Inference on structural breaks using information criteria

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

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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.
Original languageEnglish
Pages (from-to)54-81
JournalThe Manchester School
Issue numberS3
Early online date29 Jul 2013
Publication statusPublished - 2013


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