Early detection or even timely recognition of business cycle turning points has always been a major concern of policy makers, businesses, and investors. Clearly, early recognition would allow the government policy maker to trigger countercyclical policy measures, businesses to change their own sales or investment strategy, and investors to reallocate assets among alternative investments to optimize their return. The typical way of monitoring and forecasting cyclical turning points is to use leading indicators. Unfortunately, no leading indicator is 100 percent perfect, which means it is sometimes difficult to tell whether or not the leading indicator signal is real. Over the years, numerous systems have been developed to screen out false signals. When these systems were put to the real-life test of forecasting turning points, some of these systems have worked well while others have not. Nearly all the methods for screening turning point signals have been ad hoc creations that may or may not have credibility with other users. However, there is one method, proposed by Salih Neftci of City University of New York (CUNY), that adds a new dimension to screening out false signals. This method is based on economic theory and statistical methods.

Neftci has proposed a method that uses sequential analysis to calculate the probability of a cyclical turning point. This method is based on a theoretical and empirical claim that the onset of a recession is marked by a pronounced decline in aggregate economic activity.