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Robust Methods for Anomaly Detection in Econometrics
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Anomaly detection plays a critical role in econometrics by identifying unusual patterns and data irregularities that can distort inference and lead to misleading conclusions. Economic data is often noisy and affected by reporting errors and rare but impactful events, making traditional detection methods insufficient. Robust methods for anomaly detection seek to address these challenges by remaining reliable while preserving interpretability and statistical validity. Developing and applying such methods is essential for improving the accuracy of econometric analysis and supporting sound economic policy and decision-making. Robust Methods for Anomaly Detection in Econometrics explores innovations surrounding anomalous observations in modern quantitative research. It examines influential observations in complex, high-volume, and high-velocity data settings. Covering topics such as econometrics, robust methods, and research, this book is an excellent resource for academicians, researchers, policy makers, software engineering, and graduate students.
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