Journal article
A branch-and-bound algorithm for instrumental variable quantile regression
Mathematical programming computation, Vol.9(4), pp.471-497
12/01/2017
DOI: 10.1007/s12532-017-0117-2
Abstract
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.
Details
- Title: Subtitle
- A branch-and-bound algorithm for instrumental variable quantile regression
- Creators
- Guanglin Xu - University of IowaSamuel Burer - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Mathematical programming computation, Vol.9(4), pp.471-497
- Publisher
- Springer Nature
- DOI
- 10.1007/s12532-017-0117-2
- ISSN
- 1867-2949
- eISSN
- 1867-2957
- Number of pages
- 27
- Language
- English
- Date published
- 12/01/2017
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380457502771
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