Application of Mathematical Methods in Economic Analysis

When mathematical methods and statistical techniques are applied to the analysis of economic problems and issues, the term usually applied to the process of applied economics is econometrics (Ekelund & HTbert, 1991, p. 372). The goal of econometrics is the development of valid, reliable, and value free predictive models of economic phenomena.

Alan Greenspan (1991, p. 52), Chairman of the Federal Reserve Board, stressed the importance of econometric models as a means of providing a clear understanding of economic events. He argued that the greatest advantage of a "fully articulated model is that it helps the forecaster keep track of the interrelationships among the primary variables of interest" (Greenspan, 1991, p. 53). Within the context of this argument, he referred to "simple accounting identity, such as the one that links government budget deficits, the current account balance, and the excess of domestic saving over investment," and "behavioral interdependencies," which "usually are subject to substantial uncertainty and, as a result, tend to be the focus of greater controversy. Taken together, identities and behavioral equations can aid the forecaster in tracing out a sequence of complicated interactions" (Greenspan, 1991, p. 53).

Another advantage of the econometric approach identified by Greenspan (1991, p. 53) was that it "permits the forecaster to assess systematically the historical accuracy of economi

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can be solved adequately with single equation econometric models. The first step in developing an econometric model is to express the hypothesized economic relationship in the form of a linear equation. Once the economic relationship has been expressed in equation form, the next step in econometric modeling is to estimate the parameters of the system, or values of the variables. The most frequently used technique for parameter estimation is the application of the method of least squares regression analysis with either historical or cross-sectional data (Ravetz, 1994, p. 172). Once the parameters, or coefficients, of the model have been obtained, predicting with a single equation model consists of obtaining the values for the independent variables in the equation and then evaluating the equation for those values. This means that an econometric model that is to be used for predictive purposes must contain independent or explanatory variables whose values for the prediction period can be readily obtained. Some issues confronting the economic analyst cannot be solved through the use of a single equation econometric model. In such instances, the interrelationships involved are so complex that they require the use of multiple equ
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Some common words found in the essay are:
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Approximate Word count = 1463
Approximate Pages = 6 (250 words per page)

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