INDIA
0
Table of Contents I. II. III.
IV.
V.
VI.
VII.
VIII. IX.
Abstract Introduction Labor Market a. Unemployment, total (% of total labor force) b. Age Dependency Ratio (% of working population) Goods and Services Market a. GDP Growth b. Exports to Imports Ratio Financial Market a. Interest Rate, Discount Rate b. Share Prices Forecasting Method a. Labor Market i. Unemployment ii. Age Dependency Ratio b. Goods and Services Market i. GDP Growth ii. Exports to Imports Ratio c. Financial Market i. Interest Rate, Discount Rate ii. Share Prices Results a. Labor Market i. Unemployment ii. Age Dependency Ratio b. Goods and Services Market i. GDP Growth ii. Exports to Imports Ratio c. Financial Market i. Interest Rate, Discount Rate ii. Share Prices Conclusion References
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I. Abstract India is a south Asian country with a population of 1.252 billion. Looking at the history of India’s economy indicates that they have been growing and developing over the past fifteen years. This year India is forecasted to have an economic slowdown which indicates that they are transitioning from a “developing” country into a “developed” country. The labor market will see increases in unemployment and dependents with an aging population. The goods and services market will see a slight decline in GDP growth and an increase in exports to imports which indicates a more self-reliant country. India will also see a slight decline in their discount rate and share price to indicate a slowing financial market. If the country had not gone through such economic growth all of these would indicate a recession. I predict that this is not an indication of a recession but rather a transition. II. Introduction Economies are made up of different market structures and there are many ways you can measure them. This time series report will address three different market structures and the variables that affect them. The labor market is one of the biggest contributors to India’s economy and in this report we will look at two variables, ‘Unemployment, total (% of total labor force)’ and ‘Age Dependency Ratio (% of working population)’, and their effect on India’s labor market. Another significant market structure included in this report is the financial market in which two variables, ‘Interest Rates, Discount Rate for India’ and ‘Total Share Price for all Shares for India’, are considered. The third market this report analyzes is the goods and services market with a focus on ‘Ratio of Exports to Imports for India’ and ‘GDP Growth Rate’ and their impact on that market. From data derived from ‘Federal Reserve Economic Data’ and ‘World Bank’, this report will model each variable by treating the trend, seasonal, and cyclical components and forecast the future values to give insight into what India’s economy will look like during 2016. III. Labor Market a. Unemployment, total (% of total labor force) The first variable in this report is ‘Unemployment, total (% of total labor force)’ which measures the proportion of those actively searching for work but are unable to find work in the entire work force. Unemployment is often used as a way to measure the health of a country’s economy and in general, a lower unemployment rate indicates a stronger economy and a higher unemployment rate indicates a weaker economy. The data derived for this series is from World Bank and a time-series plot for past data is below in figure 1. In this graph, we see that there is no obvious trend or seasonality, but there is a cyclical component that needs to be considered when forecasting.
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Figure 1 Unemployment, total (% of total labor force) 1998 – 2014 Unemployment 4.6
4.4
4.2
4.0
3.8
3.6
3.4 1998
2000
2002
2004
2006
2008
2010
2012
2014
b. Age Dependency Ratio (% of working population) The age dependency ratio measures how many people in a population that are younger than 15 or older than 64 compared to how many people are of the typical working age, 15-64. This gives us insight as to what proportion of a population is considered working age to non-working age. A higher ratio indicates that there is a greater burden on the working population and on the economy to support an aging population. Common knowledge tells us that the longevity of life has increased over time for the majority of countries as medicine has made major improvements, so we should expect that a typical country will have an increasing age dependency ratio. Shown in figure 2 below, we see that there is an increasing linear trend along with a cyclical component that need to be considered when forecasting this model. Figure 2: Graph of Age Dependency 1998 - 2014 Age Dependency 8.6 8.4 8.2 8.0 7.8 7.6 7.4 7.2 7.0 1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
3
IV. Goods and Services Market a. GDP Growth (annual %) Gross Domestic Product (GDP) Growth is the rate of change from year to year at which the value of a basket of goods and services produced in a country can sell for in a given market. This is one of the most important measurements of economic growth in a country as it indicates the gross value added by the producers in a country, or simply put how much the countries goods are worth. Figure 3 is a time series plot of the data derived from World Bank and we can see that there is no obvious trend or seasonality to the model but there is a cyclical component to treat before we can forecast. Figure 3: Graph of GDP Growth 1998 - 2014 GDP Growth 11 10 9 8 7 6 5 4 3 1998
2000
2002
2004
2006
2008
2010
2012
2014
b. Ratio of Exports to Imports for India One variable depicted in this model is the ‘Ratio of Exports to Imports for India’. This variable gives us an amount of import goods an economy can purchase per unit of export good. This is an indicator as to how strong an economy is in that it shows how much a country produces on its own and how much it depends on other countries goods. In general, if a country is importing more than it is exporting, that indicates a weaker economy that is not self-sufficient. As shown in Figure 4 below, India has become more self-sufficient over time as it has a downward sloping quadratic trend. We also see increasing seasonality and cyclicality that need to be addressed.
