On using interval response data in experimental economics

Many empirical applications in the experimental economics literature involve interval response data. Various methods have been considered to treat this type of data. One approach assumes that the data correspond to the interval midpoint and then utilizes ordinary least squares to estimate the model. Another approach is to use maximum likelihood estimation, assuming that the underlying variable of interest is normally distributed. In the case of distributional misspecification, these estimation approaches can yield inconsistent estimators. In this paper, we explore a method that can help reduce the misspecification problem by assuming a distribution that can model a wide variety of distributional characteristics, including possible heteroskedasticity. The method is applied to the problem of estimating the impact of various explanatory factors associated with individual discount rates in a field experiment. Our analysis suggests that the underlying distribution of discount rates exhibits skewness, but not heteroskedasticity, In this example, the findings based on a normal distribution are generally robust across distributions.