Final Project - Report

St. Cloud State UniversityECON 470/570

india

Emerald Peltier 12945651 (Junior)

Table of Contents

I. Abstract 2 II. Introduction 2

III. Labor Market 2 a. Unemployment, total (% of total labor force)b. Age Dependency Ratio (% of working population)

IV. Goods and Services Market 4 a. GDP Growthb. Exports to Imports Ratio

V. Financial Market 5 a. Interest Rate, Discount Rateb. Share Prices

VI. Forecasting Method 6 a. Labor Market 6

i. Unemploymentii. Age Dependency Ratio

b. Goods and Services Market 10 i. GDP Growth

ii. Exports to Imports Ratioc. Financial Market 15

i. Interest Rate, Discount Rateii. Share Prices

VII. Results 19 a. Labor Market 19

i. Unemploymentii. Age Dependency Ratio

b. Goods and Services Market 21 i. GDP Growth

ii. Exports to Imports Ratioc. Financial Market 23

i. Interest Rate, Discount Rateii. Share Prices

VIII. Conclusion 25 IX. References 25

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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

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

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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

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

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

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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

VI. Forecasting Methoda. Labor Market

i. Unemployment

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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.

Dependent Variable: UNEMPLOYMENTMethod: Least SquaresDate: 05/04/16 Time: 22:49Sample: 1999Q1 2013Q4Included observations: 60Convergence achieved after 42 iterations

Variable Coefficient Std. Error t-Statistic Prob.

C 3.961086 0.168819 23.46346 0.0000AR(1) 0.900186 0.103138 8.728010 0.0000MA(1) 0.494196 0.187163 2.640458 0.0107

R-squared 0.908440 Mean dependent var 3.960000Adjusted R-squared 0.903535 S.D. dependent var 0.324845S.E. of regression 0.100893 Akaike info criterion -1.640530Sum squared resid 0.570046 Schwarz criterion -1.500907Log likelihood 53.21591 Hannan-Quinn criter. -1.585916F-statistic 185.2067 Durbin-Watson stat 1.885701Prob(F-statistic) 0.000000

Inverted AR Roots .90Inverted MA Roots -.49

Table 1: Output using ARMA (1, 1) to model Unemployment

Figure 7: Actual, Fitted, Residual Graph using ARMA (1, 1) to model unemployment

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Date: 05/04/16 Time: 22:55Sample: 1999Q1 2013Q4Included observations: 60Q-statistic probabilities adjusted for 2 ARMA terms

Autocorrelation Partial Correlation AC PAC Q-Stat Prob

. | . | . | . | 1 0.040 0.040 0.0987 . |** | . |** | 2 0.235 0.234 3.6335 . |*. | . |*. | 3 0.113 0.103 4.4689 0.035 ***| . | ****| . | 4 -0.406 -0.498 15.426 0.000 . | . | . | . | 5 0.014 -0.010 15.439 0.001 **| . | . | . | 6 -0.277 -0.047 20.741 0.000 . | . | . |*. | 7 -0.047 0.096 20.895 0.001 **| . | *****| . | 8 -0.341 -0.630 29.201 0.000 . | . | . |** | 9 0.040 0.345 29.316 0.000 . |*. | . |** | 10 0.079 0.280 29.783 0.000 . | . | . |*. | 11 0.027 0.167 29.839 0.000 . |**** | **| . | 12 0.538 -0.295 52.258 0.000 . | . | . |** | 13 0.002 0.220 52.258 0.000 . |*. | . |*. | 14 0.189 0.125 55.161 0.000 . | . | . | . | 15 0.013 -0.038 55.174 0.000 . | . | . | . | 16 -0.027 0.054 55.237 0.000

Table 2: Correlogram indicating no correlation between residuals after treatment

Figure 8: Residual Histogram

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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.

Dependent Variable: AGEMethod: Least SquaresDate: 05/04/16 Time: 23:22Sample: 1999Q1 2013Q4Included observations: 60Convergence achieved after 52 iterations


C 7.087819 0.096551 73.41012 0.0000T 0.018240 0.004052 4.501166 0.0000T2 2.08E-05 4.35E-05 0.477904 0.6346

AR(1) 0.942663 0.045572 20.68491 0.0000MA(1) 0.675832 0.107882 6.264528 0.0000



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Table 3: Output after modeling with ARMA (1, 1) and treating the trend

