Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Frequentist model averaging has been demonstrated as an efficient tool to deal with model uncertainty in big data analysis. In contrast with a conventional data set, the number of regressors in a big data set is usually quite large, which leads to a exponential number of potential candidate models. In this paper, we propose a heteroscedasticity-robust model screening (HRMS) method that constructs a candidate model set through an iterative procedure. Our simulation results and empirical exercise with big data analytics demonstrate the superiority of our HRMS method over existing methods.