Bayesian Indicator-Saturated Regression

Lucas D. Konrad, Lukas Vashold and Jesus Crespo Cuaresma

Papers from arXiv.org

Abstract: Structural break detection has emerged as an important tool for assessing the effects of policies in settings where conventional policy evaluation methods might not be applicable.In this paper, we introduce a unified Bayesian framework for detecting structural breaks with unknown timing and arbitrary sequence in longitudinal data. The proposed setup builds on a indicator-saturated regression design and uses a spike-and-slab prior for selection among indicators. We establish that a non-local prior as the slab component is a necessary condition to provide model selection consistency in this model class. Simulation results show that the method outperforms comparable frequentist approaches, particularly in environments with a high probability of structural breaks. We illustrate the proposed framework by analysing climate policies in the European road transport sector.

Date: 2026-03, Revised 2026-08
New Economics Papers: this item is included in nep-ecm, nep-ene and nep-env
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