Additive General Error Models for Production, Cost, and Derived Demand or Share Systems

Abstract

Many empirical studies of production specify a deterministic model of the firm, derive the implied behavioral equations (input demand or share system), and then "embed this system in a stochastic framework" by tacking on linear error terms. In contrast, this paper proposes general error models (GEMs) in which the error specification is an integral part of the optimization model. These models are the statistical embodiment of Stigler's view that apparent observed inefficiencies reflect the investigator's ignorance of the true optimization problem. Additive GEMs are proposed and interpreted. Specification tests indicate that a translog additive GEM is superior to the standard translog specification.