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Partial identification and inference in censored quantile regression

Yanqin Fan and Ruixuan Liu

Journal of Econometrics, 2018, vol. 206, issue 1, 1-38

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.

Keywords: Archimedean copula; Competing risks model; Confidence set; Dependent censoring; Independent censoring (search for similar items in EconPapers)
JEL-codes: C12 C14 C34 C41 C51 (search for similar items in EconPapers)
Date: 2018
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Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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Handle: RePEc:eee:econom:v:206:y:2018:i:1:p:1-38