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Figure 4: Graph of Ratio of Exports to Imports 1998 - 2014 Export To Import 100
90
80
70
60
50 1998
2000
2002
2004
2006
2008
2010
2012
2014
V. Financial Market a. Interest Rate, Discount Rate This report studies the ‘Interest Rates, Discount Rate for India’ as one indicator for the financial market. The interest rate in this report is the rate charged to the commercial banks by the central bank for loans of reserve funds. A lower discount rate indicates a vulnerable economy as the central bank tries to make it easier for people to borrow money so the economy can strengthen again. If a country has a strong economy, it may have a higher discount rate because the economy can handle it. We can see in Figure 5 that India likely went through a recession from 2003 – 2012 as the discount rate stayed very low during that time. It climbed back up after 2012 indicating their economy has been strengthening. There is no obvious trend or seasonality to treat in this model so the cyclical component should be the only one to take into consideration. Figure 5: Graph Interest Rate, Discount Rate Interest Rate 11
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5 1998
2000
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2004
2006
2008
2010
2012
2014
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b. Share Prices ‘Total Share Prices for all Shares for India’ is the other variable for the financial market included in this study. The ‘Total Share Prices…’ is based on 2010 as the base year and it implies economic strength for India on whether it is increasing or decreasing. If the variable is decreasing we can conclude that the economy is not as strong as it was previously; conversely, if it is increasing we can conclude that the economy is getting stronger. We see in Figure 6 that India’s share prices have been climbing pretty steadily since 1998 with a small decrease from 2007 to 2009. In this graph we see possible upward sloping linear trend with a cyclical component. Figure 6 Share Price 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 1998
2000
2002
2004
2006
2008
2010
2012
2014
VI. Forecasting Method a. Labor Market i. Unemployment From the initial graph depicted above in figure 1, we saw that there was a cyclical component to treat when forecasting this variable. After running a quick estimation equation, the correlogram indicated that both the autocorrelation function and partial autocorrelation functions showed that the residuals were decaying with two significant lags, this indicates that we need to use an ARMA (1, 1) to treat the cyclical component. The results are given below in table 1 which is the estimation output and shows that there is no autocorrelation between residuals, the fewest parameters, and the model describes 90.84% of the variability which states the data is closely fit to the regression line. Figure 7 is the actual, fitted, residual graph which shows us graphically that there is no autocorrelation in the residuals because there is no pattern and our fitted line follows closely to the actual line. Table 2 is the correlogram that shows no more significant lags of the model, and figure 8 is the residual histogram showing that the residuals have a mean of zero, indicating no autocorrelation.
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Dependent Variable: UNEMPLOYMENT Method: Least Squares Date: 05/04/16 Time: 22:49 Sample: 1999Q1 2013Q4 Included observations: 60 Convergence achieved after 42 iterations Variable C AR(1) MA(1)
Coefficient
Std. Error
t-Statistic
Prob.
