Partial identification and inference in censored quantile regression
In this paper, we study partial identification and inference in a linear quantile regression, where the dependent variable is subject to possibly unknown dependent censoring characterized by an Archimedean copula. An outer set of the identified set for the regression coefficient is characterized via...
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| Published in | Journal of econometrics Vol. 206; no. 1; pp. 1 - 38 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
01.09.2018
Elsevier Science Publishers Elsevier Sequoia S.A |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0304-4076 1872-6895 |
| DOI | 10.1016/j.jeconom.2018.04.002 |
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| Abstract | In this paper, we study partial identification and inference in a linear quantile regression, where the dependent variable is subject to possibly unknown dependent censoring characterized by an Archimedean copula. An outer set of the identified set for the regression coefficient is characterized via inequality constraints. For one-parameter ordered families of Archimedean copulas, we construct a simple confidence set by inverting an asymptotically pivotal statistic. A bootstrap confidence set is also constructed. Sensitivity of the identified set to possible misspecification of the true copula and the finite sample performance of the boostrap confidence set are investigated numerically. |
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| AbstractList | In this paper, we study partial identification and inference in a linear quantile regression, where the dependent variable is subject to possibly unknown dependent censoring characterized by an Archimedean copula. An outer set of the identified set for the regression coefficient is characterized via inequality constraints. For one-parameter ordered families of Archimedean copulas, we construct a simple confidence set by inverting an asymptotically pivotal statistic. A bootstrap confidence set is also constructed. Sensitivity of the identified set to possible misspecification of the true copula and the finite sample performance of the boostrap confidence set are investigated numerically. |
| Audience | Academic |
| Author | Liu, Ruixuan Fan, Yanqin |
| Author_xml | – sequence: 1 givenname: Yanqin surname: Fan fullname: Fan, Yanqin email: fany88@uw.edu organization: Department of Economics, University of Washington, Box 353330, Seattle, WA 98195, United States – sequence: 2 givenname: Ruixuan surname: Liu fullname: Liu, Ruixuan organization: Department of Economics, Emory University, 1602 Fishburne Drive, Atlanta, GA 30322, United States |
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| Keywords | C41 Confidence set Archimedean copula Competing risks model C12 C34 C14 C51 Independent censoring Dependent censoring |
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| SubjectTerms | Archimedean copula Bootstrap method Competing risks model Confidence set Dependent censoring econometric models Econometrics economic analysis economic theory Independent censoring Inequality Inference probability distribution Regression analysis Statistical inference |
| Title | Partial identification and inference in censored quantile regression |
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