A Bayesian Framework for Detecting Structural Changes in Time Varying Parameters of Panel Models

Thomas M. Menino Convention & Exhibition Center 

We develop a Bayesian framework for detecting structural breaks in panel data models. Our method offers greater flexibility in detecting structural breaks relative to existing tests by allowing for breaks to occur at different times for distinct parameters. By jointly detecting the presence of structural breaks and estimating parameters, our framework overcomes the problems of inaccurate inference associated with pre-testing in standard test-based approaches to addressing structural breaks. We incorporate unobserved heterogeneity specific to panel settings by allowing for fixed effects or interactive fixed effects in the model. A dynamic extension of the spike-and-slab prior provides the primary mechanism for change point detection and parameter estimation in the sub-periods. We show that we recover structural breaks in a wide range of simulation settings even when standard methods do not identify such breaks. We apply our method on data from Ditzen et al. (2024) to detect whether unconventional monetary policies resulted in structural breaks in bank loan growth.

Structural breaks

Bayesian estimation

Panel data