{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T10:03:11Z","timestamp":1790676191201,"version":"4.1.0"},"reference-count":45,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2019,9]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>\n                    The purpose of this paper is to provide guidelines for empirical researchers who use a class of bivariate threshold crossing models with dummy endogenous variables. A common practice employed by the researchers is the specification of the joint distribution of unobservables as a bivariate normal distribution, which results in a\n                    <jats:italic>bivariate probit model<\/jats:italic>\n                    . To address the problem of misspecification in this practice, we propose an easy\u2010to\u2010implement semiparametric estimation framework with parametric copula and nonparametric marginal distributions. We establish asymptotic theory, including root\u2010\n                    <jats:italic>n<\/jats:italic>\n                    normality, for the sieve maximum likelihood estimators that can be used to conduct inference on the individual structural parameters and the average treatment effect (ATE). In order to show the practical relevance of the proposed framework, we conduct a sensitivity analysis via extensive Monte Carlo simulation exercises. The results suggest that estimates of the parameters, especially the ATE, are sensitive to parametric specification, while semiparametric estimation exhibits robustness to underlying data\u2010generating processes. We then provide an empirical illustration where we estimate the effect of health insurance on doctor visits. In this paper, we also show that the absence of excluded instruments may result in identification failure, in contrast to what some practitioners believe.\n                  <\/jats:p>","DOI":"10.1002\/jae.2727","type":"journal-article","created":{"date-parts":[[2019,7,4]],"date-time":"2019-07-04T13:41:22Z","timestamp":1562247682000},"page":"994-1015","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Estimation in a generalization of bivariate probit models with dummy endogenous regressors"],"prefix":"10.1002","volume":"34","author":[{"given":"Sukjin","family":"Han","sequence":"first","affiliation":[{"name":"Department of Economics University of Texas at Austin  Austin Texas United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Global Asia Institute National University of Singapore  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,9]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00470"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.3368\/jhr.XL.4.791"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2226"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1257\/aer.98.2.351"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0266466608080316"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0266466614000036"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1573-4412(07)06076-X"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2005.07.027"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2005.03.004"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000311"},{"issue":"3","key":"e_1_2_10_12_1","first-page":"949","article-title":"Nonparametric identification and estimation of non\u2010 classical errors\u2010in\u2010variables models without additional information","volume":"19","author":"Chen X.","year":"2009","journal-title":"Statistica Sinica"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2009.02.002"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1214\/10-AOS809"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2010.07.006"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.2307\/2946645"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/07474938.2018.1514025"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.2307\/1913241"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214501753208582"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.3982\/QE989"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2017.04.001"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.2307\/1912352"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0012-9682.2008.00823.x"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/240435"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1201\/b13150"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2006.10.009"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-842X.2007.00483.x"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2011.02.008"},{"key":"e_1_2_10_29_1","unstructured":"Liao Z. &Shi X.(2017).A uniform model selection test for semi\/nonparametric models. 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