Mathematical Programming Computation, Volume 9, Issue 4, December 2017
A branch-and-bound algorithm for instrumental variable quantile regression
Guanglin Xu, Samuel Burer
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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