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

Citation data:

Mathematical Programming Computation, ISSN: 1867-2949, Vol: 9, Issue: 4, Page: 471-497

Publication Year:
2017
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Readers 2
Citations 1
Citation Indexes 1
DOI:
10.1007/s12532-017-0117-2
Author(s):
Guanglin Xu; Samuel Burer
Publisher(s):
Springer Nature
Tags:
Mathematics; Computer Science
article description
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 NP-hard—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.