The intention of this research project was to build an empirically based model that could be used to prepare a forecast of future consumption. The first step that must be taken is to study the effects of income and interest rates on the consumption of non-durable goods for the years 1990 to 1994. To forecast future consumption, Microsoft EXCEL was used to produce both simple and multiple regressions from the data period 1991 through 1993 data. In the simple regression model, income was used as the independent variable. Interest rates along with income provided the independent variables in the multiple regression analysis. The best resulting regression equations were determined and used to forecast consumption for the year 1994.
Regression analysis is the statistical technique most frequently used in the field of economics to process empirical evidence and to test the explanatory power of theoretical models. In order to forecast future consumption and compare the results to theoretical models, simple and multiple regressions were run. All data needed to run the regression was taken from the Federal Reserve Bank of St. Louis data bank (FREDDATA). Monthly data in chained 1992 dollars is used for both consumption expenditure and disposable income. The monthly data was lagged five to twelve months and analyzed to find the best equation. The lag was needed so that it could not be argued that changes in income cause changes in spending, or that changes in spending cause changes in income. The seasonally adjusted annual rate (SAAR) also played a role in the data transformation process so that no other outside factors, such as the seasons, could be responsible for the regression results.
After the regression analysis was finished, the next step was to determine which; of the economic theories best explained the results of the data analysis. To help explain these theories and results, Miller's Economics Today, Picconi, Romano, and Ol...
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