Goodness-of-fit in optimizing models

Abstract

Conventional econometric tests of optimizing models typically involve embedding the optimizing model in a parametric specification and then examining the parametric restrictions imposed by the optimization hypothesis. The optimization hypothesis is rejected if the estimated parameters are significantly different, in the statistical sense, from the values implied by optimization. I argue that a more fruitful approach to testing optimizing behavior is to measure the departure from optimization using the estimated objective function, and see whether this departure is significant in an economic sense. I discuss procedures for doing this that can be used in several sorts of optimizing models, and give a detailed illustration in the case of aggregate demand estimation.