Econometrics: Methods and Applications
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Forecasting and Econometric Models
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Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how to translate data into models to make forecasts and to support decision making. When you know econometrics, you are able to translate data into models to make forecasts and to support decision making in a wide variety of fields, ranging from macroeconomics to finance and marketing. If all such logical connections have been made, the model is complete and can, in principle, be used to forecast the economy or to test theories about its behavior.
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Actually, no econometric model is ever truly complete. For example, a realistic model must include personal income taxes collected by the government because taxes are the wedge between the gross income earned by households and the net income what economists call disposable income available for households to spend. The taxes collected depend on the tax rates in the income tax laws.
But the tax rates are determined by the government as a part of its fiscal policy and are not explained by the model. That requires an assumption about whether the government will change future income tax rates and, if so, when and by how much. Deviations from the predictions of these equations are called model errors.
Most econometric forecasters believe that economic judgment can and should be used not only to determine values for exogenous variables an obvious requirement , but also to reduce the likely size of model error.
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The economic forecaster must be prepared to be wrong because of unpredictable model error. 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. 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.
Application of Econometric Methods to Various Economic Problems
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. 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.
Use of Prior Information. Preliminary Test and Stein-Rule Estimators. Feasible Generalized Least Squares Estimation. Lagged Dependent Variables and Autocorrelation. Unobservable Variables. Varying Coefficient Models. Distributed Lags. Uncertainty in Model Specification and Selection. Introduction to Simultaneous Equations Models.
Limited Information Estimation. Full Information Estimation. Properties of Dynamic Simultaneous Equations Models. Special Topics in Simultaneous Equations.
Related Econometric methods
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