AbstractBusiness cycle forecasting involves several different methodological problems. Some of these are discussed in the current issue of this journal and are introduced in this paper. The forecasting approach itself often focuses on turning points in the business cycle and a number of papers in this issue examine this particular aspect of business cycle forecasting. For example, a Bayesian technique for detecting changing random slopes in leading composite indexes is discussed. Business survey data are often used in the process of forecasting business cycles. A Kalman filtering procedure is suggested to make business survey information useful in predicting changes in industrial production. Other data issues of relevance to this topic that are discussed include the preliminary data and revision problem. Methodology for using high‐frequency data and for converting high‐frequency to low‐frequency data is also presented. A number of the papers discuss the analysis of dynamic structures, such as the existence of time‐varying dynamics, and the use of vector‐autoregressive (VAR) models. Finally, a few comments are made on general structural variability aspects, related to business cycle fo