A Panel Quantile Approach to Attrition Bias in Big Data: Evidence from a Randomized Experiment
This addresses attrition bias in Big Data applications, such as evaluating interventions with high user dropout, but it is incremental as it builds on earlier work by Hausman and Wise (1979).
The paper tackles attrition bias in Big Data by introducing a quantile regression estimator for panel data with individual heterogeneity, providing a two-step estimation procedure that is computationally easy to implement. It demonstrates the method's effectiveness through Monte Carlo simulations and an application to a Time-of-Day electricity pricing experiment.
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. For 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).