Jackknife model averaging for expectile regressions in increasing dimension

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. (C) 2020 Elsevier B.V. All rights reserved.