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Article: Errors in the Dependent Variable of Quantile Regression Models
Title | Errors in the Dependent Variable of Quantile Regression Models |
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Authors | |
Keywords | functional analysis Measurement error quantile regression |
Issue Date | 2021 |
Publisher | Econometric Society: Econometrica. The Journal's web site is located at https://www.econometricsociety.org/publications/econometrica/aims-and-scope |
Citation | Econometrica: journal of the Econometric Society, 2021, v. 89 n. 2, p. 849-873 How to Cite? |
Abstract | We study the consequences of measurement error in the dependent variable of random‐coefficients models, focusing on the particular case of quantile regression. 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. Monte Carlo evidence verifies our method outperforms quantile regression in mean bias and MSE. Finally, we 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. |
Persistent Identifier | http://hdl.handle.net/10722/298737 |
ISSN | 2023 Impact Factor: 6.6 2023 SCImago Journal Rankings: 17.701 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Hausman, J | - |
dc.contributor.author | Liu, H | - |
dc.contributor.author | Luo, Y | - |
dc.contributor.author | Palmer, C | - |
dc.date.accessioned | 2021-04-12T03:02:42Z | - |
dc.date.available | 2021-04-12T03:02:42Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Econometrica: journal of the Econometric Society, 2021, v. 89 n. 2, p. 849-873 | - |
dc.identifier.issn | 0012-9682 | - |
dc.identifier.uri | http://hdl.handle.net/10722/298737 | - |
dc.description.abstract | We study the consequences of measurement error in the dependent variable of random‐coefficients models, focusing on the particular case of quantile regression. 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. Monte Carlo evidence verifies our method outperforms quantile regression in mean bias and MSE. Finally, we 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. | - |
dc.language | eng | - |
dc.publisher | Econometric Society: Econometrica. The Journal's web site is located at https://www.econometricsociety.org/publications/econometrica/aims-and-scope | - |
dc.relation.ispartof | Econometrica: journal of the Econometric Society | - |
dc.rights | The copyright to this article is held by the Econometric Society, http://www.econometricsociety.org/. | - |
dc.subject | functional analysis | - |
dc.subject | Measurement error | - |
dc.subject | quantile regression | - |
dc.title | Errors in the Dependent Variable of Quantile Regression Models | - |
dc.type | Article | - |
dc.identifier.email | Luo, Y: kurtluo@hku.hk | - |
dc.identifier.authority | Luo, Y=rp02428 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.3982/ECTA14667 | - |
dc.identifier.scopus | eid_2-s2.0-85102823645 | - |
dc.identifier.hkuros | 322009 | - |
dc.identifier.volume | 89 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 849 | - |
dc.identifier.epage | 873 | - |
dc.identifier.isi | WOS:000631036300012 | - |
dc.publisher.place | United Kingdom | - |