Knowledge that Transforms

INVENTORY CYCLE analysis has so far assumed sales forecasts by firms-projecting a constant multiple of either the latest level, or the latest rate of change, of sales into the future. This structure is adequate to show the way in which expectations and the level of business activity interact, but it has a certain ring of implausibility. Other ways of forecasting sales change the behavior of the model, so we may ask if stability is particularly sensitive to the technique assumed, and if more realistic forecasting techniques will enhance or diminish cyclical stability. Here I propose one forecasting technique that pretends to added realism (while remaining sufficiently simple to be incorporated in a formal inventory cycle model) and partly evaluate its effect on cyclical stability. If we are to shift our attention to forecasting models very far removed from naive extrapolation, we must endow the hypothetical seer with information beyond his own past sales levels. A wide choice of alternative assumptions confronts us; casual inspection of forecasting texts will show that some firms find it useful to look at trends in national income or similar aggregates, while others find more specific indicators useful: automobile tire manufacturers use estimates of the stock of cars on the road, for example. But many of these alternatives are specific to a particular industry; here we seek a model that could plausibly be used by most industries, one which will take account of industry differences while using one functional form (for mathematical simplicity). We shall assume that the forecaster knows which sectors of the economy buy the output of his industry, and has some idea of both the trends in