Introduction

Other things being equal, the best method for forecasting the future values of a given variable would be to build a structural econometric model employing the correct theory, estimate its parameters from an accurate data base, and employ this model to predict the future values of the variable of interest. Since, by construction, such a model embodies the correct economic theory, it must produce forecasts which are, a priori, superior to those derived from other methods. However, the practitioner is seldom in this ideal situation and it is not always possible to construct an econometric model. This is because first, the practitioner may be unclear as to what constitutes the appropriate economic theory. Thus, for example while the theory of the consumption function is relatively uncontroversial, the role of money is not. Second, reliable data on the values of the variables believed to be relevant for the model may not exist. For example, monthly wealth and national income figures are not published in the UK. Third, the cost of constructing and estimating an econometric model may be greater than the perceived benefits from such an exercise, so that a cheaper method of forecasting is sought.

In chapter 1 we discussed some extrapolation methods in which past values of a single series were smoothed to give forecasts. Here we outline some of the more complex procedures, generally known as time-series methods, for univariate forecasting and then discuss multivariate forecasting methods.