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 Forecasting Guide Overview Categories Selecting the Approach Combining Approaches Judgmental Models Delphi Method Curve Fitting Analogous Data Time Series Models Moving Average Exponential Smoothing Decomposition Models Box-Jenkins Models Regression Leading Indicator Input-Output Models

TECHNIQUE #10: Leading Indicator Analysis

BASIC IDEA : Leading indicators are industrial and economic statistics from which an indication of the value or direction of another variable (for example, a sales forecast) might be obtained. They are called "leading" because their direction or magnitude historically "leads" the focal variable. For example, we may find that money supply indicates (leads) the future level of consumer spending. This technique is most useful for identifying the turning points or cyclic nature of a variable.

PROCEDURE: Start with common sense in searching out likely leading indicators. You might start by considering such leading indicators as GNP, interest rates, capital spending, inflation, and/or the unemployment rate - looking for those that appear to have a meaningful "leading" relationship with the variable (e.g. sales) you are forecasting. For example, a very good leading indicator of computer sales might be corporate profits whereas the unemployment rate may not. In addition to data sources available within many firms which may serve as useful leading indicators, there are several published sources of statistics that are frequently used by forecasters and market analysts, including:

1. Economic indicators (council of economic advisers)

2. Federal reserve bulletin

3. Handbook of basic economic statistics

4. Survey of current business

5. Survey of industrial purchasing power

6. Predicasts forecasts and Predicasts basebook

7. Economic Report of the President

Having identified a likely leading indicator, then you must experiment to find the degree of lead-lag (for example, how many months or years) in the indicator-sales relationship. A simple way to identify whether a relationship exists and the degree of lead-lag is to graph the values of both variables over a number of time periods and then line up their peaks and troughs. The amount of adjustment need to match the profiles depicted on the graph by both sets of data indicates the length of time the leading indicator "leads" the variable of interest.

Many times, a number of indicators have some association with the variable to be forecast. you can give them equal or individual weightings and then a consensus of them is used to form the forecast. This is known as "barometric forecasting".

EXAMPLE:

COMMENTS:

• Leading indicators are manytimes difficult to apply in practice because the lead-lag relationship tends to be quite volatile.

• Turning point may be difficult to identify quickly; for example, does a decrease in a leading indicator represent a turning point or just a temporary fall?

• Also, regresssion analysis can be used to identify the nature of the relationship. Simply try running a series of regresssions of the variable of interest (i.e. sales) on the indicator variable at varying time periods. The appropriate lead-lag period is the one for which the regression produces the highest value of R2 (a statistic provided in the output of the regression that indicates the strength of the relationship between the independent and dependent variables).

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