The Econometrics of Piecewise-Linear Budget Constraints

Abstract

This article presents a Monte Carlo evaluation of some alternative estimators for a demand model when the budget constraint is piecewise-linear and the budget set is convex. We examine the performance of two maximum likelihood (ML) estimators and an ordinary least squares (OLS) estimator under varying sample sizes and error variances. A simple log-linear demand function, with income and price as the explanatory variables, is specified. Although I find that the OLS bias decreases as the error variance decreases, the ML results are far superior. Furthermore, statistical tests based on the OLS results lead to erroneous conclusions regarding the structure.

KEY WORDS:

• Monte Carlo experiments
• Alternative estimator performance