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LESSON: Business and Economic Forecasting Reference: Study Guide and Casebook for Managerial Economics By Edwin Masfield and Elizabeth Mansfield Although surveys are of considerable use, most major firms seem to base their forecasts in large part of the quantitative analysis of economic time series. The classical approach to business forecasting assumes that an economic time series can be decomposed into four components: 1. Trend 2. Seasonal variation 3. Cyclical variation 4. Irregular movements If the trend in the time series is linear, simple regression may be used to estimate an equation representing the trend. If it seems to be nonlinear, a quadratic equation may be estimated by multiple regression, or an exponential trend may be fitted The seasonal variation in a particular time series is described by a figure for each month (the seasonal index) that shows the extent to which the month's value typically departs from what would be expected on the basis of trend and cyclical variations. Such seasonal indexes can be used to deseasonalize a time series, that is, to remove the seasonal element from the data. Many business and economic time series go up and down with the fluctuations of the economy as a whole. This cyclical variation, as well as the trend and seasonal variations, is reflected in many time series. It is customary to divide business fluctuations into four phases: 1. Trough 2. Expansion 3. Peak 4. Recession Variables go down before the peak and up before the trough are called leading series. Some important leading series are new orders for durable goods, average work week, building contracts, stock prices, certain wholesale prices, and claims for unemployment insurance. Economists sometimes use leading series, which are often called leading indicators, to forecast whether a turning point is about to occur. If a large number of leading indicators turn downward, this is viewed as a sign for a coming peak. If a large number turn upward, this is thought to signal an impending trough. Although these indicators are not very reliable, they are watched closely and are used to supplement other, more sophisticated forecasting techniques. The simplest kind of forecasting method is a straightforward extrapolation of a trend. To allow for a seasonal variation, such an extrapolation can be multiplied by the seasonal index (divided by 100) for the month to which the forecast applies. This entire procedure is simply a mechanical extrapolation of the time series into the future