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.