Is the Endogenous Business Cycle Dead?

Abstract

A clearly defined dichotomy exists in the business cycle literature between endogenous and exogenous cycles. Exogenous cycles are either temporary, heavily damped random deviations from a stable long-run growth path or permanent stochastic fluctuations in the growth path which both require repeated stochastic impulses to generate typically observed recurrent and irregular fluctuations. In contrast, endogenous cycles are systematic (deterministic), self-generating recurrent cycles that result from the inherent instability (structure) of the underlying economy. The most recent and most severe critiques of endogenous theory are empirical in nature and stem from the unit root debates which contrast trend stationary (TS) and difference stationary (DS) models. Despite this critique, the evolution of this methodology has produced conflicting results with respect to the most appropriate model. More importantly this approach implicitly rejects, through the use of an overly restrictive specification, endogenous cycles in favor of stochastic cycles. In this light, the purpose of this paper is to justify and apply an alternative, more general, estimation framework that includes DS, TS and endogenous cycles as nested alternatives. In particular, I employ a structural time series (STS) or unobserved components methodology which allows for a direct empirical test of endogenous cycle theory against stochastic alternatives and/or mixed stochastic-endogenous models. The integration of secular regime shifts into the basic STS model effectively introduces nonlinearities and thus moves the analysis one step beyond simple linear models. This general approach which relies on economic theory for model specification is superior to the ARIMA-based unit root methodology which relies solely on the data to identify the structure of macro time series. Using this approach, I estimate STS models for seven relevant U.S. macroeconomic time series and find that endogenous cycles play a fundamental role in characterizing the data generation process. The remainder of this paper is organized in the following manner. Section II reviews the restrictive nature of the unit root-ARIMA methodology. Section III offers an alternative approach. Section IV presents estimation results and section V contains my conclusions.