Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects.
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- Record sourced from PubMed, PMID 34905573.
- Also identified by DOI 10.1371/journal.pone.0261144 and PMC identifier 8670681.
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Abstract
This paper considers the quantile regression model with individual fixed effects for spatial panel data. Efficient minimum distance quantile regression estimators based on instrumental variable (IV) method are proposed for parameter estimation. The proposed estimator is computational fast compared with the IV-FEQR estimator proposed by Dai et al. (2020). Asymptotic properties of the proposed estimators are also established. Simulations are conducted to study the performance of the proposed method. Finally, we illustrate our methodologies using a cigarettes demand data set.
Medical subject headings
- Computer Simulation
- Data Interpretation, Statistical
- Models, Statistical
- Regression Analysis
- Tobacco Products