Bayesian Indicator-Saturated Regression
Published in Forthcoming in the Econometrics Journal, 2026
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
- International Conference on Economic Modeling and Data Science (EcoMod 2026)
- Asia Meeting of the Econometric Society, East & Southeast Asia (AMES 2026)
- European Meeting of the Econometric Society (EEA-ESEM 2026)
Status: Forthcoming in the Econometrics Journal
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" arXiv.
Download Paper | Download Bibtex