Confirmation, Correction and Improvement for Outlier Validation using Dummy Variables
Dummy variables can be used to detect, validate and measure the impact of outliers in data. This paper uses a model to evaluate the effectiveness of dummy variables in detecting outliers. While generally confirming some findings in the literature, the model refutes the presumption that the t˗stat...
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| Format: | Article |
| Language: | English |
| Published: |
Econometric Research Association
2013-09-01
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| Series: | International Econometric Review |
| Subjects: | |
| Online Access: | http://www.era.org.tr/makaleler/30050079.pdf |
| Summary: | Dummy variables can be used to detect, validate and measure the impact of outliers in
data. This paper uses a model to evaluate the effectiveness of dummy variables in
detecting outliers. While generally confirming some findings in the literature, the model
refutes the presumption that the t˗statistic or the F˗incremental statistic is enough to
validate an observation as an outlier. In order to rectify this fallacy, this paper
recommends an easily-calculable robust standardized residual statistic that is more
compatible with the definition of outliers.
The robust standardized residual statistic suggested herein is still used in many robust
regression methods and is more effective than the t˗statistic or the F˗incremental statistic
in validating outliers with dummy variables. The results of this study suggest some
practical recommendations for dealing with outliers and improvements in maintaining the
integrity of data. We recommend all previous studies using this statistics be revised in
light of the findings presented in this paper. |
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| ISSN: | 1308-8793 1308-8815 |