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Quantile Regression under Limited Dependent Variable

Javier Alejo, Gabriel Montes-Rojas

arXiv 13 Dec 2021 · Econometrics · publishedJournal of Statistical Software (2025)

arXiv:2112.06822 · PDF · DOI · OpenAlex · Extracted main text

Abstract

A new Stata command, ldvqreg, is developed to estimate quantile regression models for the cases of censored (with lower and/or upper censoring) and binary dependent variables. The estimators are implemented using a smoothed version of the quantile regression objective function. Simulation exercises show that it correctly estimates the parameters and it should be implemented instead of the available quantile regression methods when censoring is present. An empirical application to women's labor supply in Uruguay is considered.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Kordas, G (2006) Smoothed binary regression quantiles0.81142100%
2Horowitz, J (1992) A smoothed maximum score estimator for the binary response model0.64422100%
3Manski, C (1985) Semiparametric analysis of discrete response: Asymptotic properties of the maximum score estimator0.64422100%
4Powell, J. L (1984) Least absolute deviations estimation for the censored regression model0.64422100%
5Powell, J. L (1986) Censored regression quantiles0.64422100%
6Buchinsky, M (1991) Methodological issues in quantile regression, Chapter 1 of The Theory and Practice of Quantile Regression, Ph.D0.51121100%
7Baker, M (2013) MCMCCQREG: Stata module to perform simulation assisted estimation of censored quantile regression using adaptive Markov chain Mo…0.40511100%
8Buchinsky, M. and J. Hahn (1998) An Alternative Estimator for the Censored Regression Model0.40511100%
9Chernozhukov, V. and H. Hong (2002) Three-Step Censored Quantile Regression and Extramarital Affairs0.40511100%
10Kaplan, D. and Y. Sun (2017) Smoothed estimating equations for instrumental variables quantile regression0.40511100%

Showing the top 10 of 16 scored citations.