Quantile regression methods for recursive structural equation models
Abstract
Two classes of quantile regression estimation methods for the recursive structural equation models of Chesher (2003) are investigated. A class of weighted average derivative estimators based directly on the identification strategy of Chesher is contrasted with a new control variate estimation method. The latter imposes stronger restrictions achieving an asymptotic efficiency bound with respect to the former class. An application of the methods to the study of the effect of class size on the performance of Dutch primary school students shows that (i.) reductions in class size are beneficial for good students in language and for weaker students in mathematics, (ii) larger classes appear bene cial for weaker language students, and (iii.) the impact of class size on both mean and median performance is negligible.(This abstract was borrowed from another version of this item.)
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Bibliographic Info
Article provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 134 (2006)
Issue (Month): 2 (October)
Pages: 471-506
Contact details of provider:
Web page: http://www.elsevier.com/locate/jeconom
Related research
Keywords:Other versions of this item:
- Lingjie Ma & Roger Koenker, 2004. "Quantile regression methods for recursive structural equation models," CeMMAP working papers CWP01/04, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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