Forecasting for planning: Causal techniques

Abstract

In our previous articles we have described how turning points can be identified and how qualitative techniques can be applied when either sudden system changes have occurred or there are relatively few available data and various judgmental/expert opinion techniques must be utilized. Time‐series analysis techniques help identify systematic variation in historical data and provide the basis for future statistical projections. As knowledge of market, economics, and other dynamics is obtained from market research, statistical analysis, and experiments, such information should be incorporated into causal forecasting models. In this article, we will consider some of the more commonly used causal models and their forecasting accuracy, with major emphasis on econometric, marketing, and simulation models.

Citation

Chambers, J.C. and Mullick, S.K. (1976), "Forecasting for planning: Causal techniques", Planning Review, Vol. 4 No. 1, pp. 17-19. https://doi.org/10.1108/eb053747