Introduction

Abstract

Whenever a neoclassical direct utility function is in close (even perfect) agreement with consumer behavior data, there is always an alternative direct utility function that agrees at least as closely with the same data. By ‘agreement’ we mean that a real sample S of consumer data assigns significant likelihood ratio support to the parameters of utility function in question when the latter is tested as a null hypothesis nested within a broader class of utility functions.¹ Existence of this equally well (if not better) fitting alternative to such a neoclassical direct utility function has considerable significance for the rational conduct of potential problem analysis in the policy-making arena. [We shall give important examples later in this book.]