Statistical surveillance of business cycles

Abstract

Methods for timely detection of turning-points in business cycles are discussed from a statistical point of view. The theory on optimal surveillance is used to characterize different approaches advocated in literature. This theory is also used to derive a new method for nonparametric detection of turning-points. It utilizes the characteristics of monotonic and unimodal regression. Estimation of parameters m a more or less stable model is thus avoided. Different new ways to evaluate methods are used and discussed. The principles are illustrated by data from Sweden and the USA.