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Outliers Detection Methods in Panel Data Regression: An Application to Environment Science
The existence of outliers in panel data may violate the assumption of classical OLS estimator. They may lead to misestimating the relationship between variables. The main objective of this study is to detect the outliers, leverages and the influence points by using different diagnostic methods. The other objective is to measure the effects of outliers on OLS estimator by comparing the results of using the sample with outliers and the samples after removing the outliers. The analyses are applied to the environmental science data on modelling the relationship between CO2 emissions, energy consumption and economic growth for two groups of countries, i.e. the developed versus the developing countries. The data take the range between1960 to 2010. Our results reveal that the Robust Mahalanobis distance method outperforms the other methods in identifying the outliers as it uses the robust mean and robust variance estimation. Besides, the estimated model after excluding the outliers from the analysis could provide a more accurate estimation. Comparing the results between two groups of countries, our results show that developing countries group has lower CO2 emissions by 0.80 unit in compare to the developed countries group.
Outlier detection, Robust Mahalanobis distance, CO2 emissions.
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