Consumption of Non Durable Goods
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 di
The graph prepared below shows what would be the consequences if the impact of interest rates would cause a five hundred dollar decrease in consumption. These results follow the classical theory that interest rates are related to consumption expenditures. To prepare this analysis it was essential to rely on Keynes' theories about consumption. Nominal interest rates and their effects on consumption of non durable goods will studied in our hypothetical model, since inflation does not factor in the analysis. The p-value was the smallest for iGS3-12, so this regression was used for the equation. 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. Business Statistics: Elements and Applications. In our analysis the multiple regression produced a higher R Square than the simple regression. In the regression plot created below, it is clear that the fit data lies closely around the line, but the forecasted data is overestimated.
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