manski, C.F. 1995. Identification Problems in the Social Sciences. Cambridge: Harvard University Press. 

Another quote:


“…all models are approximations. Essentially, all models are wrong, but some are useful. However, the approximate nature of the model must always be borne in mind…”

— George E.P. Box

In George E. P. Box and Norman R. Draper, Empirical Model-Building and Response Surfaces (2007), 414


1 comment:

  1. Manski presented at a forum at the University of Kentucky back in 2012 on this topic - the paper is linked here http://www.nber.org/papers/w16207. I guess this general idea has been something on his agenda for many years since the similar paper you reference is 12 years earlier.

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