Inference on Structural Breaks using Information Criteria

Abstract

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. AMonteCarlo 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 inEuro area monetary policy between 1971 and 2007.

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