Quantile Regression with Classical Additive Measurement Errors
AbstractThis note derives the bias of the quantile regression estimator in the presence of classical additive measurement error, and show its connection to least squares models. The bias structure suggests that the instrumental variables estimator proposed for least squares can be applied to the quantile regression case.
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Bibliographic InfoArticle provided by AccessEcon in its journal Economics Bulletin.
Volume (Year): 31 (2011)
Issue (Month): 4 ()
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Quantile Regression; Measurement Errors; Instrumental Variables;
Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
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