Heteroscedasticity-robust model screening: A useful toolkit for model averaging in big data analytics

Tian Xie

Economics Letters, 2017, vol. 151, issue C, 119-122

Abstract: 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.

Keywords: Model screening; Model averaging; Big data analytics (search for similar items in EconPapers)
JEL-codes: C52 C53 D03 M21 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:151:y:2017:i:c:p:119-122

DOI: 10.1016/j.econlet.2016.12.019

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