Heteroscedasticity-robust model screening: a useful toolkit for model averaging in big data analytics. 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.
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References in zbMATH (referenced in 2 articles , 1 standard article )
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- Qiu, Yue; Ren, Yu; Xie, Tian: Weighing asset pricing factors: a least squares model averaging approach (2019)
- Xie, Tian: Heteroscedasticity-robust model screening: a useful toolkit for model averaging in big data analytics (2017)