Bayesian Indicator-Saturated Regression for Climate Policy Evaluation

Lucas D. Konrad, Lukas Vashold and Jesus Crespo Cuaresma

Papers from arXiv.org

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.

Date: 2026-03
New Economics Papers: this item is included in nep-ecm and nep-ene
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