3.961086 0.900186 0.494196
0.168819 0.103138 0.187163
23.46346 8.728010 2.640458
0.0000 0.0000 0.0107
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.908440 0.903535 0.100893 0.570046 53.21591 185.2067 0.000000
Inverted AR Roots Inverted MA Roots
.90 -.49
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
3.960000 0.324845 -1.640530 -1.500907 -1.585916 1.885701
Table 1: Output using ARMA (1, 1) to model Unemployment Figure 7: Actual, Fitted, Residual Graph using ARMA (1, 1) to model unemployment Unemployment 4.6 4.4 4.2 4.0 3.8
.4
3.6 .2
3.4
.0 -.2 -.4 99
00
01
02
03
04
05
Residual
06
07 Actual
08
09
10
11
12
13
Fitted
7
Date: 05/04/16 Time: 22:55 Sample: 1999Q1 2013Q4 Included observations: 60 Q-statistic probabilities adjusted for 2 ARMA terms Autocorrelation
Partial Correlation
.|. | . |** | . |*. | ***| . | .|. | **| . | .|. | **| . | .|. | . |*. | .|. | . |**** | .|. | . |*. | .|. | .|. |
.|. | . |** | . |*. | ****| . | .|. | .|. | . |*. | *****| . | . |** | . |** | . |*. | **| . | . |** | . |*. | .|. | .|. |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
AC
PAC
Q-Stat
Prob
0.040 0.235 0.113 -0.406 0.014 -0.277 -0.047 -0.341 0.040 0.079 0.027 0.538 0.002 0.189 0.013 -0.027
0.040 0.234 0.103 -0.498 -0.010 -0.047 0.096 -0.630 0.345 0.280 0.167 -0.295 0.220 0.125 -0.038 0.054
0.0987 3.6335 4.4689 15.426 15.439 20.741 20.895 29.201 29.316 29.783 29.839 52.258 52.258 55.161 55.174 55.237
0.035 0.000 0.001 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Table 2: Correlogram indicating no correlation between residuals after treatment Figure 8: Residual Histogram Unemployment 16
Series: Residuals Sample 1999Q1 2013Q4 Observations 60
14 12 10 8 6 4 2 0 -0.3
-0.2
-0.1
0.0
0.1
0.2
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
-0.006466 -0.019392 0.299966 -0.332035 0.098078 0.314088 6.488925
Jarque-Bera Probability
31.41800 0.000000
0.3
ii. Age Dependency Ratio The forecasting method used for this variable is the ARMA (1, 1) along with treating the trend shown in Figure 2. Table 3 indicates no autocorrelation, a low amount of parameters, and 99.98% of the variability is explained by the model. Figure 9 shows no pattern to the residual and that the fitted and actual lines match up well. Table 4 shows no significant lags in the model and figure 10 shows the residuals have a mean of zero and that they have constant variance. 8
Dependent Variable: AGE Method: Least Squares Date: 05/04/16 Time: 23:22 Sample: 1999Q1 2013Q4 Included observations: 60 Convergence achieved after 52 iterations Variable C T T2 AR(1) MA(1)
Coefficient
Std. Error
t-Statistic
Prob.
7.087819 0.018240 2.08E-05 0.942663 0.675832
0.096551 0.004052 4.35E-05 0.045572 0.107882
73.41012 4.501166 0.477904 20.68491 6.264528
0.0000 0.0000 0.6346 0.0000 0.0000
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.999812 0.999795 0.004756 0.001221 237.0314 57582.32 0.000000
Inverted AR Roots Inverted MA Roots
.94 -.68
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
7.727217 0.332258 -7.701047 -7.491613 -7.619126 1.794809
Table 3: Output after modeling with ARMA (1, 1) and treating the trend Figure 9: Actual, fitted, residual graph Age Dependency Ratio 8.4 8.0 7.6 .015 7.2
.010 .005
6.8
.000 -.005 -.010 -.015 99
00
01
02
03
04
05
Residual
06
07 Actual
08
09
10
11
12
13
Fitted
9
Date: 05/04/16 Time: 23:25 Sample: 1999Q1 2013Q4 Included observations: 60 Q-statistic probabilities adjusted for 2 ARMA terms Autocorrelation
Partial Correlation
. |*. | . |*** | . |** | .*| . | . |*. | .*| . | .|. | .*| . | .|. | .*| . | .|. | .*| . | .*| . | .*| . | .*| . | **| . |
. |*. | . |*** | . |** | ***| . | .|. | . |*. | . |*. | **| . | .|. | .|. | .|. | **| . | .|. | .*| . | . |*. | **| . |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
AC
PAC
Q-Stat Prob*
0.086 0.418 0.238 -0.125 0.199 -0.092 0.055 -0.093 0.006 -0.084 -0.043 -0.095 -0.114 -0.168 -0.071 -0.218
0.086 0.413 0.220 -0.386 0.029 0.121 0.076 -0.288 0.064 0.056 0.012 -0.266 -0.000 -0.088 0.142 -0.297
0.4690 11.668 15.356 16.391 19.060 19.644 19.857 20.469 20.472 20.998 21.139 21.843 22.873 25.146 25.565 29.596
