Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/235351 
Year of Publication: 
2021
Series/Report no.: 
CESifo Working Paper No. 8981
Publisher: 
Center for Economic Studies and Ifo Institute (CESifo), Munich
Abstract: 
Assessing the robustness of the results of econometric analysis is a long standing subject of lively research. The majority of the literature focuses on sensitivity to model specification, while the quantification of sensitivity to sets of influential observations has received relatively little attention. A major obstacle in this context is masking, a phenomenon where influential observations obscure each other, which makes their identification particularly challenging. We show how inferential measures are affected by influential sets of observations and present two adaptive algorithms aimed at identifying such sets. We demonstrate the merits of these algorithms via simulation studies and empirical applications. These exercises show that masking problems and a pronounced sensitivity to influential sets are present in a wide range of scenarios. Overall, our findings suggest that increased attention to influential sets is warranted and comprehensive robustness measures for regression analysis are required.
Subjects: 
regression diagnostics
robustness
masking
influence
JEL: 
C18
C20
Document Type: 
Working Paper
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