Copula-based regression models with data missing at random

JOURNAL OF MULTIVARIATE ANALYSIS

卷:180

Article

Hamori, Shigeyuki¹  Motegi, Kaiji¹  Zhang, Zheng² 

[1] Kobe Univ, Grad Sch Econ, Kobe, Hyogo 6578501, Japan

[2] Renmin Univ China, Inst Stat & Big Data, Beijing 100080, Peoples R China

关键词: Calibration estimation;    Generalized regression model;    Missing at random (MAR);    Semiparametric copula;   

DOI  :  10.1016/j.jmva.2020.104654

来源: Elsevier

【 摘 要 】

The existing literature of copula-based regression assumes that complete data are available, but this assumption is violated in many real applications. The present paper allows the regressand and regressors to be missing at random (MAR). We formulate a generalized regression model which unifies many prominent cases such as the conditional mean and quantile regressions. A semiparametric copula and the target regression curve are estimated via the calibration approach. The consistency and asymptotic normality of the estimated regression curve are proved. We show via Monte Carlo simulations that the proposed approach operates well in finite samples, while a benchmark equal-weight approach fails with substantial bias under MAR. An empirical application on revenues and R&D expenses of German manufacturing firms highlights a practical use of our approach. (C) 2020 Elsevier Inc. All rights reserved.

【 授权许可】

Free