Economic Data Analysis Using Software STATA - Coursework Example

The maiden aim of this study was to determine whether the GDP, the rate of inflation, the rate of un-employment, long term interest rate andshort term interest rate, the CPI-consumer price index, the money supply (M) can be expressed in terms of regression lines, as below, Y=a+bx+e………………………………………..a for simple regression and Y=a+bx1+cx2+dx3+…+zx26+e…………………...b for a multiple regression This was occasioned by the facts that, the GDP of a country most of the times tends to follow a certain criteria depending on the rate of inflation and the rate of un-employment among the country’s population. In this regard, data was collected for 19 years (1980-1998), for all the variables; GDP, the rate of inflation, the rate of un-employment, long term interest rate, short term interest rate, the CPI-consumer price index an, the money supply (M) and regression analysis done using STATA. Introduction When investing, you are most likely to hear the terms inflation, unemployment, Consumer Price Index (CPI) and Gross Domestic Product (GDP) about every day (Barnes, R 2007). Investors are often made to feel that these terms should be looked deeply as a surgeon would study a patients chart prior to operating (Barnes, R 2007). Countries really need to find a level of understanding which can assists them in decision making without engaging piles of data to solve the problem. Inflation Inflation in the increase in the money supply. It can also mean an increase in price levels for different commodities. Generally, inflation is about a prices increase as compared to some set levels (Richmond, H 2006). According to Gay, A (2006), if the money supply goes up, this is usually manifested in higher price levels for commodities, however, he continues to state that, this manifestation takes only a short while and that, it is simply a matter of time. GDP According to Dawson, G. et al. (2006), Gross Domestic Product in a country represents the total aggregate output of its economy. Further to him, the GDP figures which are reported to investors and those that want to invest are already adjusted for inflation. For example, if the gross GDP is calculated to be 6% above than the preceding year and inflation calculated at 2% over the same period, then the GDP growth would be reported as 4% (6%-2). The relationship between inflation and GDP is like a delicate dance where any can break affecting the masses (Dobson, S & Palfreman, S 1999). The annual GDP growth is vital for each and every country in that, if the overall economic output declines or holds steadily, then many organizations will not be in a position to increase profits which is the sole driver of stock performance in all of them. On the other hand, too much GDP growth is dangerous since in many cases it comes with an increase in prices; inflation. This erodes the stock market gains by making the available money less valuable (Doyle, E 2005). To him further, many economists agree that 2-3% GDP growth per year in any country is the most agreeable which can safely be maintained. According to experts, a GDP growth of over 2% causes a 0.5% drop in unemployment for every percentage point over 2%. However, this positive relationship does not hold when un-employment gets very low (Doyle, E 2005. To him, extremely low unemployment rates are more costly than valuable. Results Descriptive statistics Variable Mean Std. Dev. Min Max GDP 5512.568 1824.14 2795.6 8759.9 pt 4.373684 2.899012 1 11 ut 5.478947 1.366495 3.5 8.5 CPI 125.2158 25.14205 82.4 163 LTRATE 8.936842 2.343253 5.58 13.45 TBRATE 6.980053 2.885037 3.02 14.029 1. Regression ANOVA table of pt and ut: Independent-ut, Dependent-pt Source SS df MS Number of obs. = 19 F (1, 17) = 1.99 Model 15.8750604 1 15.8750604 Prob > F = 0.1761 Residual 135.401784 17 7.96481083 R-squared = 0.1049 Adj R-squared = 0.0523 Total 151.276845 18 8.40426914 Root MSE = 2.8222 From the