Quantile-Regression Inference With Adaptive Control of Size

References

• Bose, A., and Chatterjee, S. (2003), “Generalized Bootstrap for Estimators of Minimizers of Convex Functions,” Journal of Statistical Planning and Inference, 117, 225–239.
   Web of Science ®Google Scholar
• Choi, S., Hall, W. J., and Schick, A. (1996), “Asymptotically Uniformly Most Powerful Tests in Parametric and Semiparametric Models,” Annals of Statistics, 24, 841–861.
   Web of Science ®Google Scholar
• Corson, W., Decker, P., Dunstan, S., and Kerachsky, S. (1992), “Pennsylvania Reemployment Bonus Demonstration: Final Report,” Unemployment Insurance Occasional Paper 92-1, United States Department of Labor.
   Google Scholar
• Doksum, K. (1974), “Empirical Probability Plots and Statistical Inference for Nonlinear Models in the Two-Sample Case,” Annals of Statistics, 2, 267–277.
   Web of Science ®Google Scholar
• Escanciano, J. C., and Goh, S. C. (2014), “Specification Analysis of Linear Quantile Models,” Journal of Econometrics, 178, 495–507.
   Web of Science ®Google Scholar
• Fan, Y., and Liu, R. (2016), “A Direct Approach to Inference in Nonparametric and Semiparametric Quantile Models,” Journal of Econometrics, 191, 196–216.
   Web of Science ®Google Scholar
• Feng, X., He, X., and Hu, J. (2011), “Wild Bootstrap for Quantile Regression,” Biometrika, 98, 995–999.
   PubMed Web of Science ®Google Scholar
• Feng, X., and Zhu, L. (2016), “Estimation and Testing of Varying Coefficients in Quantile Regression,” Journal of the American Statistical Association, 111, 266–274.
   Web of Science ®Google Scholar
• Feng, Y., Chen, Y., and He, X. (2015), “Bayesian Quantile Regression with Approximate Likelihood,” Bernoulli, 21, 832–850.
   Web of Science ®Google Scholar
• Gutenbrunner, C., and Jurečková, J. (1992), “Regression Rank Scores and Regression Quantiles,” Annals of Statistics, 20, 305–330.
   Web of Science ®Google Scholar
• Hall, P., and Sheather, S. J. (1988), “On the Distribution of a Studentized Quantile,” Journal of the Royal Statistical Society, Series B, 50, 381–391.
   Google Scholar
• He, X. (2017), “Resampling Methods,” in Handbook of Quantile Regression, eds. R. Koenker, V. Chernozhukov, X. He, and L. Peng, Boca Raton, FL: Chapman and Hall/CRC, pp. 7–19.
   Google Scholar
• He, X., and Hu, F. (2002), “Markov Chain Marginal Bootstrap,” Journal of the American Statistical Association, 97, 783–795.
   Web of Science ®Google Scholar
• Hendricks, W., and Koenker, R. (1992), “Hierarchical Spline Models for Conditional Quantiles and the Demand for Electricity,” Journal of the American Statistical Association, 87, 58–68.
   Web of Science ®Google Scholar
• Jurečková, J., and Sen, P. K. (1996), Robust Statistical Procedures: Asymptotics and Interrelations, New York: Wiley.
   Google Scholar
• Kocherginsky, M., He, X., and Mu, Y. (2005), “Practical Confidence Intervals for Regression Quantiles,” Journal of Computational and Graphical Statistics, 14, 41–55.
   Web of Science ®Google Scholar
• Koenker, R. (1994), “Confidence Intervals for Regression Quantiles,” in Asymptotic Statistics: Proceedings of the Fifth Prague Symposium, held from September 4–9, 1993, eds. P. Mandl, and M. Hušková, Heidelberg: Physica-Verlag, pp. 349–359.
   Google Scholar
• ——— (2005), Quantile Regression, New York: Cambridge University Press.
   Google Scholar
• ——— (2018), quantreg: Quantile Regression, R Package Version 5.35, available at https://CRAN.R-project.org/package=quantreg.
   Google Scholar
• Koenker, R., and Bassett, G. (1978), “Regression Quantiles,” Econometrica, 46, 33–50.
   Web of Science ®Google Scholar
• Koenker, R., and Bilias, Y. (2001), “Quantile Regression for Duration Data: A Reappraisal of the Pennsylvania Reemployment Bonus Experiments,” Empirical Economics, 26, 199–220.
   Google Scholar
• Koenker, R., Chernozhukov, V., He, X., and Peng, L. (2017), Handbook of Quantile Regression, Boca Raton, FL: Chapman and Hall/CRC.
   Google Scholar
• Koenker, R., and Machado, J. A. F. (1999), “Goodness of Fit and Related Inference Processes for Quantile Regression,” Journal of the American Statistical Association, 94, 1296–1310.
   Web of Science ®Google Scholar
• Koenker, R., and Xiao, Z. (2002), “Inference on the Quantile Regression Process,” Econometrica, 70, 1583–1612.
   Web of Science ®Google Scholar
• Komunjer, I., and Vuong, Q. (2010), “Semiparametric Efficiency Bound In Time Series Models for Conditional Quantiles,” Econometric Theory, 26, 383–405.
   Web of Science ®Google Scholar
• Ma, S., and He, X. (2016), “Inference for Single-Index Quantile Regression Models with Profile Optimization,” Annals of Statistics, 44, 1234–1268.
   Web of Science ®Google Scholar
• Machado, J. A. F., and Mata, J. (2005), “Counterfactual Decomposition of Changes in Wage Distributions using Quantile Regression,” Journal of Applied Econometrics, 20, 445–465.
   Web of Science ®Google Scholar
• Newey, W. K., and Powell, J. L. (1990), “Efficient Estimation of Linear and Type I Censored Regression Models under Conditional Quantile Restrictions,” Econometric Theory, 6, 295–317.
   Web of Science ®Google Scholar
• Parzen, M. I., Wei, L. J., and Ying, Z. (1994), “A Resampling Method Based on Pivotal Estimating Functions,” Biometrika, 81, 341–350.
   Web of Science ®Google Scholar
• Portnoy, S. (2012), “Nearly root-n Approximation for Regression Quantile Processes,” Annals of Statistics, 40, 1714–1736.
   Web of Science ®Google Scholar
• Powell, J. L. (1991), “Estimation of Monotonic Regression Models Under Quantile Restrictions,” in Nonparametric and Semiparametric Methods in Econometrics and Statistics: Proceedings of the Fifth International Symposium in Economic Theory and Econometrics, eds. W. A. Barnett, J. L. Powell, and G. E. Tauchen, Cambridge, UK: Cambridge University Press, pp. 357–384.
   Google Scholar
• R Core Team (2016), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing, available at https://www.R-project.org/
   Google Scholar
• Yang, Y., and He, X. (2012), “Bayesian Empirical Likelihood for Quantile Regression,” Annals of Statistics, 40, 1102–1131.
   Web of Science ®Google Scholar
• Yang, Y., Wang, H. X., and He, X. (2016), “Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood,” International Statistical Review, 84, 327–344.
   Web of Science ®Google Scholar
• Zhou, K. Q., and Portnoy, S. L. (1996), “Direct Use of Regression Quantiles to Construct Confidence Sets in Linear Models,” Annals of Statistics, 24, 287–306.
   Web of Science ®Google Scholar