Knowing the Cycle

Policy makers are primarily interested in fluctuations in the level of activity  the classical cycle. Academics have in recent times focused their efforts on studying fluctuations and comovement in aggregate variables that have been rendered stationary after some appropriate transformation. That is academics focus on the growth cycle. One reason for this shift in focus was the impression among academics that Burns and Mitchell’s work lacked the precision required in modern macroeconomics. In this paper we show that pattern recognition algorithms which emulate Burns and Mitchell’s approach to the cycle can be constructed and used to collect precise information on the classical cycle. The information so marshaled comprises the duration, amplitude, and cumulative movements of output within business cycle phases. We show that this information can be used to assess a range of business cycle models that have been proposed in the literature.