Bayesian Indicator-Saturated Regression for Climate Policy Evaluation

Abstract: Structural break identification methods are an important tool for evaluating the effectiveness of climate change mitigation policies. In this paper, we introduce a unified probabilistic framework for detecting structural breaks with unknown timing and arbitrary sequence in longitudinal data. The proposed Bayesian setup uses indicator-saturated regression and a spike-and-slab prior with an inverse-moment density as the slab component to ensure model selection consistency. Simulation results show that the method outperforms comparable frequentist approaches, particularly in environments with a high probability of structural breaks. We apply the framework to identify and evaluate the effects of climate policies in the European road transport sector.

Presented at:

• What Works Climate Solutions (WWCS) 2024
• International Society for Bayesian Analysis (ISBA) World Meeting 2024
• European Seminar on Bayesian Econometrics (ESOBE) 2024
• University of Melbourne - Econometrics Seminar 2025
• Monash University - EBS seminar 2025

Status: Work in progress

Co-authors: Lukas Vashold, Jesús Crespo Cuaresma

Keywords: Panel Data, Structural Breaks, Bayesian Statistics, Indicator Saturation

Recommended citation: Konrad, L.D., Vashold, L., Crespo Cuaresma, J. (2026). "Bayesian Indicator-Saturated Regression for Climate Policy Evaluation" arXiv.
Download Paper