Long-Range Forecasting for a Consumer Durable in an International Market

Abstract

There has been a substantial amount of interest recently in long-range planning. One necessary component of the long-range plan Is the long-range forecast. In contrast to the emphasis on the planning process, however, little attention has been given to forecasting. This study considers the problem of long-range forecasting in a situation which is of growing importance - forecasting sales for international markets.Many researchers appear to operate under the impression that causal models (i.e., models based on an analysis of underlying factors) lead to more accurate sales forecasts than those provided by naive models (i.e., projections based on historical sales data only). A survey of the research literature led to the conclusion that this confidence in causal models is virtually unsupported. One can hardly criticize firms, then, for relying primarily upon naive models for sales forecasting since these models are simpler and less expensive than causal model.This study was based on the hypothesis that causal models are superior to naive models in certain situations. The key element of these situations is that -there are 'large changes.' Long-range sales forecasting usually involves such large changes; and there are many reasons to expect that long-range forecasting for international markets is a situation in which substantial changes will occur (e.g., the Kennedy round tariff cuts and the formation of common markets.)A causal model was developed to provide long-range forecasts of the international market for still cameras. This model provided unconditional forecasts of unit camera sales by country for year t n on the basis of l) knowledge about camera sales in year t and 2) predicted changes in four causal variables from year t to t n. These four causal variables included, in order of importance, per capita income, price of cameras, number of potential buyers and quality of cameras.The predictive ability of the causal model was superior to that of a naive model purporting to represent current practice. Each model was used to provide backcasts of 1954 camera sales in 17 countries on the basis of data from 1967 to 1960 only. The mean absolute percentage error for the causal model was 23% while that for the naive model was 43%. This result was statistically significant (0? = .05); but, more importantly, it appeared to have great practical significance. An evaluation, based on very conservative subjective estimates, indicated that such an improvement in accuracy would have a present value worth in excess of one percent of a typical firm's yearly sales volume.Further support for the use of the causal model was obtained by noting that the standard errors of the estimated relationships were low (evidence of reliability), that the estimates of causal relationships from different measurement models were in rather close agreement (evidence of construct validity), and that the causal model performed well in another situation where predictions were provided for I960-65 camera sales in 11 'new' countries (evidence of concurrent validity).The causal relationships were initially specified by a subjective analysis. Various parts of the causal model were then updated by use of a number of measurement models including an analysis of differences among sales rates for 30 countries, of differences among changes in the sales rates from 1961 to 1965 for 21 countries, and of differences among six income categories from United States household survey data. This updating led to a modest, though valuable, gain as the mean absolute percentage error of the 1954 backcast was reduced from 30% to the 23% mentioned above.Additional benefits associated with the development of the causal model included the ability to evaluate large changes in the market; to estimate current sales where trade and production figures are inadequate; to evaluate alternative assumptions about the future rapidly and cheaply; and to identify markets which have not been fully exploited.In summary, the study argues that the development of better long-range forecasting models is an important problem; describes the development of causal models; and demonstrates the superiority of causal models over naive models in a case involving long-range forecasting for international markets.