Copula-based regression models with data missing at random
Copula-based regression models with data missing at random
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基于 Copula 的回归模型，数据随机丢失
DOI：
10.1016/j.jmva.2020.104654
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发表时间：
2020
影响因子：
1.6
通讯作者：
and Zheng Zhang
中科院分区：
文献类型：
--
作者：
Shigeyuki Hamori;Kaiji Motegi;and Zheng Zhang
现有的基于Copula回归的文献假设有完整的数据可用，但这一假设在许多实际应用中是违反的。本文允许回归变量和回归变量随机缺失(MAR)。我们建立了一个广义回归模型，它统一了许多突出的情况，如条件均值回归和分位数回归。通过定标方法估计了半参数Copula和目标回归曲线。证明了估计回归曲线的相合性和渐近正态。我们通过蒙特卡罗模拟表明，所提出的方法在有限样本下运行良好，而基准等权重方法在MAR下因严重的偏差而失败。对德国制造企业的收入和研发费用的实证应用突出了我们方法的实际应用。
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.