A multi-row deletion diagnostic for influential observations in small-sample regressions

Daniel Kaffine and Graham Davis

Computational Statistics & Data Analysis, 2017, vol. 108, issue C, 133-145

Abstract: The inference from ordinary least-squares regressions is often sensitive to the presence of one or more influential observations. A multi-row deletion method is presented as a simple diagnostic for influential observations in small-sample data sets. Multi-row deletion is shown to be complementary to two related diagnostic tests, DFBETAS and robust regression. As an illustration, the technique is applied both to simulated data and to a real data set from an influential study examining the role of institutions for economic growth in resource-rich economies. Multi-row deletion reveals that the key economic insight that institutions matter is sensitive to small variations in sample, indicating additional analysis may be warranted.

Keywords: Regression diagnostics; Multi-row deletion; Inference; Influence points; Outliers; Growth regressions; Institutions; Resource curse (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947316302341
Full text for ScienceDirect subscribers only.

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:eee:csdana:v:108:y:2017:i:c:p:133-145

DOI: 10.1016/j.csda.2016.10.007

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().