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.
- Alternative estimator performance
- Monte carlo experiments
ASJC Scopus subject areas
- Statistics and Probability
- Social Sciences (miscellaneous)
- Economics and Econometrics
- Statistics, Probability and Uncertainty