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Inference in Constrained Quantile Regression

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Inference in Constrained Quantile Regression
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13
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CC Attribution - NonCommercial - NoDerivatives 4.0 International:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Release Date2018
LanguageEnglish

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Abstract
I investigate the asymptotic distribution of linear quantile regression coefficient estimates when the parameter may lie on the boundary of the parameter space, and related inference procedures when the null hypothesis asserts that the parameters lie on a boundary of this set. Particular attention is paid to parameter spaces defined by sets of linear inequalities. I provide a uniform characterization of the constrained quantile regression process over an interval of quantile levels. This asymptotic theory is used to derive asymptotic inference methods for three related processes based on the constrained quantile regression process.