Understanding Beyond the Mean with Distributional Regression Models

Moving beyond the traditional mean-focused analyses, this thesis presents three innovative distribution modeling strategies for exploring multivariate independent and time series data. These models offer a complete picture of the underlying data distribution and reveal critical insights into data characteristics such as skewness, tail risks, and variability that mean-focused analyses might overlook. Empirically, these models have been successfully applied across various fields, including insurance, economic forecasting, and policy-making, providing a solid foundation for informed economic decision-making and effective policy formulation.