Figure 9: Actual, fitted, residual graph


Autocorrelation Partial Correlation AC PAC Q-Stat Prob*

. |*. | . |*. | 1 0.086 0.086 0.4690 . |*** | . |*** | 2 0.418 0.413 11.668 . |** | . |** | 3 0.238 0.220 15.356 0.000 .*| . | ***| . | 4 -0.125 -0.386 16.391 0.000 . |*. | . | . | 5 0.199 0.029 19.060 0.000 .*| . | . |*. | 6 -0.092 0.121 19.644 0.001 . | . | . |*. | 7 0.055 0.076 19.857 0.001 .*| . | **| . | 8 -0.093 -0.288 20.469 0.002 . | . | . | . | 9 0.006 0.064 20.472 0.005 .*| . | . | . | 10 -0.084 0.056 20.998 0.007 . | . | . | . | 11 -0.043 0.012 21.139 0.012 .*| . | **| . | 12 -0.095 -0.266 21.843 0.016 .*| . | . | . | 13 -0.114 -0.000 22.873 0.018 .*| . | .*| . | 14 -0.168 -0.088 25.146 0.014 .*| . | . |*. | 15 -0.071 0.142 25.565 0.019 **| . | **| . | 16 -0.218 -0.297 29.596 0.009

Table 4: Correlogram showing no significant lags.

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Figure 10: Residual Report showing mean of zero.

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 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: GDPMethod: ARMA Maximum Likelihood (OPG - BHHH)Date: 05/05/16 Time: 00:03Sample: 1999Q1 2013Q4Included observations: 60Convergence achieved after 110 iterations


C 7.207561 0.997731 7.223954 0.0000AR(1) 0.778256 0.120514 6.457784 0.0000MA(1) 0.591089 0.145618 4.059173 0.0002

R-squared 0.835285 Mean dependent var 7.113190Adjusted R-squared 0.826461 S.D. dependent var 2.294699S.E. of regression 0.955925 Akaike info criterion 2.847354Sum squared resid 51.17237 Schwarz criterion 2.986977Log likelihood -81.42063 Hannan-Quinn criter. 2.901969F-statistic 94.66066 Durbin-Watson stat 1.994199Prob(F-statistic) 0.000000

Inverted AR Roots .78

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Inverted MA Roots -.59

Table 5: Output after treating the variable

Figure 11: Actual, fitted, residual graph showing random residuals



. | . | . | . | 1 -0.004 -0.004 0.0009 . |** | . |** | 2 0.248 0.248 3.9389 . |*. | . |*. | 3 0.143 0.154 5.2791 0.022 ***| . | ****| . | 4 -0.455 -0.557 19.022 0.000 . |*. | . | . | 5 0.110 0.065 19.846 0.000 .*| . | . |*. | 6 -0.169 0.187 21.814 0.000 . | . | . | . | 7 -0.052 0.041 22.000 0.001 . | . | **| . | 8 0.059 -0.341 22.250 0.001 .*| . | . | . | 9 -0.090 0.023 22.846 0.002 . | . | . | . | 10 -0.044 0.035 22.987 0.003 . | . | . | . | 11 -0.021 0.040 23.022 0.006 .*| . | **| . | 12 -0.128 -0.292 24.287 0.007 . | . | . |*. | 13 0.030 0.096 24.357 0.011 . | . | . |** | 14 0.065 0.240 24.694 0.016 . | . | . | . | 15 -0.021 -0.054 24.730 0.025 . |** | . | . | 16 0.267 -0.032 30.770 0.006

Table 6: Correlogram

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Figure 12: Residual Report

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: LEXPORTMethod: Least SquaresDate: 05/05/16 Time: 01:05Sample: 1999Q1 2013Q4Included observations: 60Convergence achieved after 4 iterations


C 4.471822 0.081449 54.90352 0.0000T -0.010237 0.006043 -1.693948 0.0963

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T2 7.98E-05 9.13E-05 0.874012 0.3861D1 0.104737 0.019136 5.473352 0.0000D2 -0.005003 0.022108 -0.226293 0.8219D3 0.012273 0.017932 0.684441 0.4967

AR(1) 0.678858 0.115943 5.855112 0.0000



Table 7: Output after treating the trend, increasing seasonality, and cyclical component with AR(1).