above results, it is evident that the rate of un-employment has got nothing to do with the rate of inflation. Further, it is evident that, the two have a weak positive relationship with each other. Coefficients table Coef. Std. Err. t P>|t| [95% Conf. Interval] ut .6872476 .4867918 1.41 0.176 -.3397934 1.714289 Cons .6082907 2.744569 0.22 0.827 -5.182244 6.398825 Further from above, both the coefficient and the constant are not significant; they cannot be used to predict the rate of inflation and the regression equation can be written as follows, Pt = .608 + .687Ut + .487……………………………………..1 The rate of unemployment has a factor of .687 toward the inflation rate which is not significant at all. 2. Regression ANOVA table of GDP and pt: Independent-pt, Dependent-GDP Source SS df MS Number of obs = 19 F (1, 17) = 42.20 Model 42695004.3 1 42695004.3 Prob > F = 0.0000 Residual 17199727.4 17 1011748.67 R-squared = 0.7128 Adj R-squared = 0.6959 Total 59894731.6 18 3327485.09 Root MSE = 1005.9 The GDP and the inflation rate are significantly related to one another. This translates to mean that, the rate of inflation can be used to predict the GDP at 95% level of significance. Coefficient table Coef. Std. Err. t P>|t| [95% Conf. Interval] pt 531.2541 81.78056 6.50 0.000 358.7122 703.796 Cons 3189.031 425.6602 7.49 0.000 2290.966 4087.095 The coefficients table above confirms that, the two have a significant relationship. Both the constant and the inflation rate coefficient are significant. The regression equation can be written as follows, GDP = 3189.031 + 531.254 Pt+ 81.78………………………………2 This means that, the rate of inflation has a contribution factor of 531.254 towards the GDP 3. Regression ANOVA table of GDP and ut: Independent-ut, Dependent-GDP Source SS df MS Number of obs = 19 F (1, 17) = 3.79 Model 10913869.3 1 10913869.3 Prob > F = 0.0683 Residual 48980862.3 17 2881227.19 R-squared = 0.1822 Adj R-squared = 0.1341 Total 59894731.6 18 3327485.09 Root MSE = 1697.4 GDP and the rate of un-employment have no significant relationship. This is so because, a positive change in the GDP has a very small effect on the rate of un-employment. Coefficients table Coef. Std. Err. t P>|t| [95% Conf. Interval] ut 569.8295 292.782 1.95 0.068 -47.88642 1187.545 Cons 2390.503 1650.727 1.45 0.166 -1092.226 5873.232 It is clearly evident that, the coefficient of the rate of un-employment is not significant at 95% Confidence level. The same applies to the constant coefficient. The regression equation can be formulated as below, GDP = 2390.503 + 569.83 Ut + 192.78…………………………………3 The rate of unemployment has a factor of 569.83 which is not significant towards the GDP. 4. Regression ANOVA table of CPI and pt: Independent-pt, Dependent-CPI Source SS df MS Number of obs =19 F (1, 17) = 41.72 Model 8083.99701 1 8083.99701 Prob > F = 0.0000 Residual 3294.20726 17 193.776898 R-squared = 0.7105 Adj R-squared = 0.6935 Total 11378.2043 18 632.122459 Root MSE = 13.92 The inflation rate and the CPI are both significantly related with one another at 95% CI. In addition, they both have a positive relationship. Coefficients table Coef. Std. Err. t P>|t| [95% Conf. Interval] pt 7.310159 1.131787 6.46 0.000 4.922297 9.698022 Cons 93.24346 5.890847 15.83 0.000 80.81486 105.6721 The coefficients for both the inflation rate and the constant are significant and have something to contribute towards the CPI. Below is the regression equation, CPI = 93.24 + 7.31 Pt + 1.13…………………………………………4 This confirms that, the inflation rate has a factor of 7.31 and the factor is significant. 