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A1029
Title: A panel quantile approach to attrition bias in big data: Evidence from a randomized experiment Authors:  Matthew Harding - University of California, Irvine (United States) [presenting]
Carlos Lamarche - University of Kentucky (United States)
Abstract: A quantile regression estimator is introduced 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 problems. For example, many users sign-up for the latest utility program but few remain active users several months later, making the evaluation of such interventions inherently very challenging. Building on an earlier work, 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. We explore an application to the evaluation of a recent Time-of-Day electricity pricing experiment.