BUSINESS-CYCLE FORECASTING

Business cycle forecasting involves several different methodological p roblems. Some of these are discussed in the current issue of this jour nal 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 surv ey data are often used in the process of forecasting business cycles. A Kalman filtering procedure is suggested to make business survey info rmation useful in predicting changes in industrial production. Other d ata issues of relevance to this topic that are discussed include the p reliminary data and revision problem. Methodology for using high-frequ ency data and for converting high-frequency to low-frequency data is a lso presented. A number of the papers discuss the analysis of dynamic structures, such as the existence of time-varying dynamics, and the us e of vector-autoregressive (VAR) models. Finally, a few comments are m ade on general structural variability aspects, related to business cyc le forecasting.