How common is identification in parametric models? (Q1194024)

Identification theory characterizes those instances in which data can provide information on the parameters of a statistical model. The question of identification frequently arises in empirical analysis. The authors examine whether identification is a `common' or `rare' phenomenon in nonlinear parametric models. It is shown that the assumption of linearity in functional relationships to ease the conceptual and computational developments of a theory can drastically limit the estimability of the model. Another feature of this paper is that global identification is established in the context of nonlinear models.