A technique for forecasting business-cycle turning points developed by Neftci (1982). It signals cyclical turning points by calculating the likelihood that the economic environment has changed.  A turning-point probability signal occurs when the estimated probability reaches some preset level of statistical confidence (say 90% or 95%).  The likelihoods are based on (1) the probability that the latest observation comes from a recession (or a recovery) sample, (2) the chance of recession (or recovery) given the length of the current cyclical phase in comparison to the historical average, and (3) the comparison of 1 and 2 with the previous month's probability estimate.