Quantile Models and Estimators for Data Analysis

G. W. Bassett (), M. -Y. S. Tam () and K. Knight ()
Additional contact information
G. W. Bassett: University of Illinois at Chicago
M. -Y. S. Tam: University of Illinois at Chicago
K. Knight: University of Toronto, Department of Statistics

A chapter in Developments in Robust Statistics, 2003, pp 77-87 from Springer

Abstract: Summary Quantile regression is used to estimate the cross sectional relationship between high school characteristics and student achievement as measured by ACT scores. The importance of school characteristics on student achievement has been traditionally framed in terms of the effect on the expected value. With quantile regression the impact of school characteristics is allowed to be different at the mean and quantiles of the conditional distribution. Like robust estimation, the quantile approach detects relationships missed by traditional data analysis. Robust estimates detect the influence of the bulk of the data, whereas quantile estimates detect the influence of co-variates on alternate parts of the conditional distribution. Since our design consists of multiple responses (individual student ACT scores) at fixed explanatory variables (school characteristics) the quantile model can be estimated by the usual regression quantiles, but additionally by a regression on the empirical quantile at each school. This is similar to least squares where the estimate based on the entire data is identical to weighted least squares on the school averages. Unlike least squares however, the regression through the quantiles produces a different estimate than the regression quantiles.

Keywords: Student Achievement; Quantile Regression; Policy Variable; School Characteristic; Quantile Estimate (search for similar items in EconPapers)
Date: 2003
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-57338-5_6

Ordering information: This item can be ordered from
http://www.springer.com/9783642573385

DOI: 10.1007/978-3-642-57338-5_6

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().