Testing heterogeneity in quantile regression: a multigroup approach

Cristina Davino (), Giuseppe Lamberti () and Domenico Vistocco
Additional contact information
Cristina Davino: University of Naples Federico II
Giuseppe Lamberti: University of Naples Federico II

Computational Statistics, 2024, vol. 39, issue 1, No 6, 117-140

Abstract: Abstract The paper aims to introduce a multigroup approach to assess group effects in quantile regression. The procedure estimates the same regression model at different quantiles, and for different groups of observations. Such groups are defined by the levels of one or more stratification variables. The proposed approach exploits a computational procedure to test group effects. In particular, a bootstrap parametric test and a permutation test are compared through artificial data taking into account different sample sizes, and comparing their performance in detecting low, medium, and high differences among coefficients pertaining different groups. An empirical analysis on MOOC students’ performance is used to show the proposal in action. The effect of the two main drivers impacting on performance, learning and engagement, is explored at different conditional quantiles, and comparing self-paced courses with instructor-paced courses, offered on the EdX platform.

Keywords: Quantile regression; Multigroup approach; Testing heterogeneity (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-023-01371-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:compst:v:39:y:2024:i:1:d:10.1007_s00180-023-01371-3

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-023-01371-3

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().