Calibration estimation of semiparametric copula models with data missing at random

JOURNAL OF MULTIVARIATE ANALYSIS

卷:173

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;    Covariate balancing;    Missing at random (MAR);    Semiparametric copula model;   

DOI  :  10.1016/j.jmva.2019.02.003

来源: Elsevier

【 摘 要 】

This paper investigates the estimation of semiparametric copula models with data missing at random. The maximum pseudo-likelihood estimation of Genest et al. (1995) is infeasible if there are missing data. We propose a class of calibration estimators for the nonparametric marginal distributions and the copula parameters of interest by balancing the empirical moments of covariates between observed and whole groups. Our proposed estimators do not require the estimation of the missing mechanism, and they enjoy stable performance even when the sample size is small. We prove that our estimators satisfy consistency and asymptotic normality. We also provide a consistent estimator for the asymptotic variance. We show via extensive simulations that our proposed method dominates existing alternatives. (C) 2019 Elsevier Inc. All rights reserved.

【 授权许可】

Free