5. Regression ANOVA table of GDP and LTRATE: Independent-LTRATE, Dependent-GDP Source SS df MS Number of obs = 19 F (1, 17) = 75.30 Model 48862709.3 1 48862709.3 Prob > F = 0.0000 Residual 11032022.3 17 648942.489 R-squared = 0.8158 Adj R-squared = 0.8050 Total 59894731.6 18 3327485.09 Root MSE = 805.57 The table above shows that, GDP and long term treasury bond rate are significantly related with each other with p|t| [95% Conf. Interval] LTRATE -703.1263 81.03035 -8.68 0.000 -874.0854 -532.1672 _cons 11796.3 747.366 15.78 0.000 10219.49 13373.1 The coefficients for both the LTRATE and the constant are significant at 95% CI. The regression line is as below, GDP = 11796.3 -703.13 LTRATE + 81.03………………………………5 6. Regression ANOVA table of GDP and TBRATE: Independent-TBRATE, Dependent-GDP Source SS df MS Number of obs = 19 F (1, 17) = 28.80 Model 37664422.7 1 37664422.7 Prob > F = 0.0001 Residual 22230309 17 1307665.23 R-squared = 0.6288 Adj R-squared = 0.6070 Total 59894731.6 18 3327485.09 Root MSE = 1143.5 The GDP and short term Treasury bond rate are significantly related with each other at 95% CI. In addition, they have as strong linear relationship with each other. Coefficients table Coef. Std. Err. t P>|t| [95% Conf. Interval] TBRATE -501.3927 93.42447 -5.37 0.000 -698.5011 -304.2843 Cons 9012.316 702.9004 12.82 0.000 7529.326 10495.31 The regression line which can show how they are related can be formulated as below, GDP = 9012.32 – 501.4 TBRATE + 93.42………………………………..6 7. Regression ANOVA table of GDP pt ut CPI TBRATE LTRATE: Independent- pt, ut, CPI, TBRATE, LTRATE, Dependent-GDP Source SS df MS Number of obs = 19 F (5, 13) = 391.34 Model 59499429.7 5 11899885.9 Prob > F = 0.0000 Residual 395301.944 13 30407.8419 R-squared = 0.9934 Adj R-squared = 0.9909 Total 59894731.6 18 3327485.09 Root MSE = 174.38 From the above table, it is evident that the GDP is dependent of the other variables; pt ut CPI TBRATE LTRATE at 95% CI. Also, all have a strong positive relationship of .9. Coefficients table Coef. Std. Err. t P>|t| [95% Conf. Interval] pt -19.91544 28.15281 -0.71 0.492 -80.73588 40.905 ut -21.74275 43.67579 -0.50 0.627 -116.0986 72.61305 CPI 72.72579 5.749376 12.65 0.000 60.30502 85.14656 TBRATE 128.9379 37.31658 3.46 0.004 48.32033 209.5555 LTRATE -166.6864 56.24926 -2.96 0.011 -288.2055 -45.16727 _cons -2797.96 920.2409 -3.04 0.009 -4786.02 -809.9007 All the coefficients are significant expect those for pt and ut. The multiple regression equation can be written as below. GDP = -2797.96 + 72.73 CPI + 128.94 TBRATE -166.69 LTRATE – 19.92 Pt – 21.74 Ut ………………………………………………….7 Figure 1 Figure 1 above is a scatter diagram of the GDP and the year it was recorded. It is evident that, the GDP has been increasing year by year since 1980. Figure 2 The consumer price index and the inflation rate have a linear relationship. The higher the inflation rate, the higher the Consumer Price Index (CPI). Figure 3 The GDP and the long term Treasury bond rate have a strong relationship although it is an inverse relationship. Low rate contributes much to the GDP in the long run than higher rate. Figure 4 Likewise, the GDP and the short term Treasury bond rate have a relationship which is a negative one. Low rate translates to higher GDP than low rate. A variable ‘ADD’ is generated by adding both pt and ut together; ADD = ut + pt. Regression of GDP and ADD yields the results shown below, ANOVA table Source SS df MS Number of obs =19 F (1, 17) = 42.77 Model 42858653.9 1 42858653.9 Prob > F = 0.0000 Residual 17036077.7 17 1002122.22 R-squared = 0.7156 Adj R-squared = 0.6988 Total 59894731.6 18 3327485.09 Root MSE = 1001.1 The two are significantly related with each other at 95% CI Coefficients table Coef. Std. Err. t P>|t| [95% Conf. Interval] ADD 430.6566 65.85249 6.54 0.000 291.72 569.5932 _cons 1269.467 688.2668 1.84 0.083 -182.6486 2721.583 The regression line is as below, GDP = 1269.66 + 430.66 ADD + 65.85…………………………………8 Discussion From the above analysis, it is evident that the GDP has many factors which affect it significantly; inflation rate, un-employment rate, Consumer Price Index, long term Treasury bond rate and short term Treasury bond rate. GDP increases from year to year and if high, it is a bit dangerous since it can cause a very high inflation rate which in many instances