INFERENCE ON STRUCTURAL BREAKS USING INFORMATION CRITERIA

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.