Figure 13: Actual, fitted, residual graph after treatments

Date: 05/05/16 Time: 01:08Sample: 1999Q1 2013Q4

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Included observations: 60Q-statistic probabilities adjusted for 1 ARMA term

Autocorrelation Partial Correlation AC PAC Q-Stat Prob*

. | . | . | . | 1 0.004 0.004 0.0009 . | . | . | . | 2 0.027 0.027 0.0480 0.827 . | . | . | . | 3 0.007 0.006 0.0508 0.975 **| . | **| . | 4 -0.311 -0.312 6.4943 0.090 . |** | . |** | 5 0.297 0.333 12.476 0.014 . | . | . | . | 6 -0.012 -0.034 12.486 0.029 . | . | . | . | 7 0.001 -0.016 12.486 0.052 . |*. | . | . | 8 0.154 0.072 14.173 0.048 **| . | .*| . | 9 -0.237 -0.081 18.280 0.019 . | . | .*| . | 10 -0.006 -0.114 18.283 0.032 . | . | . |*. | 11 0.025 0.075 18.331 0.050 . | . | . | . | 12 -0.059 -0.001 18.600 0.069 . |** | . |*. | 13 0.246 0.111 23.395 0.025 .*| . | . | . | 14 -0.081 -0.051 23.919 0.032 .*| . | .*| . | 15 -0.125 -0.105 25.207 0.033 . | . | .*| . | 16 -0.051 -0.093 25.426 0.045

Table 8: Correlogram showing no significant lags.

Figure 14: Residual Report showing residuals with mean zero

c. Financial Market

i. Interest Rate, Discount Rate

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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_RATEMethod: Least SquaresDate: 05/05/16 Time: 01:54Sample: 1999Q1 2013Q4Included observations: 60Convergence achieved after 15 iterations


C 7.337608 0.980324 7.484879 0.0000AR(1) 0.927790 0.060463 15.34478 0.0000

R-squared 0.814766 Mean dependent var 6.749833Adjusted R-squared 0.808266 S.D. dependent var 1.110844S.E. of regression 0.486410 Akaike info criterion 1.478040Sum squared resid 13.48590 Schwarz criterion 1.582758Log likelihood -41.34121 Hannan-Quinn criter. 1.519001F-statistic 125.3591 Durbin-Watson stat 2.099617Prob(F-statistic) 0.000000


Table 9: Output after treating the model

Figure 15: Actual, fitted, residual graph

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Date: 05/05/16 Time: 02:08Sample: 1999Q1 2013Q4Included observations: 60Q-statistic probabilities adjusted for 1 ARMA term


.*| . | .*| . | 1 -0.105 -0.105 0.6947 . | . | . | . | 2 0.019 0.009 0.7190 0.396 . | . | . | . | 3 0.057 0.060 0.9283 0.629 . | . | . | . | 4 0.055 0.068 1.1281 0.770 .*| . | . | . | 5 -0.074 -0.064 1.4966 0.827 . |** | . |** | 6 0.317 0.303 8.4040 0.135 .*| . | .*| . | 7 -0.155 -0.115 10.094 0.121 . | . | . | . | 8 0.004 -0.021 10.095 0.183 . | . | . | . | 9 -0.029 -0.063 10.156 0.254 . | . | . | . | 10 0.024 -0.004 10.199 0.335 . | . | . | . | 11 -0.030 0.022 10.268 0.417 . | . | .*| . | 12 0.000 -0.115 10.268 0.506

Table 10: Correlogram showing no significant lags

Figure 16: Residual Report showing residuals with mean zero

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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_PRICEMethod: Least SquaresDate: 05/05/16 Time: 03:09Sample: 1999Q1 2013Q4Included observations: 60Convergence achieved after 21 iterations


C 0.639589 0.297244 2.151733 0.0357AR(1) 0.974575 0.046966 20.75053 0.0000MA(1) 0.433661 0.107086 4.049644 0.0002



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Table 11: Output after treating the model

Figure 17: Actual, Fitted, Residual Graph



. | . | . | . | 1 -0.005 -0.005 0.0018 . | . | . | . | 2 -0.007 -0.007 0.0046 . | . | . | . | 3 -0.057 -0.057 0.2133 0.644 .*| . | .*| . | 4 -0.151 -0.153 1.7345 0.420 .*| . | .*| . | 5 -0.124 -0.131 2.7709 0.428 . |*. | . |*. | 6 0.116 0.109 3.6926 0.449 . | . | . | . | 7 -0.025 -0.042 3.7375 0.588 .*| . | .*| . | 8 -0.071 -0.115 4.0994 0.663 . | . | . | . | 9 0.012 -0.018 4.1101 0.767 . | . | . | . | 10 -0.044 -0.031 4.2535 0.834 . | . | . | . | 11 0.022 0.028 4.2900 0.891 . |** | . |*. | 12 0.234 0.198 8.5273 0.577 .*| . | .*| . | 13 -0.092 -0.117 9.1968 0.604 . |*. | . |*. | 14 0.145 0.168 10.887 0.539 . | . | . | . | 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

