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Estimation and test of restricted linear EV model with nonignorable missing covariates

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Abstract

This paper deals with estimation and test procedures for restricted linear errors-invariables (EV) models with nonignorable missing covariates. We develop a restricted weighted corrected least squares (WCLS) estimator based on the propensity score, which is fitted by an exponentially tilted likelihood method. The limiting distributions of the proposed estimators are discussed when tilted parameter is known or unknown. To test the validity of the constraints, we construct two test procedures based on corrected residual sum of squares and empirical likelihood method and derive their asymptotic properties. Numerical studies are conducted to examine the finite sample performance of our proposed methods.

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Authors and Affiliations

  1. Department of Statistics, Jiaxing University, Jiaxing, 314001, China

    Lin-jun Tang, Sheng-chao Zheng & Zhan-gong Zhou

Authors
  1. Lin-jun Tang
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  2. Sheng-chao Zheng
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  3. Zhan-gong Zhou
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Corresponding author

Correspondence to Lin-jun Tang.

Additional information

Supported by the Zhejiang Provincial Natural Science Foundation of China (LY15A010019), and National Natural Science Foundation of China (11501250).

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Tang, Lj., Zheng, Sc. & Zhou, Zg. Estimation and test of restricted linear EV model with nonignorable missing covariates. Appl. Math. J. Chin. Univ. 33, 344–358 (2018). https://doi.org/10.1007/s11766-018-3550-8

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  • DOI: https://doi.org/10.1007/s11766-018-3550-8

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