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Bayesian Bootstrap of the Quantile Regression Estimator: A Large Sample Study

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  • Hahn, Jinyong
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    Abstract

    The large sample property of the Bayesian bootstrap distribution of the quantile regression estimator is investigated. When the pair of dependent and independent variables are resampled, the Bayesian bootstrap is shown to converge weakly in probability to the limiting distribution of the quantile regression estimator. The Bayesian bootstrap thus has the same asymptotic distribution as the Frequentist bootstrap. In addition, the median of the Bayesian bootstrap distribution has the same asymptotic distribution as the quantile regression estimator. Copyright 1997 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

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    Article provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.

    Volume (Year): 38 (1997)
    Issue (Month): 4 (November)
    Pages: 795-808

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    Handle: RePEc:ier:iecrev:v:38:y:1997:i:4:p:795-808
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    Cited by:

    1. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.
    2. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val, 2011. "Conditional quantile processes based on series or many regressors," CeMMAP working papers CWP19/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Joshua Angrist & Victor Chernozhukov & Iván Fernández-Val, 2006. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," Econometrica, Econometric Society, vol. 74(2), pages 539-563, 03.
    4. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers CWP57/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Ying-Ying Lee, 2015. "Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-14, December.
    6. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    7. David M. Kaplan & Longhao Zhuo, 2015. "Bayesian and frequentist inequality tests," Working Papers 1516, Department of Economics, University of Missouri, revised Apr 2017.
    8. David M. Kaplan & Longhao Zhuo, 2015. "Bayesian and frequentist nonlinear inequality tests," Working Papers 1709, Department of Economics, University of Missouri, revised Apr 2017.
    9. Giuseppe Ragusa, 2007. "Bayesian Likelihoods for Moment Condition Models," Working Papers 060714, University of California-Irvine, Department of Economics.
    10. repec:gam:jecnmx:v:4:y:2015:i:1:p:2:d:61252 is not listed on IDEAS
    11. Yang Yang & Tae-Hwy Lee, 2004. "Bagging Binary Predictors for Time Series," Econometric Society 2004 Far Eastern Meetings 512, Econometric Society.

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