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Figure 18: Residual Report

VII. Results

a. Labor Market

i. Unemployment

Unemployment Forecast Upper Bound Lower Bound2014Q1 3.6 3.624946 3.831792 3.4181

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2014Q2 3.6 3.658497 4.025352 3.2916422014Q3 3.6 3.6887 4.156045 3.2213552014Q4 3.6 3.715888 4.256794 3.1749812015Q1 NA 3.740362 4.337507 3.1432172015Q2 NA 3.762393 4.403219 3.1215682015Q3 NA 3.782226 4.457126 3.1073252015Q4 NA 3.800078 4.501505 3.0986522016Q1 NA 3.816149 4.538089 3.094212016Q2 NA 3.830616 4.568249 3.0929832016Q3 NA 3.843639 4.593096 3.0941812016Q4 NA 3.855362 4.61354 3.097183

ii. Age Dependency Ratio

Age Dependency Forecast Upper Bound Lower Bound2014Q1 8.355329 8.349622 8.359927 8.3393172014Q2 8.38736 8.370017 8.390891 8.3491442014Q3 8.420912 8.390485 8.419277 8.3616922014Q4 8.455983 8.411022 8.446878 8.3751662015Q1 NA 8.431628 8.474126 8.3891292015Q2 NA 8.452301 8.501202 8.4033992015Q3 NA 8.473039 8.528202 8.417876

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2015Q4 NA 8.493842 8.555185 8.4324992016Q1 NA 8.514707 8.58219 8.4472242016Q2 NA 8.535634 8.609248 8.462022016Q3 NA 8.556622 8.636381 8.4768632016Q4 NA 8.577669 8.663606 8.491732

b. Goods and Services Market

i. GDP Growth

GDP Growth Forecast Upper Bound Lower Bound2014Q1 7.364672 7.226893 9.129001 5.3247852014Q2 7.372008 7.222607 10.47076 3.9744572014Q3 7.289921 7.21927 11.09198 3.346562014Q4 7.118412 7.216674 11.4457 2.9876472015Q1 NA 7.214653 11.66248 2.7668262015Q2 NA 7.213081 11.80107 2.6250932015Q3 NA 7.211857 11.89238 2.531332015Q4 NA 7.210904 11.95403 2.4677762016Q1 NA 7.210163 11.99653 2.4237922016Q2 NA 7.209586 12.02638 2.3927912016Q3 NA 7.209137 12.04769 2.370585

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2016Q4 NA 7.208788 12.06312 2.354453

ii. Exports to Imports Ratio

Export/Import Forecast Upper Bound Lower Bound2014Q1 74.35164 70.1999 72.32885 68.070942014Q2 67.86546 60.36171 62.55283 58.170592014Q3 67.69252 61.5229 63.70772 59.338092014Q4 66.6514 60.5965 62.78489 58.408122015Q1 NA 66.95914 69.16235 64.755932015Q2 NA 59.98832 62.20006 57.776572015Q3 NA 61.17167 63.3778 58.965552015Q4 NA 60.27948 62.49044 58.068522016Q1 NA 66.6408 68.86875 64.412852016Q2 NA 59.73177 61.97041 57.493132016Q3 NA 60.9393 63.1733 58.70532016Q4 NA 60.07932 62.31953 57.8391

c. Financial Market

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i. Interest Rate, Discount Rate

Interest Rate Forecast Upper Bound Lower Bound2014Q1 9 8.648011 9.614966 7.6810572014Q2 9 8.553387 9.88889 7.2178852014Q3 9 8.465596 10.06211 6.8690872014Q4 9 8.384144 10.1829 6.585392015Q1 8.67 8.308574 10.27052 6.3466312015Q2 8.42 8.238461 10.33514 6.1417862015Q3 8.25 8.17341 10.38299 5.9638322015Q4 7.75 8.113057 10.41826 5.8078562016Q1 NA 8.057062 10.44392 5.6702042016Q2 NA 8.005111 10.46218 5.5480382016Q3 NA 7.95691 10.47473 5.4390912016Q4 NA 7.912191 10.48287 5.341508

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ii. Share Price

Share Price Forecast Upper Bound Lower Bound2014Q1 1.160596 1.15892 1.292557 1.0252832014Q2 1.310644 1.145716 1.383346 0.9080862014Q3 1.443419 1.132847 1.445106 0.8205892014Q4 1.510228 1.120306 1.495039 0.7455742015Q1 1.572849 1.108084 1.537896 0.6782732015Q2 NA 1.096173 1.57577 0.6165752015Q3 NA 1.084564 1.609795 0.5593332015Q4 NA 1.07325 1.640667 0.5058332016Q1 NA 1.062224 1.668855 0.4555942016Q2 NA 1.051479 1.694692 0.4082662016Q3 NA 1.041007 1.718432 0.3635812016Q4 NA 1.030801 1.740273 0.321329

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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.

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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.

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Documents

Final Project - Report