Data-Driven Tax Management: Developing a Long-Run Analysis System for Niger through Outlier Detection
Keywords:
Cointegration, CRISP-DM, Data mining, Outlier detection, Tax administration, Vector error correction model.Abstract
Developing countries often face challenges in conducting long-term economic analyses, which in turn affects their ability to design effective planning and policy decisions. This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to support Niger’s tax administration in implementing a long-run analysis scheme. Several statistical and machine learning tools, such as boxplots, the interquartile range (IQR), the augmented Dickey-Fuller test, the Johansen test, and the vector error correction model (VECM) were employed. The dataset covers the period from January 1996 to December 2014 and reveals seven (7) outliers. Results showed that VAT, ITS, and ISB account for 93.64% of revenues in the dataset with outliers and 94.25% without outliers, confirming that cointegration tests were highly sensitive to outliers. Both datasets were non-stationary but cointegrated, with rank three (3). Tax revenue took approximately 81 days to absorb shocks with outliers, compared to 60 days without. Outliers, thus, significantly distorted the Nigerien economic planning and policy outcomes.
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