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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
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DOI: 10.1016/j.csda.2016.10.007

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