T HAS long been recognized that a good economic forecast should consist of several components. In addition to predicting turning points, a forecast should theoretically include some statements about the timing of the and the amplitude and duration of the subsequent movement. If the forecaster makes quarterly quantitative estimates of GNP, he is, in fact, estimating all of the aforementioned components. However, when forecasters use other types of predictions, they usually do not provide estimates of the timing or amplitude. This is especially true when analysts speak of the forecasting behavior of the leading series and/or the rate of change methods. While has been considerable discussion about the success of these methods in forecasting turning points, I very little is known about other aspects of their forecasting behavior. It has generally been concluded, that, in practice, the leading series and rate of change methods predict every turning point of a predictand such as the Federal Reserve Board's Index of Industrial Production, but they also display a large number of false leads. As for their other forecasting characteristics, Moore2 has concluded that some information about the amplitude of a recession could be obtained about six months after the movement first began. Finally Wright and Okun have attempted to estimate the dates of turning points, I but no attempt has been made to attach probabilities to the predicted dates of turning points. Our paper will present one method for attaching probabilities to the turning point forecasts which are obtained from using the leading series and diffusion indexes. The probability must refer to the likelihood of the predictand's occurring in a given time interval. Statements such as there will be a turn or there is an X per cent chance of a turn are tautologies, for sooner or later will be a turn. Thus to have any usefulness, the probabilities must be attached to specific time periods. In the following section we shall outline the methodology of the study, discuss the data to which this methodology was applied, and finally present the results.

The article reviews the book "Quantitative Analysis for Business Decisions," by Harold Bierman Jr., Charles P. Bonini, Lawrence E. Fouraker and Robert K. Jaedicke.

The article focuses on the application of multiple correlation analysis in multiple product costing. The allocation of costs assumes that the products should receive costs relative to the benefits that the products received from the production process. The authors conclude that multiple correlation analysis frees the accountant from making the assumption that marginal costs per product are always too difficult to unearth. For products that have an output proportion that is variable, multiple correlation allocates costs in a method valuable for decision-making.