Abstract
The quantile regression is a semiparametric technique that has been gaining considerable popularity in economics (for example, Buchinsky, 1994). It was introduced by Koenker and Bassett (1978b) as an extension to ordinary quantiles in a location model. In this model, the conditional quantiles have linear forms. A well-known special case of quantile regression is the least absolute deviation (LAD) estimator of Koenker and Bassett (1978a), which fits medians to a linear function of covariates. In an important generalization of the quantile regression model, Powell (1984; 1986) introduced the censored quantile regression model. This model is an extension of the ‘Tobit’ model and is designed to handle situations in which some of the observations on the dependent variable are censored.
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Bibliography
Andrews, D. and Buchinsky, M. 2000. A three-step method for choosing the number of bootstrap repetitions. Econometrica 68, 23–51.
Bofinger, E. 1975. Estimation of density function using order statistics. Australian Journal of Statistics 17, 1–7.
Buchinsky, M. 1994. Changes in the U.S. wage structure 1963–1987: application of quantile regression. Econometrica 62, 405–58.
Buchinsky, M. 1995. Estimating the asymptotic covariance matrix for quantile regression models: a Monte Carlo study. Journal of Econometrics 68, 303–38.
Efron, B. 1979. Bootstrap methods: another look at the jackknife. Annals of Statistics 7, 1–26.
Hansen, L. 1982. Large sample properties of generalized method of moments estimators. Econometrica 50, 1029–54.
Huber, P. 1967. The behavior of maximum likelihood estimates under nonstandard conditions. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1. Berkeley: University of California Press.
Koenker, R. 2005. Quantile Regression. Econometric Society Monograph. New York: Cambridge University Press.
Koenker, R. and Bassett, G. 1978a. The asymptotic distribution of the least absolute error estimator. Journal of the American Statistical Association 73, 618–22.
Koenker, R. and Bassett, G. 1978b. Regression quantiles. Econometrica 46, 33–50.
Newey, W. and Powell, J. 1990. Efficient estimation of linear and type I censored regression models under conditional quantile restrictions. Econometric Theory 6, 295–317.
Portnoy, S. and Koenker, R. 1989. Adaptive L-estimation for linear models. Annals of Statistics 17, 362–81.
Powell, J. 1984. Least absolute deviation estimation for the censored regression model. Journal of Econometrics 25, 303–25.
Powell, J. 1986. Censored regression quantiles. Journal of Econometrics 32, 143–55.
Siddiqui, M. 1960. Distribution of quantile from a bivariate population. Journal of Research of the National Bureau of Standards 64, 145–50.
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Buchinksy, M. (2010). Quantile Regression. In: Durlauf, S.N., Blume, L.E. (eds) Microeconometrics. The New Palgrave Economics Collection. Palgrave Macmillan, London. https://doi.org/10.1057/9780230280816_25
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DOI: https://doi.org/10.1057/9780230280816_25
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