Quantile Regression Varying-Coefficient Model in The Case of Longitudinal Data: A Study on The Success Rates of Secondary Schools in Al-Diwaniyah Governorate \ Iraq
Abstract
In this paper, we study the quantile regression varying-coefficient model in the case of longitudinal data. two methods are used to estimate the model: the traditional method, which ignores within-subject correlations, and the weighted method, which accounts for within-subject correlations by incorporating weights. the local polynomial method is used to estimate the nonparametric functions. Five quantile levels are examined. these methods are applied to data on the success rates of the third intermediate grade in Al-Diwaniyah Governorate. the study investigates the impact of four variables on the success rates of 337 middle and high schools over five years. the results show that the weighted estimation method is more efficient than the traditional method across all quantile levels
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References
1. Kim, M. O., & Yang, Y. 2011. Semiparametric approach to a random effects quantile regression model. Journal of the American Statistical Association, 106(496), 1405-1417.
2. Badr, Duraid Hussein 2016. The diagnosis estimation of the nonparametric regression function of the panal data in Case some of its hypotheses are not verified. the Ph. D to the College of Administration and Economics, University of Baghdad.
3. Cai, Z., & Xu, X. 2008. Nonparametric quantile estimations for dynamic smooth coefficient models. Journal of the American Statistical Association, 103(484), 1595-1608.
4. Kim, M. O. (2007). Quantile regression with varying coefficients.
5. Kim, S., & Cho, H. R. 2018. Efficient estimation in the partially linear quantile regression model for longitudinal data.
6. Koenker, R., & Bassett Jr, G. 1978. Regression quantiles. Econometrica: journal of the Econometric Society, 33-50.
7. Lin, F., Tang, Y., & Zhu, Z. 2020. Weighted quantile regression in varying-coefficient model with longitudinal data. Computational Statistics & Data Analysis, 145, 106915.
8. Qu, A., & Li, R. 2006. Quadratic inference functions for varying-coefficient models with longitudinal data. Biometrics, 62(2), 379-391.
9. Rashed, Husam A. 2014. Nonparametric smoothers for varying coefficient model and partial varying coefficient model. the Ph. D to the College of Administration and Economics, University of Baghdad.
10. Saifalddin, Ali. 2012. Nonparametric model estimation for longitudinal data of economic activies in iraq . the Ph. D to the College of Administration and Economics, University of Baghdad.
11. Tang, C. Y., & Leng, C. 2011. Empirical likelihood and quantile regression in longitudinal data analysis. Biometrika, 98(4), 1001-1006.
12. Wu, C. O., & Chiang, C. T. 2000. Kernel smoothing on varying coefficient models with longitudinal dependent variable. Statistica Sinica, 433-456.