0.000 0.000 0.000 0.001 0.001 0.002 0.005 0.007 0.012 0.016 0.018 0.014 0.019 0.009
Table 4: Correlogram showing no significant lags. Figure 10: Residual Report showing mean of zero. Age Dependency Ratio 8
Series: Residuals Sample 1999Q1 2013Q4 Observations 60
7 6 5 4 3 2
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
8.50e-05 -0.000432 0.011702 -0.013358 0.004549 -0.199395 3.854125
Jarque-Bera Probability
2.221408 0.329327
1 0 -0.010
-0.005
0.000
0.005
0.010
b. Goods and Services Market i. GDP Growth The model used in this forecasting method was ARMA (1, 1) to treat the cyclical component. Table 5 shows no autocorrelation, few parameters, and 83.53% of the variability is described
10
in the model. Figure 11 shows no pattern to the residuals and the actual and fitted lines are closely related. Table 6 shows no significant lags of the variable and figure 12 shows residuals have a mean zero and low standard deviation. Dependent Variable: GDP Method: ARMA Maximum Likelihood (OPG - BHHH) Date: 05/05/16 Time: 00:03 Sample: 1999Q1 2013Q4 Included observations: 60 Convergence achieved after 110 iterations Variable
Coefficient
Std. Error
t-Statistic
Prob.
7.207561 0.778256 0.591089
0.997731 0.120514 0.145618
7.223954 6.457784 4.059173
0.0000 0.0000 0.0002
C AR(1) MA(1) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.835285 0.826461 0.955925 51.17237 -81.42063 94.66066 0.000000
Inverted AR Roots Inverted MA Roots
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
7.113190 2.294699 2.847354 2.986977 2.901969 1.994199
.78 -.59
Table 5: Output after treating the variable Figure 11: Actual, fitted, residual graph showing random residuals GDP Growth 12 10 8 6 4 4 2 2 0 -2 -4 99
00
01
02
03
04
05
Residual
06
07 Actual
08
09
10
11
12
13
Fitted
11
Date: 05/05/16 Time: 00:05 Sample: 1999Q1 2013Q4 Included observations: 60 Q-statistic probabilities adjusted for 2 ARMA terms Autocorrelation
Partial Correlation
.|. | . |** | . |*. | ***| . | . |*. | .*| . | .|. | .|. | .*| . | .|. | .|. | .*| . | .|. | .|. | .|. | . |** |
.|. | . |** | . |*. | ****| . | .|. | . |*. | .|. | **| . | .|. | .|. | .|. | **| . | . |*. | . |** | .|. | .|. |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
AC
PAC
Q-Stat
Prob
-0.004 0.248 0.143 -0.455 0.110 -0.169 -0.052 0.059 -0.090 -0.044 -0.021 -0.128 0.030 0.065 -0.021 0.267
-0.004 0.248 0.154 -0.557 0.065 0.187 0.041 -0.341 0.023 0.035 0.040 -0.292 0.096 0.240 -0.054 -0.032
0.0009 3.9389 5.2791 19.022 19.846 21.814 22.000 22.250 22.846 22.987 23.022 24.287 24.357 24.694 24.730 30.770
0.022 0.000 0.000 0.000 0.001 0.001 0.002 0.003 0.006 0.007 0.011 0.016 0.025 0.006
Table 6: Correlogram Figure 12: Residual Report GDP Growth 14
Series: Residuals Sample 1999Q1 2013Q4 Observations 60
12 10 8 6 4 2 0 -3
-2
-1
0
1
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
-0.021572 0.071317 2.251334 -3.419052 0.931051 -1.064731 6.211916
Jarque-Bera Probability
37.12752 0.000000
2
12
ii. Exports to Imports Ratio This variable had increasing seasonality so first the natural log of the model was taken, then the trend and seasonality were treated as well. From there an AR(1) was depicted from a correlogram showing a decaying autocorrelation and a cut off of one lag on the partial autocorrelation. Table 7 shows no autocorrelation through the Durbin-Watson statistic, few parameters through a low Schwarz criterion, and that 78.97% of the variability in the model was accounted for. This is slightly low compared to most models meaning this model has a higher volatility and therefor is harder to predict. Figure 13 shows no pattern in the residuals and that the actual and fitted lines are closely related. Table 8 shows no more significant lags of the variable to account for and figure 14 shows the residuals with a bell shape around mean zero. Dependent Variable: LEXPORT Method: Least Squares Date: 05/05/16 Time: 01:05 Sample: 1999Q1 2013Q4 Included observations: 60 Convergence achieved after 4 iterations Variable
Coefficient
Std. Error
t-Statistic
Prob.
