A branch-and-bound algorithm for instrumental variable quantile regression

  • This paper studies a statistical problem called instrumental variable quantile regression (IVQR). We model IVQR as a convex quadratic program with complementarity constraints and—although this type of program is generally NPhard—we develop a branch-and-bound algorithm to solve it globally. We also derive bounds on key variables in the problem, which are valid asymptotically for increasing sample size. We compare our method with two well known global solvers, one of which requires the computed bounds. On random instances, our algorithm performs well in terms of both speed and robustness.

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Metadaten
Author:Guanglin Xu, Samuel Burer
DOI:https://doi.org/10.1007/s12532-017-0117-2
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2017
Tag:Software; Theoretical Computer Science
Volume:9
Issue:4
Page Number:27
First Page:471
Last Page:497
Mathematical Programming Computation :MPC 2017 - Issue 4
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