countries are not prepared for. A growth in GDP reduces the amount of un-employment rate in a country since investor will have confidence to invest in such a country translating into more jobs thus less un-employment. The Consumer Price Index is controlled by the rate of inflation. If the inflation is high, the price of the various commodities are severely affected either. Further, the treasury bonds rate are not supposed to take a long while to mature if a country is to attain any significant growth GDP wise; both long term and short term. Conclusion In conclusion, for a country to have any significant growth, controls must be in place to gauge the inflation rate. For example, the internal security should be of high priority is the country is to attract investor who contribute fully towards such growth. Policies should be put in place to ensure that the rate of growth is sustainable over a long period so as to boost the morale of those that invest. Reference: Barnes, R (2007). The Importance of Inflation and GDP. Retrieved 09 January, 2008, from, http://www.investopedia.com/articles/06/gdpinflation.asp Dawson, G. et al. (2006). Economics and Economic Change. London: FT Prentice Hall. ISBN10: 0273693514 Dobson, S. & Palfreman, S. (1999). Introduction to Economics. London: Oxford University Press Doyle, E (2005). The Economic System. London: John Wiley and Sons Ltd. ISBN10: 0470850019 Gay, A (2006). Introduction to Economics and its Applications. Nairobi: General Printers Richmond, H (2006). Economics of Today: How it is shaping the world. Ohio: Akron University Press Appendix regress pt ut Source | SS df MS Number of obs = 19 -------------+------------------------------ F( 1, 17) = 1.99 Model | 15.8750604 1 15.8750604 Prob > F = 0.1761 Residual | 135.401784 17 7.96481083 R-squared = 0.1049 -------------+------------------------------ Adj R-squared = 0.0523 Total | 151.276845 18 8.40426914 Root MSE = 2.8222 ------------------------------------------------------------------------------ pt | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ut | .6872476 .4867918 1.41 0.176 -.3397934 1.714289 _cons | .6082907 2.744569 0.22 0.827 -5.182244 6.398825 ------------------------------------------------------------------------------ . log using "C:\Documents and Settings\skimote\Desktop\All user files\1.smcl" ------------------------------------------------------------------------------- log: C:\Documents and Settings\skimote\Desktop\All user files\1.smcl log type: smcl opened on: 9 Jan 2008, 14:17:29 . regress GDP pt Source | SS df MS Number of obs = 19 -------------+------------------------------ F( 1, 17) = 42.20 Model | 42695004.3 1 42695004.3 Prob > F = 0.0000 Residual | 17199727.4 17 1011748.67 R-squared = 0.7128 -------------+------------------------------ Adj R-squared = 0.6959 Total | 59894731.6 18 3327485.09 Root MSE = 1005.9 ------------------------------------------------------------------------------ GDP | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- pt | 531.2541 81.78056 6.50 0.000 358.7122 703.796 _cons | 3189.031 425.6602 7.49 0.000 2290.966 4087.095 ------------------------------------------------------------------------------ . regress GDP ut Source | SS df MS Number of obs = 19 -------------+------------------------------ F( 1, 17) = 3.79 Model | 10913869.3 1 10913869.3 Prob > F = 0.0683 Residual | 48980862.3 17 2881227.19 R-squared = 0.1822 -------------+------------------------------ Adj R-squared = 0.1341 Total | 59894731.6 18 3327485.09 Root MSE = 1697.4 ------------------------------------------------------------------------------ GDP | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ut | 569.8295 292.782 1.95 0.068 -47.88642 1187.545 _cons | 2390.503 1650.727 1.45 0.166 -1092.226 5873.232 ------------------------------------------------------------------------------ . regress CPT pt variable CPT not found r(111); . regress CPI pt Source | SS df MS Number of obs = 