Macroeconomic_Analysis

I choose to discuss the change in Gross Domestic Product (GDP) from years 1970-2000. GDP is a very important tool in measuring the economic condition of a country. When GDP is increasing from year to year, it would be safe to assume income, labor opportunities, and overall business would increase as well. I decided to discuss the change of GDP opposed to total GDP, for an understanding of the changes in the select macroeconomic variables that significantly affect GDP. The purpose of this study is to attempt to determine what factors affect the change in GDP, and what arrangements can be made to result in more accurate data. This can be helpful to any nation’s economy. If accurate results are found to determine what affects the change in GDP, then economic policies can be pursued and implemented to aid in the steady increase of a countries GDP. The change in GDP is not subject to any certain variables, for many variables affect the change in GDP. I chose three variables that I believe will have a considerable impact on the change in GDP: change in consumption, change in investment, and change in unemployment rate. It is apparent that consumption and investment are directly related in finding the total GDP. I believe the change in consumption to have a consistent positive relationship with the change in GDP; the more consumers spend the more likely GDP will increase. I believe change in investment will also provide a positive relation to change in GDP. A strong investment should provide for greater capital, which will increase productivity. The third variable, change in unemployment rate, I believe to have a negative relationship to the change in GDP. I suspect that a decrease in the unemployment rate from year to year will correspond to an increase in the change of GDP. The data for the three independent variables and dependent variable (GDP) is shown in Appendix A. This data is time series data from 1970-2000. Initial Results: These are the results of the Multiple Regression of the three dependent variables to GDP, resulting from data in Appendix B. From the results above we notice a very strong correlation. The R^2 is significantly high with .9582 of GDP being explained by this equation. At a 5% alpha level, the P-value of .0001 shows that the correlation is significant and that the equation explains the change of GDP to a great extent. For the regression coefficients all variables are significant. The T-score for consumption is quite high, which makes it safe to assume that a 1 million dollar increase in Consumption will result in a 807,287 dollar increase in GDP, (1,000,000(.807287)). The T-score for Investment is also high enough to be a significant variable. A 1 million dollar increase in Investment will result in a 585,340 dollar increase in the rate of GDP, (1,000,000(.585340)). The T-score for Unemployment rate is also found to consider the variable significant to GDP. Assumed as before the relationship is negative, for every 1 % increase in unemployment rate will result in a loss of 401,595.02 dollars in rate of GDP (.01(40.1595)*(1000000). The Durbin Watson for this regression analysis is 2.019, which gives the notion of no autocorrelation. Looking at Appendix C, at the cross correlation matrix, the values are insignificant and small representing no multi-collinearity suggesting that the independent variables have no relation to each other. Appendices D-F represents a linear line model. Refinements: I found that the refinement step lead to less accurate data then my first regression run. Referring to Appendix H the T-score for CC (C/GDP) provides for an insignificant relationship. There has not been any effect found for CC on GDP. The R^2, Ra^2, and the DW values are very close to what was found in the initial results. This is The DW for this refinement test still falls under the 1.7-2.3 range. This means that there is still no autocorrelation with these variables. In looking at Appendices J-L we can see that the graphs are represented as a straight line relationship opposed to a curved relationship. This also represents a lack of autocorrelation. There seems to be multicollinearity present in the refinement data as well. Let’s take a look at Appendix I and look at the cross sectional matrix diagram. It is noticeable that there is mulitcollinearity between change in Consumption (C) and change in Consumption/change in GDP (CC). This can be viewed as trivial when considering mulicollinearity between the variables because change in Consumption is the numerator in the variable CC. Conclusion: Given the results of my study, I have found that there are significant relationships found between the change in GDP and independent variables: change in Consumption, change in Investment and change in Unemployment rate. The initial results represent a solid linear equation explaining a great amount of the information. Both change in Consumption and change in Investment are positive relationships with the change in GDP; this research proves the importance of a strong role in both Consumption and Investment. The change in unemployment rate was found to be significant, so after doing this study, I can safely assume there is a negative relationship between rate of GDP and change in Unemployment rate. This study helps understand the significance in minimizing the Unemployment rate to help increase the growth of GDP. Though the three independent variables I chose explain 95.82% of the change in GDP, there is much more work that can be done to help explain other factors that affect the change in GDP. Work Cited United States. Bureau of Economic Analysis. , 2005. Web. 28 Oct 2010. .