Quantile regression methods for recursive structural equation models
Lingjie Ma and
Additional contact information
Lingjie Ma: Institute for Fiscal Studies
No CWP01/04, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
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.
Pages: 36 pp.
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
Our link check indicates that this URL is bad, the error code is: 404 Not Found (http://cemmap.ifs.org.uk/wps/cwp0401.pdf [301 Moved Permanently]--> http://www.cemmap.ac.uk/wps/cwp0401.pdf)
Journal Article: Quantile regression methods for recursive structural equation models (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ifs:cemmap:01/04
Ordering information: This working paper can be ordered from
The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE
Access Statistics for this paper
More papers in CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies The Institute for Fiscal Studies 7 Ridgmount Street LONDON WC1E 7AE. Contact information at EDIRC.
Bibliographic data for series maintained by Emma Hyman ().