Inference on structural breaks using information criteria

Alastair R. Hall, Denise R. Osborn, Nikolaos Sakkas

OAI: oai:purehost.bath.ac.uk:openaire_cris_publications/618d7f14-4295-4ee8-ab82-38274c361c10 • DOI: https://doi.org/10.1111/manc.12017

This paper investigates the usefulness of information criteria for inference on the number of structural breaks in a standard linear regression model. In particular, we propose a modified penalty function for such criteria, which implies each break is equivalent to estimation of three individual regression coefficients. A Monte Carlo analysis compares information criteria to sequential testing, with the modified Bayesian and Hannan-Quinn criteria performing well overall, for data-generating processes both without and with breaks. The methods are also used to examine changes in Euro area monetary policy between 1971 and 2007.