C T T2 D1 D2 D3 AR(1)
4.471822 -0.010237 7.98E-05 0.104737 -0.005003 0.012273 0.678858
0.081449 0.006043 9.13E-05 0.019136 0.022108 0.017932 0.115943
54.90352 -1.693948 0.874012 5.473352 -0.226293 0.684441 5.855112
0.0000 0.0963 0.3861 0.0000 0.8219 0.4967 0.0000
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.789700 0.761391 0.069041 0.247865 79.23130 27.89514 0.000000
Inverted AR Roots
.68
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
4.268960 0.141339 -2.374377 -2.095131 -2.265148 1.966719
Table 7: Output after treating the trend, increasing seasonality, and cyclical component with AR(1).
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Figure 13: Actual, fitted, residual graph after treatments Export To Import Ratio 4.6 4.4 4.2 .2 4.0 .1 3.8 .0 -.1 -.2 99
00
01
02
04
03
05
06
Residual
08
07
09
Actual
10
11
12
13
Fitted
Date: 05/05/16 Time: 01:08 Sample: 1999Q1 2013Q4 Included observations: 60 Q-statistic probabilities adjusted for 1 ARMA term Autocorrelation .|. .|. .|. **| . . |** .|. .|. . |*. **| . .|. .|. .|. . |** .*| . .*| . .|.
| | | | | | | | | | | | | | | |
Partial Correlation .|. .|. .|. **| . . |** .|. .|. .|. .*| . .*| . . |*. .|. . |*. .|. .*| . .*| .
| | | | | | | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
AC
PAC
Q-Stat Prob*
0.004 0.027 0.007 -0.311 0.297 -0.012 0.001 0.154 -0.237 -0.006 0.025 -0.059 0.246 -0.081 -0.125 -0.051
0.004 0.027 0.006 -0.312 0.333 -0.034 -0.016 0.072 -0.081 -0.114 0.075 -0.001 0.111 -0.051 -0.105 -0.093
0.0009 0.0480 0.0508 6.4943 12.476 12.486 12.486 14.173 18.280 18.283 18.331 18.600 23.395 23.919 25.207 25.426
0.827 0.975 0.090 0.014 0.029 0.052 0.048 0.019 0.032 0.050 0.069 0.025 0.032 0.033 0.045
Table 8: Correlogram showing no significant lags.
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Figure 14: Residual Report showing residuals with mean zero Export To Import Ratio 12
Series: Residuals Sample 1999Q1 2013Q4 Observations 60
10
8
6
4
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
0.001463 0.003164 0.158545 -0.174396 0.064799 0.124590 3.097663
Jarque-Bera Probability
0.179071 0.914356
2
0 -0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
c. Financial Market i. Interest Rate, Discount Rate An AR(1) was used to treat the cyclical component in this model after viewing a correlogram that had decaying autocorrelation and a partial autocorrelation that cut off after a lag of 1 variable. Table 9 is the output after treating the cyclical component and the table shows no autocorrelation, few parameters, and 81.48% of the variability in the model is accounted for. Figure 15 is a graph that shows the residuals have no pattern and the actual and fitted lines closely match. Table 10 is the correlogram which shows no significant lags of the variable. Figure 16 is the residual output showing the residuals have a mean zero. Dependent Variable: INTEREST_RATE Method: Least Squares Date: 05/05/16 Time: 01:54 Sample: 1999Q1 2013Q4 Included observations: 60 Convergence achieved after 15 iterations Variable C AR(1) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Inverted AR Roots
Coefficient
Std. Error
t-Statistic
Prob.
