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Many researchers appear to operate under the impression that causal models lead to more accurate forecasts than those provided by naive models (or “projections”). This study was based on the premise that causal models lead to better forecasts than do naive models in certain situations. The key element of these situations is that there are “large changes.” One situation where large changes might be expected is that of long-range forecasting—and, in particular, long-range forecasting for international markets. Recent improvements in the quality and availability of international data have substantially reduced the cost of developing causal models in this situation. A study of camera markets in seventeen countries indicated that the margin of superiority of causal models over naive models is of great practical importance.
Date Posted: 02 November 2011
This document has been peer reviewed.