Managerial decision making is often based on forecasts of future events. This chapter examines several techniques for economic forecasting, including qualitative analysis, trend analysis and projection, econometric models, and input-output methods.
• Qualitative analysis is an intuitive judgmental approach to forecasting that is useful when based on unbiased, informed opinion. The personal insight method is one in which an informed individual uses personal or organizational experience as a basis for developing future expectations. The panel consensus method relies on the informed opinion of several individuals. In the delphi method, responses from a panel of experts are analyzed by an independent party to elicit a consensus opinion.
• Survey techniques that skillfully use interviews or mailed questionnaires constitute another important forecasting tool, especially for short-term projections.
• Trend analysis involves characterizing the historical pattern of an economic variable and then projecting or forecasting its future path based on past experience. A secular trend is the long-run pattern of increase or decrease in economic data. Cyclical fluctuation describes the rhythmic variation in economic series that is due to a pattern of expansion or contraction in the overall economy. Seasonal variation, or seasonality, is a rhythmic annual pattern in sales or profits caused by weather, habit, or social custom. Irregular or random influences are unpredictable shocks to the economic system and the pace of economic activity caused by wars, strikes, natural catastrophes, and so on.
• A simple linear trend analysis assumes a constant period-by-period unit change in an important economic variable over time. Growth trend analysis assumes a constant period-by-period percentage change in an important economic variable over time.
• Macroeconomic forecasting involves predicting the pace of economic activity, employment, or interest rates at the international, national, or regional level. Microeconomic forecasting involves predicting economic performance, say, profitability, at the industry, firm, or plant level.
• The business cycle is the rhythmic pattern of contraction and expansion observed in the overall economy. Economic indicators are series of data that successfully describe the pattern of projected, current, or past economic activity. A composite index is a weighted average of leading, coincident, or lagging economic indicators. An economic recession is a significant decline in activity spread across the economy that lasts more than a few months. Recessions are visible in terms of falling industrial production, declining real income, shrinking wholesale-retail, and rising unemployment. An economic expansion exhibits rising economic activity.
• Exponential smoothing (or "averaging") techniques are among the most widely used forecasting methods. In two-parameter (Holt) exponential smoothing, the data are assumed to consist of fluctuations about a level that is changing with some constant or slowly drifting linear trend. The three-parameter (Winters) exponential smoothing method extends the two-parameter technique by including a smoothed multiplicative seasonal index to account for the seasonal behavior of the forecast series.
• Econometric methods use economic theory and mathematical and statistical tools to forecast economic relations. Identities are economic relations that are true by definition. Behavioral equations are hypothesized economic relations that are estimated by using econometric methods.
• Forecast reliability, or predictive consistency, must be accurately judged in order to assess the degree of confidence that should be placed in economic forecasts. A given forecast model is often estimated by using a test group of data and evaluated by using forecast group data. No forecasting assignment is complete until reliability has been quantified and evaluated. The sample mean forecast error is one useful measure of predictive capability.
The appropriate technique to apply in a given forecasting situation depends on such factors as the distance into the future being forecast, the lead time available, the accuracy required, the quality of data available for analysis, and the nature of the economic relations involved in the forecasting problem.
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