Introduction

In this chapter we are concerned with methods of forecasting in the microeconomic environment. That is, at the level of a particular firm or industry rather than with the whole economy (which is the subject of chapter 5). In chapters 2 and 3 we saw various time-series methods which can be applied in any circumstances, given the required data. They are basically non-causal extrapolative methods. Here the approach adopted is to assume that a causal model will help in understanding behaviour and so will produce accurate forecasts. Whether it is worthwhile constructing such a model depends on the costs and benefits of the forecasting process. The costs can generally be estimated but the benefits are more difficult to assess. This is particularly the case when the choice is between a virtually costless naive model, say, a random walk or simple extrapolation model, and a complex causal model which is expected, ex ante, to give more accurate forecasts, but requires much judgement by the modeller.

We take as our starting point demand analysis, in which the theory of consumer behaviour is used to model how individuals behave in a perfectly competitive market. By making simplifying assumptions it is possible, in principle, to construct an economic model which attempts to explain demand in terms of observed prices, incomes, advertising and other variables. With further assumptions it is possible to make forecasts of their future values.