This is the season of economic forecasts, for which there are many uses beyond their pure entertainment value.
Forecasts most often are used to assemble budget estimates and to aid in changing public policy.
While economic forecasts are almost always wrong in the strictest sense, they still can be useful, if only to give everyone a point at which to begin their arguments. I also think forecasts provide some interesting insights into the way economic research is conducted and how it is used to influence policy.
Almost all forecasts are constructed using historical data from a variety of sources like the Bureau of Labor Statistics or the Institute for Supply Management. This data is then tortured to reveal relationships that can be used to make predictions.
The mathematical waterboarding of the data can be done in two ways. The oldest of these involves carefully constructed equations that relate one variable to another. An example might be that consumers will buy more cars as population and income increases, fuel prices drop and borrowing rates decline. How much each factor will affect car sales is estimated using historical data and statistical tools.
The second method involves allowing historical data to explain all these relationships. This requires a lot of statistical training, and enough economic theory to figure out what variables must be included.
The forecasting model my center employs (the Indiana Econometric Model) uses both approaches to predict economic performance at the county, regional and state level.
Given the vitriol over economic policy debates, it would seem that different economic ideology drives these forecasts. It does not. A Marxist or libertarian economist would not really disagree on the criterion for selecting the best model: its past performance.
The real difficulty in economic forecasting is the fact that human behavior changes. As a result, constancy is elusive. Luckily, we are fairly good at predicting these changes, too. It is data that gives us our biggest headaches.
For some economic variables, we have monthly data; for others, we have only decennial census data. Also, the government collects data that is convenient, not data that is theoretically appropriate. So we are never quite certain about things like the size of the 7.4 million U.S. businesses or the technology skills of 170 million workers.
But the deeper problem is the frequency of economic phenomena we might wish to study. There have been fewer recessions since 1950 than there are hurricanes in a typical year, and far fewer data elements.
As a consequence, economic forecasters are bad at predicting turning points in recessions and recoveries. These are fortunately infrequent, but a well-thought-out economic forecast offers a great deal of useful information.•
Hicks is director of the Center for Business and Economic Research at Ball State University. His column appears weekly. He can be reached at firstname.lastname@example.org.