Quantile Regression with Interval Data
Arie Beresteanu ()
No 5991, Working Paper from Department of Economics, University of Pittsburgh
This paper investigates the identification of quantiles and quantile regression parameters when observations are set valued. We deï¬ ne the identification set of quantiles of random sets in a way that extends the deï¬ nition of quantiles for regular random variables. We then give sharp characterization of this set by extending concepts from random set theory. For quantile regression parameters, we show that the identification set is characterized by a system of conditional moment inequalities. This characterization extends that of parametric quantile regression for regular random variables. Estimation and inference theories are developed for continuous cases, discrete cases, nonparametric conditional quantiles, and parametric quantile regressions. A fast computational algorithm of set linear programming is proposed. Monte Carlo experiments support our theoretical properties. We apply the proposed method to analyze the eï¬€ects of cleanup of hazardous waste sites on local housing values.
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Working Paper: Quantile Regression with Interval Data (2020)
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