7.337608 0.927790
0.980324 0.060463
7.484879 15.34478
0.0000 0.0000
0.814766 0.808266 0.486410 13.48590 -41.34121 125.3591 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
6.749833 1.110844 1.478040 1.582758 1.519001 2.099617
.93
15
Table 9: Output after treating the model Figure 15: Actual, fitted, residual graph
Interest Rate, Discount Rate 11 10 9 8 7
3
6 2
5
1 0 -1 -2 99
00
01
02
03
04
05
06
Residual
07 Actual
08
09
10
11
12
13
Fitted
Date: 05/05/16 Time: 02:08 Sample: 1999Q1 2013Q4 Included observations: 60 Q-statistic probabilities adjusted for 1 ARMA term Autocorrelation .*| . .|. .|. .|. .*| . . |** .*| . .|. .|. .|. .|. .|. .|. .|. .|.
| | | | | | | | | | | | | | |
Partial Correlation .*| . .|. .|. .|. .|. . |** .*| . .|. .|. .|. .|. .*| . . |*. .|. .|.
| | | | | | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
AC
PAC
Q-Stat
Prob
-0.105 0.019 0.057 0.055 -0.074 0.317 -0.155 0.004 -0.029 0.024 -0.030 0.000 -0.001 0.001 -0.009
-0.105 0.009 0.060 0.068 -0.064 0.303 -0.115 -0.021 -0.063 -0.004 0.022 -0.115 0.091 -0.016 0.028
0.6947 0.7190 0.9283 1.1281 1.4966 8.4040 10.094 10.095 10.156 10.199 10.268 10.268 10.268 10.268 10.274
0.396 0.629 0.770 0.827 0.135 0.121 0.183 0.254 0.335 0.417 0.506 0.592 0.672 0.742 16
.|.
|
.|.
|
16 -0.001 -0.023 10.274 0.802
Table 10: Correlogram showing no significant lags
Figure 16: Residual Report showing residuals with mean zero Interest Rate, Discount Rate 50
Series: Residuals Sample 1999Q1 2013Q4 Observations 60
40
30
20
10
0 -1.0
-0.5
0.0
0.5
1.0
1.5
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
-0.034524 -0.096588 2.233412 -1.057749 0.476825 2.615450 13.31257
Jarque-Bera Probability
334.2786 0.000000
2.0
ii. Share Prices ARMA (1, 1) was used as the model for this variable after viewing the correlogram. Table 11 is the estimation output which shows no autocorrelation, few parameters, and that 96.5% of the variation in the model is explained. Figure 17 shows that the residuals are random and the actual and fitted lines are closely relatable. In Table 12 we see that there are no significant lags left to treat and figure 18 shows us that the residuals have a mean of zero. We conclude that the variable has been properly treated. Dependent Variable: SHARE_PRICE Method: ARMA Maximum Likelihood (OPG - BHHH) Date: 05/05/16 Time: 03:09 Sample: 1999Q1 2013Q4 Included observations: 60 Convergence achieved after 21 iterations Variable C AR(1) MA(1) R-squared Adjusted R-squared S.E. of regression Sum squared resid
Coefficient
Std. Error
t-Statistic
Prob.
0.639589 0.974575 0.433661
0.297244 0.046966 0.107086
2.151733 20.75053 4.049644
0.0357 0.0000 0.0002
0.964972 0.963095 0.066377 0.246730
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion
0.589020 0.345522 -2.457509 -2.317886
17
Log likelihood F-statistic Prob(F-statistic)
77.72528 514.2358 0.000000
Inverted AR Roots Inverted MA Roots
.97 -.43
Hannan-Quinn criter. Durbin-Watson stat
-2.402895 1.923484
Table 11: Output after treating the model Figure 17: Actual, Fitted, Residual Graph Share Price 1.2 1.0 0.8 0.6 .2
0.4 0.2
.1
0.0 .0 -.1 -.2 99
00
01
02
04
03
05
06
Residual
08
07 Actual
09
10
11
12
13
Fitted
Date: 05/05/16 Time: 03:12 Sample: 1999Q1 2013Q4 Included observations: 60 Q-statistic probabilities adjusted for 2 ARMA terms Autocorrelation .|. .|. .|. .*| . .*| . . |*. .|. .*| . .|. .|. .|. . |** .*| . . |*.
| | | | | | | | | | | | | |
Partial Correlation .|. .|. .|. .*| . .*| . . |*. .|. .*| . .|. .|. .|. . |*. .*| . . |*.
| | | | | | | | | | | | | |
1 2 3 4 5 6 7 8 9 10 11 12 13 14
AC
PAC
Q-Stat
Prob
-0.005 -0.007 -0.057 -0.151 -0.124 0.116 -0.025 -0.071 0.012 -0.044 0.022 0.234 -0.092 0.145
-0.005 -0.007 -0.057 -0.153 -0.131 0.109 -0.042 -0.115 -0.018 -0.031 0.028 0.198 -0.117 0.168
0.0018 0.0046 0.2133 1.7345 2.7709 3.6926 3.7375 4.0994 4.1101 4.2535 4.2900 8.5273 9.1968 10.887
0.644 0.420 0.428 0.449 0.588 0.663 0.767 0.834 0.891 0.577 0.604 0.539 18
.|. .|.