19 -------------+------------------------------ F( 1, 17) = 41.72 Model | 8083.99701 1 8083.99701 Prob > F = 0.0000 Residual | 3294.20726 17 193.776898 R-squared = 0.7105 -------------+------------------------------ Adj R-squared = 0.6935 Total | 11378.2043 18 632.122459 Root MSE = 13.92 ------------------------------------------------------------------------------ CPI | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- pt | 7.310159 1.131787 6.46 0.000 4.922297 9.698022 _cons | 93.24346 5.890847 15.83 0.000 80.81486 105.6721 ------------------------------------------------------------------------------ . regress GDP LTRATE Source | SS df MS Number of obs = 19 -------------+------------------------------ F( 1, 17) = 75.30 Model | 48862709.3 1 48862709.3 Prob > F = 0.0000 Residual | 11032022.3 17 648942.489 R-squared = 0.8158 -------------+------------------------------ Adj R-squared = 0.8050 Total | 59894731.6 18 3327485.09 Root MSE = 805.57 ------------------------------------------------------------------------------ GDP | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- LTRATE | -703.1263 81.03035 -8.68 0.000 -874.0854 -532.1672 _cons | 11796.3 747.366 15.78 0.000 10219.49 13373.1 ------------------------------------------------------------------------------ . regress GDP TBRATE Source | SS df MS Number of obs = 19 -------------+------------------------------ F( 1, 17) = 28.80 Model | 37664422.7 1 37664422.7 Prob > F = 0.0001 Residual | 22230309 17 1307665.23 R-squared = 0.6288 -------------+------------------------------ Adj R-squared = 0.6070 Total | 59894731.6 18 3327485.09 Root MSE = 1143.5 ------------------------------------------------------------------------------ GDP | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- TBRATE | -501.3927 93.42447 -5.37 0.000 -698.5011 -304.2843 _cons | 9012.316 702.9004 12.82 0.000 7529.326 10495.31 ------------------------------------------------------------------------------ . regress GDP pt ut CPI TBRATE LTRATE Source | SS df MS Number of obs = 19 -------------+------------------------------ F( 5, 13) = 391.34 Model | 59499429.7 5 11899885.9 Prob > F = 0.0000 Residual | 395301.944 13 30407.8419 R-squared = 0.9934 -------------+------------------------------ Adj R-squared = 0.9909 Total | 59894731.6 18 3327485.09 Root MSE = 174.38 ------------------------------------------------------------------------------ GDP | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- pt | -19.91544 28.15281 -0.71 0.492 -80.73588 40.905 ut | -21.74275 43.67579 -0.50 0.627 -116.0986 72.61305 CPI | 72.72579 5.749376 12.65 0.000 60.30502 85.14656 TBRATE | 128.9379 37.31658 3.46 0.004 48.32033 209.5555 LTRATE | -166.6864 56.24926 -2.96 0.011 -288.2055 -45.16727 _cons | -2797.96 920.2409 -3.04 0.009 -4786.02 -809.9007 ------------------------------------------------------------------------------ sum GDP pt ut CPI LTRATE TBRATE Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- GDP | 19 5512.568 1824.14 2795.6 8759.9 pt | 19 4.373684 2.899012 1 11 ut | 19 5.478947 1.366495 3.5 8.5 CPI | 19 125.2158 25.14205 82.4 163 LTRATE | 19 8.936842 2.343253 5.58 13.45 -------------+-------------------------------------------------------- TBRATE | 19 6.980053 2.885037 3.02 14.029 . generate ADD = pt+ut . regress GDP ADD Source | SS df MS Number of obs = 19 -------------+------------------------------ F( 1, 17) = 42.77 Model | 42858653.9 1 42858653.9 Prob > F = 0.0000 Residual | 17036077.7 17 1002122.22 R-squared = 0.7156 -------------+------------------------------ Adj R-squared = 0.6988 Total | 59894731.6 18 3327485.09 Root MSE = 1001.1 ------------------------------------------------------------------------------ GDP | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- ADD | 430.6566 65.85249 6.54 0.000 291.72 569.5932 _cons | 1269.467 688.2668 1.84 0.083 -182.6486 2721.583 ------------------------------------------------------------------------------ . Read More