Long-Range Forecasting For International Markets: The Use of Causal Models

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. Disciplines Business | Marketing Comments Suggested Citation: Armstrong, J.S. (1968). Long-Range Forecasting For International Markets: The Use of Causal Models. In Marketing and the New Science of Planning. by Robert L. King. American Marketing Association. © 1968 American Marketing Association This book chapter is available at ScholarlyCommons: http://repository.upenn.edu/marketing_papers/172 Long-Range Forecasting For International Markets: The Use of Causal Models J. SCOTT ARMSTRONG * 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. Models for sales forecasting may be divided into two categories—naive and causal. Native models at tempt to forecast by using only historical sales data. Causal models 1 attempt to go beyond sales data to utilize other variables (i.e., the causal variables) in making sales forecasts. Figure 1 provides illustrations of each type of model where “Y” represents sales of a given product, “X” represents the set of causal vari ables, and “t” represents the current year. The ob jective of each approach is to forecast sales in year t+n. The naive model does this directly. The cau sal model relates sales to other variables which, in essence, makes the problem one of forecasting “other variables” and using these forecasts to estimate sales. These two approaches are actually extreme points of a continuum. Development of a forecasting model may borrow from both the naive and the causal ap proaches. Examples of the naive model approach would be use of simple trend projections or use of moving averages (such as exponential smoothing). I Causal is to be interpreted in its common-sense meaning. A causal variable, X, is one which is necessary or sufficient to the occurrence of an effect, Y, and X precedes Y in time. An interesting interpretation of the literature on causal models may be found in Hubert M, Blalock, Jr., Causal lnjrrences in Nonex periinental Research (Chapel Hill: University of North Carolina Press, 1964). * Assistcnt Professor of Marketing, Wharton School, University of Pennsylvania Et-2’Yt1’jHt+n Examples of the causal model approach would be those based on “econometric” models with no tagged sales variables as predictors. 2 In practice, these models are not so pure: the naive approach is often tempered by a subjective appraisal of underlying causal factors, while the causal model generally uses knowledge about cur rent sales rates and attempts only to predict changes in sales. The causal and naive approaches are contrasted in Figures 2 and 3. It should be noted that the developI Econometricians prefer the word “structural” to “causal.” FIGURE 1 BASIC APPROACHES TO FORECASTING