This article studies optimal model averaging for partially linear models with heteroscedasticity. A Mallows-type criterion is proposed to choose the weight. The resulting model averaging estimator is proved to be asymptotically optimal under some regularity conditions. Simulation experiments suggest that the proposed model averaging method is superior to other commonly used model selection and averaging methods. The proposed procedure is further applied to study Japan’s sovereign credit default swap spreads.

Additional Metadata
Keywords Asymptotic optimality, Heteroscedasticity, Model averaging, Partially linear model
Persistent URL dx.doi.org/10.5705/ss.202015.0392, hdl.handle.net/1765/119632
Journal Statistica Sinica
Citation
Zhang, X. (Xinyu), & Wang, W. (2019). Optimal model averaging estimation for partially linear models. Statistica Sinica, 29(2), 693–718. doi:10.5705/ss.202015.0392