  Recurrent boom-and-bust cycles are a salient feature of economic and
financial history. Cycles found in the data are stochastic, often highly
persistent, and span substantial fractions of the sample size. We refer to such
cycles as "long". In this paper, we develop a novel approach to modeling
cyclical behavior specifically designed to capture long cycles. We show that
existing inferential procedures may produce misleading results in the presence
of long cycles, and propose a new econometric procedure for the inference on
the cycle length. Our procedure is asymptotically valid regardless of the cycle
length. We apply our methodology to a set of macroeconomic and financial
variables for the U.S. We find evidence of long stochastic cycles in the
standard business cycle variables, as well as in credit and house prices.
However, we rule out the presence of stochastic cycles in asset market data.
Moreover, according to our result, financial cycles as characterized by credit
and house prices tend to be twice as long as business cycles.
