Jackknife model averaging for expectile regressions in increasing dimension

Yundong Tu and Siwei Wang

Economics Letters, 2020, vol. 197, issue C

Abstract: Expectile regression is a useful tool for modeling data with heterogeneous conditional distributions. This paper develops the jackknife model averaging method for expectile regressions. The asymptotic properties of expectile estimator under misspecification with increasing dimension of parameters have been studied. The model averaging expectile estimator using the leave-one-out cross-validated weight is shown to be asymptotically optimal in the sense of out-of-sample final prediction error. Numerical results demonstrate the nice performance of the averaging estimators.

Keywords: Expectile regression; Heteroscedasticity; Jackknife model averaging; High dimensional data (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:197:y:2020:i:c:s0165176520303670

DOI: 10.1016/j.econlet.2020.109607

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