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Bayesian Indicator-Saturated Regression for Climate Policy Evaluation

Lucas D. Konrad, Lukas Vashold, Jesus Crespo Cuaresma

arXiv 5 Mar 2026 · Econometrics

arXiv:2603.04997 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

31
references
66
in-text mentions
31
distinct cited
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self-citations
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main-text words

appendix boundary found by appendix_titled_section at “Appendix: Selected simulation results ” · 98% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Koch, Nicolas and Naumann, Lennard and Pretis, Felix and Ritter, Nol… (2022) Attributing agnostically detected large reductions in road CO2 emissions to policy mixes1.000114100%
2Pretis, Felix and Schwarz, Moritz (2022) Discovering What Mattered: Answering Reverse Causal Questions by Detecting Unknown Treatment Assignment and Timing as Breaks in…1.00093100%
3Castle, Jennifer L. and Doornik, Jurgen A. and Hendry, David F. and… (2015) Detecting Location Shifts during Model Selection by Step-Indicator Saturation1.00053100%
4Stechemesser, Annika and Koch, Nicolas and Mark, Ebba and Dilger, El… (2024) Climate policies that achieved major emission reductions: Global evidence from two decades0.81142100%
5Valen E. Johnson and David Rossell (2010) On the Use of Non-Local Prior Densities in Bayesian Hypothesis Tests0.73732100%
6Jeffreys, Harold (1998) The theory of probability0.64422100%
7Tibshirani, Robert (1996) Regression shrinkage and selection via the Lasso0.64422100%
8George, Edward I. and McCulloch, Robert E (1997) Approaches for Bayesian variable selection0.51121100%
9Ishwaran, Hemant and Rao, J. Sunil (2005) Spike and slab variable selection: Frequentist and Bayesian strategies0.51121100%
10Valen E. Johnson and David Rossell (2012) Bayesian Model Selection in High-Dimensional Settings0.51121100%

Showing the top 10 of 31 scored citations.