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Abstract : 

We study the mistakes that happen in the real-time identification of structural breaks in the selected aggregate-level of the U.S. financial data series. We are interested in the real time identification because of its relevance for forecasting. The level of noisiness of different data sets and techniques used for the identification of breaks affect the frequency of mistakes encountered in real time. We find that mistakes in not finding the true breaks and/or finding the wrong ones in real time are made more frequently in the case of a noisier financial data set. Moreover, the techniques for optimal break detection based on sequential learning of the Bai and Perron (2003) are found to make fewer mistakes than those based on Information Criteria (IC).