Optimal Model Averaging Estimation for the Varying-Coefficient Partially Linear Models with Missing Responses

Jie Zeng, Weihu Cheng and Guozhi Hu ()
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Jie Zeng: School of Mathematics and Statistics, Hefei Normal University, Hefei 230601, China
Weihu Cheng: Faculty of Science, Beijing University of Technology, Beijing 100124, China
Guozhi Hu: School of Mathematics and Statistics, Hefei Normal University, Hefei 230601, China

Mathematics, 2023, vol. 11, issue 8, 1-21

Abstract: In this paper, we propose a model averaging estimation for the varying-coefficient partially linear models with missing responses. Within this context, we construct a HR C p weight choice criterion that exhibits asymptotic optimality under certain assumptions. Our model averaging procedure can simultaneously address the uncertainty on which covariates to include and the uncertainty on whether a covariate should enter the linear or nonlinear component of the model. The simulation results in comparison with some related strategies strongly favor our proposal. A real dataset is analyzed to illustrate the practical application as well.

Keywords: model averaging; asymptotic optimality; HR C p; varying-coefficient partially linear model; missing data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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