Statistical inference for heteroscedastic semi-varying coefficient EV models

Zhao, Fanrong; Song, Weixing; Shi, Jianhong

doi:10.1080/03610926.2016.1242735

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…First, some regularity conditions are needed. These conditions are mild, and similar conditions can be found in [9][10][11], and other varying-coefficient partially linear heteroscedasticity literature.…”

Section: Asymptotic Propertiessupporting

confidence: 72%

“…First of all, according to the estimators of model residuals, we suggest an adjusted Nadaraya-Watson kernel estimation method for the variance function. By (11) and ( 12), we can estimate the residual error by:…”

Section: Adaptive-weighted Adjusted Profile Least Squares Estimationmentioning

confidence: 99%

“…Zhao et al [10] proposed a two-stage iterative estimation method by using the orthogonal projection technique, which can individually estimate unknown parameters and functional coefficients. Zhao et al [11] proposed a re-weighted estimation procedure when the covariates contain an additive measurement error. Zhang and Li [12] proposed a weighted estimation and testing method when the covariates suffer from an additive measurement error.…”

Section: Introductionmentioning

confidence: 99%

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Estimation in Semi-Varying Coefficient Heteroscedastic Instrumental Variable Models with Missing Responses

Zhang,

Luo,

2023

Mathematics

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This paper studies the estimation problem for semi-varying coefficient heteroscedastic instrumental variable models with missing responses. First, we propose the adjusted estimators for unknown parameters and smooth functional coefficients utilizing the ordinary profile least square method and instrumental variable adjustment technique with complete data. Second, we present an adjusted estimator of the stochastic error variance by employing the Nadaraya–Watson kernel estimation technique. Third, we apply the inverse probability-weighted method and instrumental variable adjustment technique to construct the adaptive-weighted adjusted estimators for unknown parameters and smooth functional coefficients. The asymptotic properties of our proposed estimators are established under some regularity conditions. Finally, numerous simulation studies and a real data analysis are conducted to examine the finite sample performance of the proposed estimators.

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Section: Asymptotic Propertiessupporting

confidence: 72%

Section: Adaptive-weighted Adjusted Profile Least Squares Estimationmentioning

confidence: 99%