Fixed Effect

Fixed effects modeling aims to isolate the impact of variables of interest while controlling for unobserved, time-invariant influences that affect individual units (e.g., individuals, firms, countries). Current research focuses on addressing challenges arising from high-dimensionality, non-linear relationships, and the potential for misinterpretations due to overfitting or the improper generalization of findings (e.g., the "fixed-effect fallacy"). This includes developing robust estimation methods, such as double machine learning and kernel-weighted approaches, particularly for multidimensional panel data with interactive fixed effects. Improved understanding and application of fixed effects models are crucial for accurate causal inference across diverse fields, from econometrics and policy evaluation to the assessment of large language model capabilities.