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

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  • Kaffine, Daniel T.
  • Davis, Graham A.

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.

Suggested Citation

  • Kaffine, Daniel T. & Davis, Graham A., 2017. "A multi-row deletion diagnostic for influential observations in small-sample regressions," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 133-145.
    • Handle: RePEc:eee:csdana:v:108:y:2017:i:c:p:133-145
    DOI: 10.1016/j.csda.2016.10.007
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    4. Raphaelle G. Coulombe & Akhil Rao, 2023. "Fires and Local Labor Markets," Papers 2308.02739, arXiv.org.

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