| |
.|. .|.
| |
15 0.005 0.032 10.889 0.620 16 -0.064 -0.005 11.231 0.668
Table 12: Correlogram showing no significant lags Figure 18: Residual Report Share Prices 50
Series: Residuals Sample 1999Q1 2013Q4 Observations 60
40
30
20
10
0 -1.0
-0.5
0.0
0.5
1.0
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
-0.034524 -0.096588 2.233412 -1.057749 0.476825 2.615450 13.31257
Jarque-Bera Probability
334.2786 0.000000
2.0
1.5
VII. Results a. Labor Market i. Unemployment Unemployment 4.8
4.4
4.0
3.6
3.2
2.8 1998
2000
2002
2004
2006
Unemployment Upper Bound
2008
2010
2012
2014
2016
Forecast Lower Bound
19
Unemployment 3.6 3.6 3.6 3.6 NA NA NA NA NA NA NA NA
2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4
Forecast 3.624946 3.658497 3.6887 3.715888 3.740362 3.762393 3.782226 3.800078 3.816149 3.830616 3.843639 3.855362
Upper Bound 3.831792 4.025352 4.156045 4.256794 4.337507 4.403219 4.457126 4.501505 4.538089 4.568249 4.593096 4.61354
Lower Bound 3.4181 3.291642 3.221355 3.174981 3.143217 3.121568 3.107325 3.098652 3.09421 3.092983 3.094181 3.097183
ii. Age Dependency Ratio
Age Dependency Ratio 8.8
8.4
8.0
7.6
7.2
6.8 1998
2000
2002
2004
2006
Age Dependency Upper Bound
2014Q1 2014Q2 2014Q3 2014Q4 2015Q1
Age Dependency 8.355329 8.38736 8.420912 8.455983 NA
Forecast 8.349622 8.370017 8.390485 8.411022 8.431628
2008
2010
2012
2014
2016
Forecast Lower Bound
Upper Bound 8.359927 8.390891 8.419277 8.446878 8.474126
Lower Bound 8.339317 8.349144 8.361692 8.375166 8.389129
0
2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4
NA NA NA NA NA NA NA
8.452301 8.473039 8.493842 8.514707 8.535634 8.556622 8.577669
8.501202 8.528202 8.555185 8.58219 8.609248 8.636381 8.663606
8.403399 8.417876 8.432499 8.447224 8.46202 8.476863 8.491732
b. Goods and Services Market i. GDP Growth
GDP Growth 14
12
10
8
6
4
2 1998
2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1
2000
2002
GDP Growth 7.364672 7.372008 7.289921 7.118412 NA NA NA NA NA
2004
2006
2008
2010
2012
GDP Growth Upper Bound
Forecast Lower Bound
Forecast 7.226893 7.222607 7.21927 7.216674 7.214653 7.213081 7.211857 7.210904 7.210163
Upper Bound 9.129001 10.47076 11.09198 11.4457 11.66248 11.80107 11.89238 11.95403 11.99653
2014
2016
Lower Bound 5.324785 3.974457 3.34656 2.987647 2.766826 2.625093 2.53133 2.467776 2.423792
1
2016Q2 2016Q3 2016Q4
NA NA NA
7.209586 7.209137 7.208788
12.02638 12.04769 12.06312
2.392791 2.370585 2.354453
ii. Exports to Imports Ratio
Export to Import Ratio 100
90
80
70
60
50 1998
2000
2002
2004
2006
Export To Import Upper Bound
2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4
Export/Import 74.35164 67.86546 67.69252 66.6514 NA NA NA NA NA NA NA NA
Forecast 70.1999 60.36171 61.5229 60.5965 66.95914 59.98832 61.17167 60.27948 66.6408 59.73177 60.9393 60.07932
2008
2010
2012
2014
2016
Forecast Lower Bound
Upper Bound 72.32885 62.55283 63.70772 62.78489 69.16235 62.20006 63.3778 62.49044 68.86875 61.97041 63.1733 62.31953
Lower Bound 68.07094 58.17059 59.33809 58.40812 64.75593 57.77657 58.96555 58.06852 64.41285 57.49313 58.7053 57.8391
1
c. Financial Market i.
