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

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.