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Subgroup effect quantile regression with high dimensional missing panel data
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AI
DOI：10.1016/j.jmva.2025.105593.png)
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Abstract
En 中文
Based on panel data, we explore partially linear varying-coefficient quantile regression with group effects under high dimension and missing observations. Using generalized estimating equations, we construct oracle estimators along with smoothed version for the unknown parameter vector, varying-coefficient functions as well as group effects, and establish their asymptotic normality. In the estimation procedure, the within-subject correlations of the panel data are considered by introducing working correlation matrix. We further investigate variable selection by the SCAD penalty for the parameters, varying-coefficient functions and group identification simultaneously, and discuss oracle properties. Meanwhile, hypothesis tests for the parameter, varying-coefficient functions and group effects are done, asymptotic distributions of the restricted estimators and test statistics under both the null and local alternative hypotheses are analyzed. Also, simulation study and real data analysis are conducted to evaluate the performance of the proposed methods.
Keywords:
High dimensional panel data
Hypothesis test
Hypothesis Missing observation
Quantile regression
Subgroup identification
Journal
J
IF:
1.7
Papers:
97
Citations:
5.8K