Interest Rate, Discount Rate Interest Rate, Discount Rate 11
10
9
8
7
6
5 1998
2000
2002
2004
2006
2008
Interest Rate Upper Bound
2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4
Interest Rate 9 9 9 9 8.67 8.42 8.25 7.75 NA NA NA NA
Forecast 8.648011 8.553387 8.465596 8.384144 8.308574 8.238461 8.17341 8.113057 8.057062 8.005111 7.95691 7.912191
2010
2012
2014
2016
Forecast Lower Bound
Upper Bound 9.614966 9.88889 10.06211 10.1829 10.27052 10.33514 10.38299 10.41826 10.44392 10.46218 10.47473 10.48287
Lower Bound 7.681057 7.217885 6.869087 6.58539 6.346631 6.141786 5.963832 5.807856 5.670204 5.548038 5.439091 5.341508
1
ii.
Share Price
Share Price 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 1998
2000
2002
2004
2006
2008
Share Price Upper Bound
2014Q1 2014Q2 2014Q3 2014Q4 2015Q1 2015Q2 2015Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4
Share Price 1.160596 1.310644 1.443419 1.510228 1.572849 NA NA NA NA NA NA NA
Forecast 1.15892 1.145716 1.132847 1.120306 1.108084 1.096173 1.084564 1.07325 1.062224 1.051479 1.041007 1.030801
2010
2014
2012
2016
Forecasting Lower Bound
Upper Bound 1.292557 1.383346 1.445106 1.495039 1.537896 1.57577 1.609795 1.640667 1.668855 1.694692 1.718432 1.740273
Lower Bound 1.025283 0.908086 0.820589 0.745574 0.678273 0.616575 0.559333 0.505833 0.455594 0.408266 0.363581 0.321329
0
VIII.
Conclusion India is working their way into becoming a developed country and we see this in how their economy has changed over the course of the past 15 years. The labor market has been growing stronger in the past but looks like it has started to slow down and we will actually see a slight increase in unemployment this year. The age dependency continues to climb, as expected, which means there are going to be more dependents to care for. The GDP growth rate is going to be very close to what it has the past few years with a very slight decrease. The interesting factor for the financial market is the export to import ratio which has been rapidly increasing and will continue to increase this year even with other indicators showing a “weaker” economy. This indicates that India is becoming more self-sufficient. The financial market looks like it will remain approximately the same as the previous few years with a slight decline in both discount rates and share prices. We see that all three markets are indicating a weakening economy, but looking at the history of India and the fact that they are becoming more self-sufficient could actually indicate that they are evolving from a “developing” country to a “developed country”. When a country goes through that transition it can imitate a small recession as the economy is just “slowing” down.
IX.
References Organization for Economic Co-operation and Development, Ratio of Exports to Imports for India© [XTEITT01INQ156N], retrieved from FRED, Federal Reserve Bank of St. Louis https://research.stlouisfed.org/fred2/series/XTEITT01INQ156N, May 3, 2016. Organization for Economic Co-operation and Development, Total Share Prices for All Shares for India© [SPASTT01INQ661N], retrieved from FRED, Federal Reserve Bank of St. Louis https://research.stlouisfed.org/fred2/series/SPASTT01INQ661N, May 5, 2016. World Bank national accounts data, and OECD National Accounts data files.GDP growth (annual %) (NY.GDP.MKTP.KD.ZG), May 5, 2016 http://databank.worldbank.org/data/reports.aspx?source=2&country=IND&series=&period= International Labour Organization, Key Indicators of the Labour Market database, Unemployment, total (% of total labor force) (modeled ILO estimate) (SL.UEM.TOTL.ZS), May 5, 2016, http://databank.worldbank.org/data/reports.aspx?source=2&country=IND&series=&period= International Monetary Fund, Interest Rates, Discount Rate for India© [INTDSRINM193N], retrieved from FRED, Federal Reserve Bank of St. Louis https://research.stlouisfed.org/fred2/series/INTDSRINM193N, May 4, 2016. World Bank, Age Dependency Ratio: Older Dependents to Working-Age Population for India [SPPOPDPNDOLIND], retrieved from FRED, Federal Reserve Bank of St. Louis https://research.stlouisfed.org/fred2/series/SPPOPDPNDOLIND, May 4, 2016.
0
