Economic Indicators of Economic Growth
6151 words (25 pages) Essay
8th Feb 2020 Economics Reference this
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In this report we will be examining three economic indicators real GDP growth, the unemployment rate and the inflation rate from the 1929 to 2017. We will be specifically focusing on the real GDP growth rate and the unemployment rate in the US and how the two variables affect each other. We will also look at future forecasts and measure the accuracy of these forecasts using different methods and calculating the errors.
Cleaning the data
We put all the cleaned data into percentages. This is because we believe that it helps to make the data easier to interpret and is a better match since we are talking about rates of change. The first variable we looked at was the inflation rate this can be described as an increase in the prices of goods, the cost of living and a decrease in the value of money. As the inflation rate increases prices, interest rates for banks increase too and can result in a higher level of unemployment due to businessâ€™s failing. Another variable in which we looked at is the unemployment rate which is those that are able to work but currently do not, expressed as a percentage. A consistently high unemployment rate can cause many issues such as an decrease in disposable income, which would lead to a decrease in consumer spending. The last variable that we looked at was the GDP growth rate (Gross Domestic Product) which is the market value of the entirety of the goods and services produced in a country over a particular time period. A high GDP would suggest that a country is increasing the amount of production and services that it is supplying and citizens should have a higher income. The cleaned data can be seen in Figure 1.
Descriptive Statistics
Figure 2: Descriptive Statistics

GDP 
Inflation 
Unemployment 
Mean 
6.35% 
3.15% 
7.15% 
Median 
6.13% 
2.80% 
5.70% 
Standard Deviation 
6.87% 
4.03% 
4.70% 
Sample Variance 
0.47% 
0.16% 
0.22% 
Kurtosis 
5.9271 
3.9766 
4.4907 
Skewness 
0.7855 
0.2803 
2.1561 
Range 
51.43% 
28.40% 
23.70% 
Minimum 
23.09% 
10.30% 
1.20% 
Maximum 
28.34% 
18.10% 
24.90% 
Count 
88 
88 
88 
Here are some descriptive statistics for all the variables. With the cleaned data we used a full sample to calculate these statistics. The first statistic that we calculated was the mean. The mean is affected by extreme values which does not make it very effective when there are outliers. When the mean is larger than the median the graph will have a positive skew. From this we can identify that all there of the variables GDP, inflation and unemployment will have a positive skew. We then looked at the standard deviation and sample variance. The standard deviation tells you how much the data varies from the mean, helping to tell you how accurate the mean is. The sample variance tells you how varied the data is. Kurtosis is used to describe the distribution of data. All kurtosis values are greater than 3, all the graphs will be leptokurtic. We also calculated the skewness of the data which can tell you how the distribution differs from a normal distribution. The range gives you an idea of the spread of the data but is affected by outliers.
Figure 3: Graph of the percentage change of the inflation, unemployment and GDP rate from 1929 to 2017
From the line graph that we have created here you can see how the inflation, unemployment and GDP growth rate has changed over the years 1929 to 2017. During the first few years all the variables fluctuate a lot, this suggests uncertainty in the economy, from 1929 to 1953. At the start where the GDP growth rate and the inflation rate had a large decrease in percentage change while the unemployment rate was increasing shows that there is a strong negative correlation between the variables GDP and unemployment as well as inflation rate and unemployment. This correlation becomes weaker as the graph progresses. However, after this time period the variables seem to be less susceptible to change and all the variables seem to increase and decrease at a similar level, this could be due to a stronger economy.
GDP Growth Rate and Unemployment Rate
Figure 4: A graph to show the correlation between GDP and unemployment rate
Regression Statistics 

Multiple R 
0.3157 
R Square 
0.0997 
Adjusted R Square 
0.0881 
Standard Error 
0.0683 
Observations 
80 
We then compared the GDP growth rate to the unemployment rate. From this scatter diagram here you can see the negative correlation between the two variables although a weak correlation it is still evident on the graph. We have also made some statistics for the regression line of this graph which show the R squared value of 0.0997 also called the coefficient of determination, which can give you an indication as to how accurate the line of best fit is, as the value is not close to 1, where the regression line fits the data, the regression line is not the best estimate for future predictions as the data is not very close to the regression line. The correlation coefficient of this graph is 0.4613, it is weak negative correlation.
Figure 5: Regression Statistics

