Anomalous observations are no longer rare irregularities in econometric data, but recurring features of modern empirical environments shaped by measurement heterogeneity, nonstationary, and structural change. In such settings, conventional estimation and inference can become fragile, allowing a...

Robust econometrics addresses the fragility of classical estimation and inference in anomaly-prone economic data. This chapter integrates robust estimation, robust inference, and anomaly diagnostics into a unified framework, highlighting the influence of outliers, structural breaks, and...

Econometric analysis is often based on clean and well-behaved data assumptions but financial and economic data in the real world is often contaminated by outliers, measurement error, missing cases and misspecification of the model. The parameter estimates when classical econometric methods are...

This chapter develops a robust econometric framework for analyzing macroeconomic and financial data in the presence of outliers, heavy tails, and structural instability. Classical econometric models rely on assumptions of normality and parameter stability that are frequently violated in real...

Economic and financial data are increasingly high-dimensional and anomaly-prone, challenging classical econometric assumptions. This chapter presents robust econometrics as a framework for reliable empirical analysis under contamination and instability. It reviews key theoretical foundations of...

This chapter, according to the authors, looks at making modeling robust and interpretable when faced with complex, irregular, and contaminated data environments. As empirical research continues to move towards larger datasets having numerous variables and high dependency among variables, along...

Anomalies, outliers, structural breaks, and irregular observations are no longer peripheral disturbances in empirical research; they have become defining characteristics of modern datasets across finance, macroeconomics, public policy, healthcare, and IoT environments. Traditional econometric...

This chapter per the authors examines anomaly detection as a central element of empirical analysis in the presence of complex, structurally unstable, and high-dimensional economic data. Classical empirical methods based on restrictive assumptions often generate fragile inference when confronted...

Anomalies in econometric data arise from measurement error, rare shocks and structural change and may distort identification, bias estimation and invalidate standard inference. Because anomaly labels are often incomplete, selectively observed or endogenous, anomaly detection cannot be treated as...

The three stage framework whose main objective is to perform robust econometric estimation and inference for anomaly detection is proposed. The first stage focuses on rigorous econometric modeling to obtain a stable, theory, based benchmark against which we can measure the impact of our model....