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The estimator solves a convex minimization problem, not requiring pre-estimation of the (number of) interactive fixed effects. It also allows the number of covariates to grow slowly with <jats:italic>N<\/jats:italic> and <jats:italic>T<\/jats:italic>. We derive an error bound on the estimator that holds uniformly in the quantile level. The order of the bound implies uniform consistency of the estimator and is nearly optimal for the low-rank component. Given the error bound, we also propose a consistent estimator of the number of interactive fixed effects at any quantile level. We demonstrate the performance of the estimator via Monte Carlo simulations.<\/jats:p>","DOI":"10.1017\/s0266466623000129","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T10:48:23Z","timestamp":1682333303000},"page":"1391-1421","source":"Crossref","is-referenced-by-count":8,"title":["NUCLEAR NORM REGULARIZED QUANTILE REGRESSION WITH INTERACTIVE FIXED EFFECTS"],"prefix":"10.1017","volume":"40","author":[{"given":"Junlong","family":"Feng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2023,4,24]]},"reference":[{"key":"S0266466623000129_r6","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00392"},{"key":"S0266466623000129_r26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2012.02.007"},{"key":"S0266466623000129_r4","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2018.1543598"},{"key":"S0266466623000129_r31","first-page":"13971","article-title":"Detecting latent communities in network formation models","volume":"23","author":"Ma","year":"2022","journal-title":"Journal of Machine Learning Research"},{"key":"S0266466623000129_r30","unstructured":"Lin, Z. , Chen, M. , and Ma, Y. 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