Economic Forecasting: The Art and
Many do not understand why disagreement among forecasting economists is common and why this disagreement can produce divergent economic forecasts. These concerns reflect too little appreciation of the difficulty of economic forecasting. In the real world, "all else held equal" doesn't hold very often, if ever. To forecast GDP, for example, one must be able to accurately predict the future pattern of government spending, tax and monetary policy, consumer and business spending, dollar strength against foreign currencies, weather, and so on. Although typical patterns can be inferred on the basis of past trends, an unexpected drought, winter storm, or labor strike can disrupt economic activity and upset the accuracy of economic forecasts.
In light of the uncertainties involved, it seems reasonable that different forecasting economists would accord differing importance to a wide variety of economic influences. Forecasters' judgment is reflected not only in the interpretation they give to the data generated by complex computer models but also in the models themselves. Computers may generate economic forecasts, but they do so on the basis of programs written by economists. Computer-generated economic forecasts are only as sophisticated as the data employed, model analyzed, and the subsequent analysis.
Given the criticism often aimed at forecasters, it is ironic to note that the success of economic forecasting is responsible, at least in part, for some of its failures. Users have come to expect a nearly unattainable level of forecast accuracy. At the same time, users forget that forecasts can, by themselves, have important economic consequences. When consumers and businesses cut back on spending in reaction to the forecast of an impending mild recession, for example, they change the basis for the forecasters' initial prediction. By their behavior, they may also cause a steeper recession. This is the forecaster's dilemma: The future as we know it doesn't exist. In fact, it can't.
See: Erin Schulte, "Double Dip: Chip Faux Pas or a Real Economic Hazard," The Wall Street Journal Online, March 2, 2002 (http://online.wsj.com).
One of the most vexing data quality problems encountered in forecasting is the obstacle presented by government-supplied data that are often tardy and inaccurate. For example, the Commerce Department's Bureau of Economic Analysis "advanced" estimate of GDP for the fourth quarter of the year is typically published in late January of the following year. A "preliminary" revision to this estimate is then released by the Bureau of Economic Analysis on March 1; an official final revision is not made available until March 31, or until 90 days after the fact. Such delays induce uncertainty for those seeking to make projections about future trends in economic activity. Worse still, preliminary and final revisions to official GDP estimates are often large and unpredictable. Extreme variation in official estimates of key economic statistics is a primary cause of forecast error among business economists.
Finally, it is worth remembering that forecasts are, by definition, never perfect. All forecasting methods rely heavily on historical data and historical relationships. Future events are seldom, if ever, explicitly accounted for in popular forecasting techniques. Managers must combine traditional forecast methods with personal insight and knowledge of future events to create the most useful forecasts.
Some forecasting techniques are basically quantitative; others are largely qualitative. The most commonly applied forecasting techniques can be divided into the following broad categories:
• Qualitative analyses
• Trend analysis and projection
• Exponential smoothing
• Econometric methods
The best forecast methodology for a particular task depends on the nature of the forecasting problem. When making a choice among forecast methodologies, a number of important factors must be considered. It is always worth considering the distance into the future that one must forecast, the lead time available for making decisions, the level of accuracy required, the quality of data available for analysis, the stochastic or deterministic nature of forecast relations, and the cost and benefits associated with the forecasting problem.
Trend analysis, market experiments, consumer surveys, and the leading indicator approach to forecasting are well suited for short-term projections. Forecasting with complex econometric models and systems of simultaneous equations have proven somewhat more useful for longrun forecasting. Typically, the greater the level of sophistication, the higher the cost. If the required level of accuracy is low, less sophisticated methods can provide adequate results at minimal cost.
An intuitive judgmental approach to forecasting based on opinion
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