Magazine article Government Finance Review

An Introduction to Dirty Forecasting

Magazine article Government Finance Review

An Introduction to Dirty Forecasting

Article excerpt

Public officials can take a number of approaches to forecasting, usually relying on a mix of formalized models, established econometric techniques, and forecaster judgment. We know our forecasts will be wrong, since the conditions surrounding the future are unknown; the goal of a successful forecaster is not to develop a perfect estimate but rather to develop an estimate with as little error as possible. A key cause of estimate error is the reactionary nature of standard forecasting procedures - a forecast is unlikely to be adjusted unless there is a noticeable change in the environment, by which point it is often too late. Techniques and measures that are predictive in nature can improve accuracy, alerting public officials to a change in the environment that is likely to influence current and future financial positions. Including non-traditional measures that reflect the behavior of residents in what is called a "dirty forecast" is a technique that has been used within economic forecasting, but it is noticeably absent in the public arena. The intent of this article is to introduce readers to the notion of dirty forecasts and explain how they can be used by local governments.

HOW IT WORKS

The phrase "dirty forecast" refers to any forecast that includes non-traditional, coincident indicators, which tell us about the behavior within an environment in the here and now, rather than measuring the environment itself. By focusing on behavior, dirty forecasts are able to pick up changes in the environment well before the changes become measureable outcomes, allowing for easier adjustments when circumstances change. The theoretical link between the indicators and their predicted measure is often murky, but they exhibit a high degree of face validity.

The roots of dirty forecasting can be traced to applied economics. During his tenure as Chairman of the Federal Reserve, Alan Greenspan was known to look at a variety of dirty economic measures to get an idea about the behavior of the market and understand the direction that the economy was heading. The most prominent, and perhaps the most unusual, of these measures is the production of cardboard boxes. Most things we use are placed into a cardboard box at some point in time. Not only are they used for packaging individual items, but they are used in the sale and transport of large quantities of goods. Because the production of an item is related to its demand, Greenspan assumed there was more demand for the products being shipped when the demand for boxes increased. For Greenspan, this signaled a forthcoming boost to the economy.

A change in general production of the market can be observed through traditional measures such as imports, exports, and investment, but traditional measures often take months to calculate and are therefore of little use in the short term. Alternatively, monitoring the production of cardboard boxes gives a real-time look at the market and its direction. For example, see Exhibit 1, which provides the correlations for box production and investment against gross domestic product in the United States from 1977 to 1997. Nearly 99 percent of all changes in box production correlated with a change in the economy. The correlation with investment was 97.3 percent. This might seem like a small difference, but keep in mind that GPD investment measures are only calculated once a quarterly, and are made public some time later.

A variety of other dirty measures exist within economics, including a lipstick indicator and a skirt length indicator, both of which portray the consumer confidence in the market. The lipstick indicator suggests that people indulge in less-expensive luxury items when they are nervous about their future. Increases in lipstick sales suggest a lack of certainty about the economy and employment in the near future. The skirt length indicator monitors the average length of hemlines in the new fashion lines; the shorter the hemline, the more confidence in the direction of the economy. …

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