Choice of structural model via parsimony: A rationale based on precision.

It is shown that, in large samples, the more parsimonious of two competing nested models yields an estimator of the common parameters that has smaller sampling variance.

Abstract

It is shown that, in large samples, the more parsimonious of two competing nested models yields an estimator of the common parameters that has smaller sampling variance. The use of parsimony as a criterion for choice between two otherwise acceptable models can thus be rationalized on the basis of precision of estimation.