{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T04:27:37Z","timestamp":1784694457860,"version":"3.55.0"},"reference-count":31,"publisher":"Cambridge University Press (CUP)","issue":"6","license":[{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>\n                    This article examines high-dimensional covariates in regression discontinuity design (RDD) analysis. We introduce estimation and inference methods for the RDD models that incorporate covariate selection while maintaining stability across various numbers of covariates. The proposed methods combine a localization approach using kernel weights with\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mime-subtype=\"png\" xlink:href=\"S0266466624000239_inline1.png\"\/>\n                        <jats:tex-math>$\\ell _{1}$<\/jats:tex-math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -penalization to handle high-dimensional covariates. We provide both theoretical and numerical evidence demonstrating the efficacy of our methods. Theoretically, we present risk and coverage properties for our point estimation and inference methods. Conditions are given under which the proposed estimator becomes more efficient than the conventional covariate adjusted estimator at the cost of an additional sparsity condition. Numerically, our simulation experiments and empirical examples show the robust behaviors of the proposed methods to the number of covariates in terms of bias and variance for point estimation and coverage probability and interval length for inference.\n                  <\/jats:p>","DOI":"10.1017\/s0266466624000239","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T04:55:06Z","timestamp":1739508906000},"page":"1416-1451","source":"Crossref","is-referenced-by-count":3,"title":["REGRESSION DISCONTINUITY DESIGN WITH POTENTIALLY MANY COVARIATES"],"prefix":"10.1017","volume":"41","author":[{"given":"Yoichi","family":"Arai","sequence":"first","affiliation":[{"name":"Waseda University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taisuke","family":"Otsu","sequence":"additional","affiliation":[{"name":"London School of Economics"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Myung Hwan","family":"Seo","sequence":"additional","affiliation":[{"name":"Seoul National University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"key":"S0266466624000239_r25","doi-asserted-by":"publisher","DOI":"10.1093\/ectj\/utac029"},{"key":"S0266466624000239_r26","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asaa103"},{"key":"S0266466624000239_r8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20192-9"},{"key":"S0266466624000239_r12","doi-asserted-by":"publisher","DOI":"10.1162\/rest_a_00760"},{"key":"S0266466624000239_r18","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-economics-051520-021409"},{"key":"S0266466624000239_r28","doi-asserted-by":"publisher","DOI":"10.1162\/qjec.122.1.159"},{"key":"S0266466624000239_r20","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176348900"},{"key":"S0266466624000239_r23","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00183"},{"key":"S0266466624000239_r5","unstructured":"Belloni, A. , Chernozhukov, V. , Chetverikov, D. , Hansen, C. , & Kato, K. (2018). High-dimensional econometrics and regularized GMM. Massachusetts Institute of Technology, Cambridge MA, Working Paper."},{"key":"S0266466624000239_r16","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA11224"},{"key":"S0266466624000239_r29","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176325632"},{"key":"S0266466624000239_r21","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v033.i01"},{"key":"S0266466624000239_r1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0262.2006.00655.x"},{"key":"S0266466624000239_r13","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA11757"},{"key":"S0266466624000239_r4","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA14434"},{"key":"S0266466624000239_r30","doi-asserted-by":"publisher","DOI":"10.1214\/14-AOS1221"},{"key":"S0266466624000239_r9","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285776"},{"key":"S0266466624000239_r17","doi-asserted-by":"publisher","DOI":"10.1108\/S0731-9053201738"},{"key":"S0266466624000239_r31","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2545-2"},{"key":"S0266466624000239_r3","unstructured":"Arai, Y. , Otsu, T. and Seo, M. (2021). Regression discontinuity design with potentially many covariates. Preprint, arXiv:2109.08351."},{"key":"S0266466624000239_r11","doi-asserted-by":"publisher","DOI":"10.1177\/1536867X1701700208"},{"key":"S0266466624000239_r22","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.2017.1421544"},{"key":"S0266466624000239_r2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1996.10476902"},{"key":"S0266466624000239_r6","doi-asserted-by":"publisher","DOI":"10.1093\/restud\/rdt044"},{"key":"S0266466624000239_r15","first-page":"38","article-title":"Rdrobust: An R package for robust inference in regression discontinuity design","volume":"7","author":"Calonico","year":"2015b","journal-title":"R Journal"},{"key":"S0266466624000239_r10","unstructured":"Calonico, S. , Cattaneo, M. D. , & Farrell, M. H. (2020). Coverage error optimal confidence intervals for local polynomial regression. 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