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Quantile Regression with Interval Data

Arie Beresteanu ()

No 6899, Working Paper from Department of Economics, University of Pittsburgh

Abstract: This paper investigates the identifi cation of quantiles and quantile regression parameterswhen observations are set valued. We de fine the identifi cation set of quantiles of random setsin a way that extends the defi nition of quantiles for regular random variables. We then givesharp characterization of this set by extending concepts from random set theory. Applying theidentifi cation set of quantiles and its sharpness to parametric quantile regression models yieldsthe identi fication set of the parameters and its sharpness. We apply our methods to data onlocalized environmental benefi ts and their impact on house values.

Date: 2020-01
New Economics Papers: this item is included in nep-gen
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Working Paper: Quantile Regression with Interval Data (2016) Downloads
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