03: Assessing the Robustness of AR Models in the Presence of Non-normality: A Simulation Study

Music City Center 

In time series modeling, it is common to assume that innovations follow a normal distribution. However, this assumption does not always hold in real-world scenarios. Environmental datasets, in particular, often contain extreme values that violate normality. Through a comprehensive simulation study, we demonstrate that traditional AR(q) models can produce inaccurate results when innovations deviate from normality, especially when they exhibit skewness. Our findings highlight that outliers can distort estimates, introduce bias, and compromise the generalizability of results.

Autoregressive Models

Robustness

Outliers

Skew distributions 

Biometrics Section