Errors in the Dependent Variable of Quantile Regression Models

Jerry Hausman, Haoyang Liu, Ye Luo and Christopher Palmer

No 25819, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: The popular quantile regression estimator of Koenker and Bassett (1978) is biased if there is an additive error term. Approaching this problem as an errors-in-variables problem where the dependent variable suffers from classical measurement error, we present a sieve maximum-likelihood approach that is robust to left-hand side measurement error. After providing sufficient conditions for identification, we demonstrate that when the number of knots in the quantile grid is chosen to grow at an adequate speed, the sieve maximum-likelihood estimator is consistent and asymptotically normal, permitting inference via bootstrapping. We verify our theoretical results with Monte Carlo simulations and illustrate our estimator with an application to the returns to education highlighting changes over time in the returns to education that have previously been masked by measurement-error bias.

JEL-codes: C19 C21 C31 I24 I26 J30 (search for similar items in EconPapers)
Date: 2019-05
New Economics Papers: this item is included in nep-bec, nep-ecm, nep-lma and nep-ore
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Citations: View citations in EconPapers (4)

Published as Jerry Hausman & Haoyang Liu & Ye Luo & Christopher Palmer, 2021. "Errors in the Dependent Variable of Quantile Regression Models," Econometrica, Econometric Society, vol. 89(2), pages 849-873, March.

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