Econometric tool

The following simple statistical procedure takes care of these objections. Here is a useful way to think about it. The U. Once that minimum is accounted for, additional purchases of clothing and shoes will amount to 2.

The fact that the parameter values and. Using the data to determine or estimate all the parameter values in the model is the critical step that turns the mathematical economic model into an econometric model.

An econometric model is said to be complete if it contains just enough equations to predict values for all of the variables in the model. Thus, there must be an equation somewhere in the model that determines W. 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.

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. 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. 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. Suggested read: 7 Major Branches of Discrete Mathematics. For example, a philosophy of international economics is that prices across open borders go hand in hand after purchasing power parity is allowed. The empirical relationship between domestic prices and foreign prices adjusted for nominal exchange rate scenarios should always be positive, and they should try to maintain parity at all times.

The second step is to specify a statistical model that captures the essence of the theory. The economist tries to propose a unique relationship between the dependent variable and the explanatory variables through the model.

By far the most easy approach is to assume linearity—meaning that any change in an explanatory variable will always induce a similar change in the dependent variable. It is certainly impossible to account for every little influence on the dependent variable, hence a variable is added to the statistical model to nullify the external disturbances. The role here of the new variable is to represent all the determinants of the dependent variable that cannot be accounted for.

Mostly caused by the complexity of the data. Recommended read: Types of statistical analysis. The third step is to estimate the unknown variables of the model using economic data at disposal. It usually involves using an appropriate statistical procedure and an econometric software package to carry out this process. This is termed as the easiest part of the analysis thanks to easy availability of abundant economic data and excellent econometric techniques and software.

The econometrics still rely on the principles of the famous GIGO garbage in, garbage out style of computing. This is the fourth step and also the most important out of all. This step involves asking the right questions to ourselves. For example,. Are the signs and the relationship of the estimated parameters that bridge the dependent variable to the explanatory variables consistent with the predictions of the economic theory? If the estimated parameters do not make sense, how should the statistical model be edited by an econometrician so as to yield appropriate results?

The main tool of the fourth stage is hypothesis testing, a statistical procedure in which the researcher remarks regarding the true value of an economic parameter, and a statistical test is carried out to finds out whether the estimated parameter is synonymous with the particular hypothesis. If it is not, the researcher must either reject the hypothesis or make changes in the statistical model and start all over again. If all four stages proceed successfully, the result is a model that can be used as a tool to assess the empirical validity of an economic model.

Students of econometrics are often fascinated by the ability of linear multiple regression to forecast economic relationships. First , the quality of the parameter findings depends on the current working condition of the economic model.

Second , if a relevant explanatory variable is excluded from the model, it is most likely for parameter estimates to become unreliable and inaccurate. Third , the parameter estimates have a very slim chance of being on similar lines with the actual parameter values that are generated by the statistical data, even if the econometrician identifies the process as the source of the original data.

Eventually the estimates are used because they will become precise as more data is available and estimates are in accordance to the vastness of coverage.

The first function of Econometrics is to test out economic theories or hypotheses laid out by the coveted econometricians. For example, is consumption directly related to income? Is the quantity demanded of a certain commodity inversely related to its price? Toggle Main Navigation. Econometrics Toolbox. Search MathWorks. Close Mobile Search. Econometrics Toolbox Model and analyze financial and economic systems using statistical methods. Get a free trial. View Pricing. Get Started:.

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