{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T05:00:35Z","timestamp":1791435635070,"version":"4.3.3"},"reference-count":42,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T00:00:00Z","timestamp":1584057600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This article investigates the instrumental variable quantile regression model (Chernozhukov and Hansen, 2005,<jats:italic>Econometrica<\/jats:italic>73, 245\u2013261; 2013,<jats:italic>Annual Review of Economics<\/jats:italic>, 5, 57\u201381) with a binary endogenous treatment. It offers two identification results when the treatment status is not directly observed. The first result is that, remarkably, the reduced-form quantile regression of the outcome variable on the instrumental variable provides a lower bound on the structural quantile treatment effect under the stochastic monotonicity condition. This result is relevant, not only when the treatment variable is subject to misclassification, but also when any measurement of the treatment variable is not available. The second result is for the structural quantile function when the treatment status is measured with error; the sharp identified set is characterized by a set of moment conditions under widely used assumptions on the measurement error. Furthermore, an inference method is provided in the presence of other covariates.<\/jats:p>","DOI":"10.1017\/s026646662000002x","type":"journal-article","created":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T14:39:40Z","timestamp":1584110380000},"page":"169-204","source":"Crossref","is-referenced-by-count":3,"title":["INSTRUMENTAL VARIABLE QUANTILE REGRESSION WITH MISCLASSIFICATION"],"prefix":"10.1017","volume":"37","author":[{"given":"Takuya","family":"Ura","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2020,3,13]]},"reference":[{"key":"S026646662000002X_r25","doi-asserted-by":"crossref","first-page":"487","DOI":"10.2307\/2971729","article-title":"Making the most out of programme evaluations and social experiments: Accounting for heterogeneity in programme impacts","volume":"64","author":"Heckman","year":"1997","journal-title":"The Review of Economic Studies"},{"key":"S026646662000002X_r2","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1080\/01621459.1996.10476902","article-title":"Identification of causal effects using instrumental variables.","volume":"91","author":"Angrist","year":"1996","journal-title":"Journal of the American Statistical Association"},{"key":"S026646662000002X_r26","doi-asserted-by":"crossref","first-page":"123","DOI":"10.3982\/QE170","article-title":"Partial identification of finite mixtures in econometric models","volume":"5","author":"Henry","year":"2014","journal-title":"Quantitative Economics"},{"key":"S026646662000002X_r19","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.jeconom.2017.02.002","article-title":"Measurement Errors in Quantile Regression Models","volume":"198","author":"Firpo","year":"2017","journal-title":"Journal of Econometrics"},{"key":"S026646662000002X_r42","author":"Yu","year":"2017"},{"key":"S026646662000002X_r3","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1016\/S0047-2727(01)00167-0","article-title":"Does 401(k) eligibility increase saving? 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