Premium
Business cycle forecasting
Author(s) -
Westlund Anders H.
Publication year - 1993
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980120302
Subject(s) - business cycle , relevance (law) , computer science , autoregressive model , econometrics , kalman filter , bayesian probability , operations research , economics , artificial intelligence , engineering , political science , keynesian economics , law
Business 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 forecasting.