This paper introduces a quantile regression estimator for panel data models
with individual heterogeneity and attrition. The method is motivated by the
fact that attrition bias is often encountered in Big Data applications...
example, many users sign-up for the latest program but few remain active users
several months later, making the evaluation of such interventions inherently
very challenging. Building on earlier work by Hausman and Wise (1979), we
provide a simple identification strategy that leads to a two-step estimation
procedure. In the first step, the coefficients of interest in the selection
equation are consistently estimated using parametric or nonparametric methods. In the second step, standard panel quantile methods are employed on a subset of
weighted observations. The estimator is computationally easy to implement in
Big Data applications with a large number of subjects. We investigate the
conditions under which the parameter estimator is asymptotically Gaussian and
we carry out a series of Monte Carlo simulations to investigate the finite
sample properties of the estimator. Lastly, using a simulation exercise, we
apply the method to the evaluation of a recent Time-of-Day electricity pricing
experiment inspired by the work of Aigner and Hausman (1980).