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Structural Change and Economic Dynamics
But is all model error really unpredictable? Suppose the forecaster reads reports that indicate unusually favorable consumer reaction to the latest styles in clothing. The answer depends on the purpose of the forecast. If the purpose is the purely scientific one of determining how accurately a well-constructed model can forecast, the answer must be: Ignore the outside information and leave the model alone.
These days, many researchers regard such behavior as inevitable in the social science of economic forecasting and have begun to study how best—from a scientific perspective—to incorporate such outside information. Much of the motivation behind trying to specify the most accurately descriptive economic model, trying to determine parameter values that most closely represent economic behavior, and combining these with the best available outside information arises from the desire to produce accurate forecasts.
One user of the forecast may care mostly about the gross domestic product GDP , another mostly about exports and imports, and another mostly about inflation and interest rates. Thus, the same forecast may provide very useful information to some users while being misleading to others.
Econometric Modeler App for Time Series Modeling
For want of anything obviously superior, the most common gauge of the quality of a macroeconomic forecast is how accurately it predicts real GDP growth. Real GDP is the most inclusive summary measure of all the finished goods and services being produced within the geographic boundaries of the nation. For many purposes, there is much value in knowing with some lead time whether to expect real GDP to be increasing at a rapid rate a booming economy with a growth rate above 4 percent , to be slowing down or speeding up relative to recent behavior, or to be slumping a weak economy with a growth rate below 1 percent or even a recessionary economy with a negative growth rate.
The information contained in Figure 2 can be used to judge, in the summary fashion just indicated, the econometric forecasting accuracy achieved by the Research Seminar in Quantitative Economics RSQE of the University of Michigan over the past three-plus decades. There are several ways to characterize the quality of the RSQE forecasting record.
Although the forecasts missed the actual percentage change by an average of only 1. On the other hand, six years had forecast errors of 2 percentage points or more, and for and , the forecast errors were 3. The discussion, so far, has focused on what is referred to as a structural econometric model. That is, the econometrician uses a blend of economic theory, mathematics, and information about the structure of the economy to construct a quantitative economic model.
The econometrician then turns to the observed data—the facts—to estimate the unknown parameter values and turn the economic model into a structural econometric model.
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The idea, for example, that spending on clothing and shoes is determined by household income comes from the core of economic theory. This situation makes it far more difficult than implied to this point to specify the economic model one must begin with to wind up with a structural econometric model for use in forecasting. In recent years, econometricians have found that it is possible to do economic forecasting using a simpler, nonstructural, procedure without losing much forecast accuracy.
Although the simpler procedure has significant costs, these costs do not show up in the normal course of forecasting. The idea of time-series forecasting is easily explained with the aid of Figure 3 , which shows year-by-year changes in spending on clothing and shoes starting in and.
The year-to-year changes, in other words, appear to be stable.
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And that, for this simple case, is the essence of time-series forecasting. Look carefully at the historical behavior of the variable of interest, and if that behavior is characterized by some kind of stability, come up with a quantitative description of that stability and use it to construct the forecast.
In general, the time-series procedure and the structural model procedure seem to produce comparably good, or bad, forecasts for a year or two into the future. But the time-series procedure has the distinct advantage of being far simpler. We can forecast spending on clothing and shoes without having to worry about the theoretical relationship between spending and household income.
It need not be specified and its parameters need not be estimated; just focus on the clothing variable itself. So, where are the significant costs in using the time-series forecasting procedure? They come from the fact that the procedure gives a numerical answer and nothing else. Submit Your Paper. Supports Open Access. View Articles.
Structural Econometric Models
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