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Bayesian Indicator-Saturated Regression for Climate Policy Evaluation

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  • Lucas D. Konrad
  • Lukas Vashold
  • Jesus Crespo Cuaresma

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.

Suggested Citation

  • Lucas D. Konrad & Lukas Vashold & Jesus Crespo Cuaresma, 2026. "Bayesian Indicator-Saturated Regression for Climate Policy Evaluation," Papers 2603.04997, arXiv.org.
    • Handle: RePEc:arx:papers:2603.04997
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    File URL: http://arxiv.org/pdf/2603.04997
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