Varying Coefficient

Varying coefficient models analyze data where the relationships between variables change over time or other covariates. Current research focuses on developing robust and efficient methods to estimate these time-varying coefficients, employing techniques like sparse regression, tree-based models (e.g., gradient boosting), and neural networks within state-space frameworks. These advancements are improving the accuracy and interpretability of models across diverse fields, including time series forecasting, system identification, and health outcome studies, by allowing for more nuanced representations of complex dynamic systems.