The applied econometrics course includes both theoretical and applied econometrics. The theoretical part mainly deals with theoretical concepts like demand, supply, income, price level and equilibrium; these are analyzed through regression models. After this, we have to deal with the real-world applications of econometrics. These applications require empirical study of selected firms along with data and analysis. The regression models used in the studies have been built upon various prior assumptions about the economic variables. The assumptions may be growth, inflation or profitability.
The regression analysis method is essential in econometrics. It helps us understand the relationship between the explanatory variable and the known economic variable. In this way we can come to a better understanding of the economy. Generally, the regression models use lags as their fundamental tool for removing non-significance terms from our equations. For example, the lags in the estimation of elasticity of prices should be taken into account so as to remove the potential influences of variations in prices. We should remember that these lags will basically depend upon the nature of the products we are trying to estimate.
Basically econometrics has four main techniques or statistical techniques for statistical analysis. First, regression analysis uses a set of regression models to estimate the relationships among the variables measured. The estimation will then help in forecasting the variation of prices with time and hence will forecast the variations of production and employment. Secondly, the data collection, handling, analysis and dissemination of the collected data are done through several techniques. Techniques such as sampling, statistics, survey, panels, meta-analysis, data synthesis and national accounts are some of these techniques.
Thirdly, the regression analysis looks into the determinants of demand and supply. Now demand determines production, availability and profitability of the firm. The firm may not be able to produce enough capital goods at home, if it is based in a rural area and the capital goods are also produced in urban locations. Hence it may be necessary to alter the location of the factory either to increase the productivity, reduce the costs or increase the flexibility of production.
Fourthly, there are various types of regression models such as the non-linear, logistic, cubic, binomial and the KWERTY. Non-linear regression models assume that prices vary according to a single parameter. Logistic regression models assume that prices vary according to the quantity of foreign exchange traded. Binomial regression models assume that firms can adjust the quantity of foreign currency with respect to each other. Finally, the KWERTY regression models assume that prices, quantities and economic policies are inter-dependent.
There are numerous problems associated with the use of regression in econometrics such as the selection of appropriate units of analysis. In addition, there are also problems associated with the aggregation of regression results. Another important problem associated with this task is overfitting or underfitting the dependent variable to the dependent data. The problem with the selection of appropriate units is that it may lead to a strong association between the variables, but that is not really what we want to see.
As in all areas of economics, the study of demand, supply, price, income, investment and employment is complex. Econometrics offers a complex model that enables researchers to construct descriptive and predictive analyses of the firm-firm and industry-specific situation. Economic theory and practice are very complex and much research has been done and continues to be done on this topic. As with all economic problems, finding answers to these questions will only be accomplished through the use of sophisticated analysis and the collaboration of many econometrics specialists.