Coefficients 
Intercept 
0.0994 
Unemployment X 
0.4613 
By using the least squares model we were able to calculate the equation of the regression line for the data which is y = 0.4613(X) + 0.0994. The slope of this regression line is 0.4613 and the Yintercept is 0.0994. The regression line can be used to help predict future outcomes.
Figure 6: Regression Line
Forecasting Analysis
Date 
Unemployment Rate % 
GDP Growth Rate % 
Forecast GDP 
Error 
Absolute Value of Error 
Squared Error 
20100101 
9.30% 
3.76% 
5.65% 
1.89% 
1.89% 
0.04% 
20110101 
8.50% 
3.67% 
6.02% 
2.35% 
2.35% 
0.06% 
20120101 
7.80% 
4.21% 
6.34% 
2.13% 
2.13% 
0.05% 
20130101 
6.70% 
3.63% 
6.85% 
3.22% 
3.22% 
0.10% 
20140101 
5.60% 
4.39% 
7.36% 
2.97% 
2.97% 
0.09% 
20150101 
5.00% 
3.98% 
7.63% 
3.65% 
3.65% 
0.13% 
20160101 
4.70% 
2.68% 
7.77% 
5.09% 
5.09% 
0.26% 
20170101 
4.02% 
4.16% 
8.09% 
3.93% 
3.93% 
0.15% 
Figure 7: Forecasting
MSE 
Mean Squared Error 
0.11% 
MAD 
Mean Absolute Deviation 
3.15% 
TS 
Tracking Signal 
8 
RMSE 
Root Mean Square Error 
0.0331 
By using the last 8 observations to create an out of sample analysis we worked out an estimated forecast and calculated the accuracy of this forecast. As the coefficient of determination is not very strong the forecast was not likely to be very accurate but can still be a useful way to predict upcoming trends in the economy. In this table it shows you the actual value of the GDP and the forecasted value with another column to show the level of error in the prediction. We also calculated the root mean standard deviation of the residuals, the RMSE, this can give you an idea of how close the data is to the line of best fit. Another measure we used to see how accurate the forecasting was the MSE, mean squared error. The smaller the value for the MAD the more accurate the forecast is. The tracking signal here is above the average which is 4 to 4 suggesting that the data does have some bias.
Figure 8: Forecasting Errors
Conclusion
Overall we think that the forecast predictions for the out of sample data were not very accurate however, we believe that they can still be used to help predict future trends in the economy along with other models. The weak correlation between the two variables made forecasted predictions of the GDP growth rate and unemployment rate inaccurate. However we were still able to identify using the in sample analysis, using all the data given to us that there is a negative correlation between the two variables, as the GDP growth rate increases the unemployment decreases. This correlation also makes sense in economic terms as when a country is producing more goods more jobs should be made available.
References:
 Economicshelp.org. (2018). [online] Available at: https://www.economicshelp.org/macroeconomics/inflation/definition/ [Accessed 25 Nov. 2018].
 Seth, S. (2018). Inflation Definition  Investopedia. [online] Investopedia. Available at: https://www.investopedia.com/terms/i/inflation.asp [Accessed 25 Nov. 2018].
 FocusEconomics  Economic Forecasts from the World’s Leading Economists. (2018). What is the unemployment rate?. [online] Available at: https://www.focuseconomics.com/economicindicator/unemploymentrate [Accessed 25 Nov. 2018].
Appendix:
Figure 1: Cleaned Data
Observation date 
GDP(billions of dollars) 
Inflation Rate % 
Unemployment rate % 
GDP Growth Rate % 
19300101 
92.1600 
6.40% 
8.70% 
11.86% 
19310101 
77.3910 
9.30% 
15.90% 
16.03% 
19320101 
59.5220 
10.30% 
23.60% 
23.09% 
19330101 
57.1540 
0.80% 
24.90% 
3.98% 
19340101 
66.8000 
1.50% 
21.70% 
16.88% 
19350101 
74.2410 
3.00% 
20.10% 
11.14% 
19360101 
84.8300 
1.40% 
16.90% 
14.26% 
19370101 
93.0030 
2.90% 
14.30% 
9.63% 
19380101 
87.3520 
2.80% 
19.00% 
6.08% 
19390101 
