{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T08:48:29Z","timestamp":1785746909303,"version":"3.56.0"},"reference-count":32,"publisher":"Cambridge University Press (CUP)","issue":"4","license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:p>\n                    This article proposes a novel method for estimating quantile regression models that account for sample selection. Unlike the approach by Arellano and Bonhomme (2017,\n                    <jats:italic>Econometrica<\/jats:italic>\n                    85(1), 1\u201328; hereafter referred to as AB17), which employs a parametric selection equation, our method utilizes a standard binary quantile regression model to handle the selection issue, thereby accommodating general heterogeneity in both the selection and outcome equations. We adopt a semiparametric estimation technique for the outcome quantile regression by integrating local moment conditions, resulting in\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <mml:math xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xmlns:mnf=\"http:\/\/cambridge.org\/core\/manifest\" xmlns:cup=\"http:\/\/contentservices.cambridge.org\" xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" xmlns:m=\"http:\/\/cambridge.org\/core\/metadata\" xmlns:core=\"http:\/\/cambridge.org\/core\" xmlns:c=\"http:\/\/cambridge.org\/core\/content\" display=\"inline\">\n                          <mml:msqrt>\n                            <mml:mi>n<\/mml:mi>\n                          <\/mml:msqrt>\n                        <\/mml:math>\n                        <jats:tex-math>$\\sqrt {n}$<\/jats:tex-math>\n                        <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" content-type=\"simple\" xlink:href=\"S0266466625100121_inline1.png\">\n                          <jats:alt-text content-type=\"machine-generated\">StartRoot n EndRoot<\/jats:alt-text>\n                        <\/jats:inline-graphic>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -consistent estimators for the quantile coefficients and copula parameter. Monte Carlo simulation results demonstrate that our estimator performs well in finite samples. Additionally, we apply our method to examine the wage distribution among women using a randomly simulated sample from the US General Social Survey. Our key finding is the presence of significant positive selection among women in the US, which is notably more pronounced than the estimates produced by the AB17\u2019s model.\n                  <\/jats:p>","DOI":"10.1017\/s0266466625100121","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T10:28:14Z","timestamp":1759141694000},"page":"1034-1077","source":"Crossref","is-referenced-by-count":0,"title":["SEMIPARAMETRIC ESTIMATION OF QUANTILE REGRESSION WITH BINARY QUANTILE SELECTION"],"prefix":"10.1017","volume":"42","author":[{"given":"Songnian","family":"Chen","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00a2xv884","id-type":"ROR","asserted-by":"publisher"}],"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanghui","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/01mv9t934","id-type":"ROR","asserted-by":"publisher"}],"name":"Shanghai University of Finance and Economics"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"S0266466625100121_r27","doi-asserted-by":"publisher","DOI":"10.1093\/jjfinec\/nbv014"},{"key":"S0266466625100121_r3","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1997.10474001"},{"key":"S0266466625100121_r4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2018.06.020"},{"key":"S0266466625100121_r24","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(85)90009-0"},{"key":"S0266466625100121_r18","first-page":"45","volume-title":"Handbook of Statistics","volume":"11","author":"Horowitz","year":"1993"},{"key":"S0266466625100121_r14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2016.01.008"},{"key":"S0266466625100121_r10","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA12121"},{"key":"S0266466625100121_r15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2021.07.010"},{"key":"S0266466625100121_r32","doi-asserted-by":"publisher","DOI":"10.1017\/S0266466619000124"},{"key":"S0266466625100121_r29","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-1554-7"},{"key":"S0266466625100121_r12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2008.05.018"},{"key":"S0266466625100121_r17","doi-asserted-by":"publisher","DOI":"10.2307\/2951582"},{"key":"S0266466625100121_r28","doi-asserted-by":"publisher","DOI":"10.2307\/1913622"},{"key":"S0266466625100121_r23","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(75)90032-9"},{"key":"S0266466625100121_r16","doi-asserted-by":"publisher","DOI":"10.2307\/1912352"},{"key":"S0266466625100121_r5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2021.11.018"},{"key":"S0266466625100121_r31","first-page":"271","article-title":"Uniform ratio limit theorems for empirical processes","volume":"22","author":"Pollard","year":"1995","journal-title":"Scandinavian Journal of Statistics"},{"key":"S0266466625100121_r22","doi-asserted-by":"publisher","DOI":"10.1002\/jae.843"},{"key":"S0266466625100121_r2","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA14030"},{"key":"S0266466625100121_r25","first-page":"2111","volume-title":"Handbook of Ecomometrics","volume":"4","author":"Newey","year":"1994"},{"key":"S0266466625100121_r30","first-page":"341","article-title":"Asymptotics via empirical processes","volume":"4","author":"Pollard","year":"1989","journal-title":"Statistical Science"},{"key":"S0266466625100121_r9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2005.02.009"},{"key":"S0266466625100121_r6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2015.06.019"},{"key":"S0266466625100121_r26","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176350374"},{"key":"S0266466625100121_r11","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2019.1660177"},{"key":"S0266466625100121_r19","doi-asserted-by":"publisher","DOI":"10.2307\/2999619"},{"key":"S0266466625100121_r21","doi-asserted-by":"publisher","DOI":"10.1017\/S0266466615000407"},{"key":"S0266466625100121_r20","doi-asserted-by":"publisher","DOI":"10.2307\/1913643"},{"key":"S0266466625100121_r7","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.3982\/ECTA10582","article-title":"Inference on counterfactual distributions","volume":"81","author":"Chernozhukov","year":"2013","journal-title":"Econometrica"},{"key":"S0266466625100121_r1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0262.2006.00671.x"},{"key":"S0266466625100121_r8","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2019.1611581"},{"key":"S0266466625100121_r13","unstructured":"Gao, Y. , Xu, S. , & Xu, K. 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