93.4370 
0.00% 
17.20% 
6.97% 
19400101 
102.8990 
0.70% 
14.60% 
10.13% 
19410101 
129.3090 
9.90% 
9.90% 
25.67% 
19420101 
165.9520 
9.00% 
4.70% 
28.34% 
19430101 
203.0840 
3.00% 
1.90% 
22.38% 
19440101 
224.4470 
2.30% 
1.20% 
10.52% 
19450101 
228.0070 
2.20% 
1.90% 
1.59% 
19460101 
227.5350 
18.10% 
3.90% 
0.21% 
19470101 
249.6160 
8.80% 
3.90% 
9.70% 
19480101 
274.4680 
3.00% 
4.00% 
9.96% 
19490101 
272.4750 
2.10% 
6.60% 
0.73% 
19500101 
299.8270 
5.90% 
4.30% 
10.04% 
19510101 
346.9140 
6.00% 
3.10% 
15.70% 
19520101 
367.3410 
0.80% 
2.70% 
5.89% 
19530101 
389.2180 
0.70% 
4.50% 
5.96% 
19540101 
390.5490 
0.70% 
5.00% 
0.34% 
19550101 
425.4780 
0.40% 
4.20% 
8.94% 
19560101 
449.3530 
3.00% 
4.20% 
5.61% 
19570101 
474.0390 
2.90% 
5.20% 
5.49% 
19580101 
481.2290 
1.80% 
6.20% 
1.52% 
19590101 
521.6540 
1.70% 
5.30% 
8.40% 
19600101 
542.3820 
1.40% 
6.60% 
3.97% 
19610101 
562.2100 
0.70% 
6.00% 
3.66% 
19620101 
603.9210 
1.30% 
5.50% 
7.42% 
19630101 
637.4510 
1.60% 
5.50% 
5.55% 
19640101 
684.4600 
1.00% 
5.00% 
7.37% 
19650101 
742.2890 
1.90% 
4.00% 
8.45% 
19660101 
813.4140 
3.50% 
3.80% 
9.58% 
19670101 
859.9580 
3.00% 
3.80% 
5.72% 
19680101 
940.6510 
4.70% 
3.40% 
9.38% 
19690101 
1017.6150 
6.20% 
3.50% 
8.18% 
19700101 
1073.3030 
5.60% 
6.10% 
5.47% 
19710101 
1164.8500 
3.30% 
6.00% 
8.53% 
19720101 
1279.1100 
3.40% 
5.20% 
9.81% 
19730101 
1425.3760 
8.70% 
4.90% 
11.43% 
19740101 
1545.2430 
12.30% 
7.20% 
8.41% 
19750101 
1684.9040 
6.90% 
8.20% 
9.04% 
19760101 
1873.4120 
4.90% 
7.80% 
11.19% 
19770101 
2081.8260 
6.70% 
6.40% 
11.12% 
19780101 
2351.5990 
9.00% 
6.00% 
12.96% 
19790101 
2627.3340 
13.30% 
6.00% 
11.73% 
19800101 
2857.3070 
12.50% 
7.20% 
8.75% 
19810101 
3207.0420 
8.90% 
8.50% 
12.24% 
19820101 
3343.7890 
3.80% 
10.80% 
4.26% 
19830101 
3634.0380 
3.80% 
8.30% 
8.68% 
19840101 
4037.6130 
3.90% 
7.30% 
11.11% 
19850101 
4338.9790 
3.80% 
7.00% 
7.46% 
19860101 
4579.6310 
1.10% 
6.60% 
5.55% 
19870101 
4855.2150 
4.40% 
5.70% 
6.02% 
19880101 
5236.4380 
4.40% 
5.30% 
7.85% 
19890101 
5641.5800 
4.60% 
5.40% 
7.74% 
19900101 
5963.1440 
6.10% 
6.30% 
5.70% 
19910101 
6158.1290 
3.10% 
7.30% 
3.27% 
19920101 
6520.3270 
2.90% 
7.40% 
5.88% 
19930101 
6858.5590 
2.70% 
6.50% 
5.19% 
19940101 
7287.2360 
2.70% 
5.50% 
6.25% 
19950101 
7639.7490 
2.50% 
5.60% 
4.84% 
19960101 
8073.1220 
3.30% 
5.40% 
5.67% 
19970101 
8577.5520 
1.70% 
4.70% 
6.25% 
19980101 
9062.8170 
1.60% 
4.40% 
5.66% 
19990101 
9630.6630 
2.70% 
4.00% 
6.27% 
20000101 
10252.3470 
3.40% 
3.90% 
6.46% 
20010101 
10581.8220 
1.60% 
5.70% 
3.21% 
20020101 
10936.4180 
2.40% 
6.00% 
3.35% 
20030101 
11458.2460 
1.90% 
5.70% 
4.77% 
20040101 
12213.7300 
3.30% 
5.40% 
6.59% 
20050101 
13036.6370 
3.40% 
4.90% 
6.74% 
20060101 
13814.6090 
2.50% 
4.40% 
5.97% 
20070101 
14451.8600 
4.10% 
5.00% 
4.61% 
20080101 
14712.8450 
0.10% 
7.30% 
1.81% 
20090101 
14448.9320 
2.70% 
9.90% 
1.79% 
20100101 
14992.0520 
1.50% 
9.30% 
3.76% 
20110101 
15542.5820 
3.00% 
8.50% 
3.67% 
20120101 
16197.0070 
1.70% 
7.80% 
4.21% 
20130101 
16784.8510 
1.50% 
6.70% 
3.63% 
20140101 
17521.7470 
0.80% 
5.60% 
4.39% 
20150101 
18219.2970 
0.70% 
5.00% 
3.98% 
20160101 
18707.1890 
2.10% 
4.70% 
2.68% 
20170101 
19485.3940 
2.10% 
4.02% 
4.16% 
SYMMARY STATISATICS
REGRESSION OUTPUT FROM EXCEL
ANYTHING ELSE
ANOVA 

df 
SS 
MS 
F 
Significance F 

Regression 
1 
0.0403 
0.0403 
8.6338 
0.0043 
Residual 
78 
0.3640 
0.0047 

Total 
79 
0.4043 
Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 

Intercept 
0.0994 
0.0137 
7.2680 
0 
0.0722 
0.1266 
0.0722 
0.1266 
Unemployment X 
0.4613 
0.1570 
2.9383 
0.0043 
0.7738 
0.1487 
0.7738 
0.1487 
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