can I suggest that you set out each of the regression results in neat tables – go to American Economic Review for an example of how you can set out tables with regression results? YOu need to discuss the tstatistic – if its greater than 1.96 its significant at the 0.05 level.
Finally, its is unclear how you forecast deposits forward. can you explain that some more?
Finally, combine all your work into 1 paper, put a cover and contents page on it, and I will give you a mark for this course.
Request you help immediately as I need to submit within deadline.
Attachment
Introduction
A research study was conducted at the beginning of the exercise where the factors that determined the growth of deposit taking institutionsin India were studied. In the study, a historical study was conducted to determine the nature of the growth in deposit taking institutions. A comprehensive literature review determined that there were several factors that contributed to this growth. Some of the factors that were selected as determinants of the growth were population growth, economic growth, government policies and improved employment. Economic growth was described in terms of GDP growth, gross capital formation and levels of inflation. Inflation was also a reflection of government’s fiscal policy. The development of the economy was also quantified by studying the growth in the most robust sectors of the economy. In the case of India, the construction industry, service industry and the manufacturing industry were selected as the most robust and determined to have the most significant contribution to the economy. In order to understand the effect that each of these factors had on the growth of the deposit takinginstitutions in India, a regression analysis was carried out on these factors.
Regression analysis
Regression is a statistical analysis method that quantifies the effect that a factor has on another variable. It is a desirable method for use when the researcher wants to determine how a set of independent variables determine the value of a dependent variable. The regression equation gives both the magnitude of the effect as a number and the direction of the effect as either positive and negative (Damodharan, 2009). In the present research study, the values for the various factors were obtained from an array of websites. Data was collected for 30m years beginning from the financial year 19841985 to 20132014. The data for the GDP, employment, manufacturing, service industry, gross capital formation and the inflation was obtained from the Reserve Bank of India database. The data for deposits was obtained in terms of deposits in the nonbank financial institutions and banks. These two sets of data were then added to obtain the total amounts of deposits in the Indian financial institutions. The total deposits were recorded in billion Rupees. The population data was recorded in terms of millions while the inflation was recorded as a year on year percentage. The data on population was obtained from the World Bank database. The data was recorded in an MS excel data sheet and analyzed using the Minitab computer data analysis software.
Data analysis
A regression analysis was done for each variable’s effect on the financial deposits. Deposit was selected as the dependent level while the other factors were selected as the independent factors. In the initial analysis each of the factor was analyzed individually. This was done in order to determine the variables that would constitute a reliable significant regression model. The factors that had significant effect were selected and used in the development of a multiple regression model with multiple factors.
Results
The regression analysisindicated that there were factors that would be reliably used in the prediction of the growth of deposits in Indian banks. An analysis was first carried out for each individual predictor factor. Out of the 7 factors analyzed, 6 factors were found to significantly influence Deposit growth.
Population was found to significantly influence the growth in financial depositsR^{2} = 0.81, F (1, 28) = 11.72, p =. 1.20035E11, with the regression equation indicating the equation
Deposits = 12.93 (Population) – 10157388.79. (Figure 1)
Employment also had a positive influence on the deposit growth with:R^{2} = .29508904, F (1, 28) = 11.72, p = .001921582, and Regression equation:
Deposits =183397.219 (Employment [millions]) – 4666639.838 (FIGURE 2)
The other factors that influenced the level of deposits significantly were: Gross capital formation: R^{2} = 0.979509792, F (1, 28) = 11.72, p=3.46998E25(figure 3), and Construction R^{2} = 0.97, F (1, 28) = 11.72, p= 2.08536E22, GDP: R^{2} = 0.96, F (1, 28) = 11.72, p< .05 and Year:R^{2} = 0.005756683, F (1, 28) = 112.9322245, p =. 2.47E11.
A regression analysis for inflation indicated that there had no significant influence on the deposit growth:R^{2} = 0.005756683, F (1, 28) = 0.162120414, p =. 0.690269444. The model returned a low t value and a high p value indicating that the difference was not significantly due to the predictors. Furthermore, the less value of R^{2 }indicated that only 0.5% of the effects on the predicted variable were due to the regression model. The inflation was thus rejected as a regression factor.
A combined model for the significant factors was then developed. This model was reliable R^{2}=0.97 p=1.17371E18. The regression summary indicated that 97% of the effects on the predicted value would be attributed to the components of the model and that the probability of getting significant values was higher than 99%.
Discussion
Several fiscal factors have been known to contribute to the growth of both banking and nonbank deposit taking institutions in India. However, according to Anon. (2011), empirical studies on the financial institutions in India have failed to identify a reliable correlation between deposits and the inflation level. Consequent RBI reports have also failed to link inflation to deposit culture of the population, attributing the variations to fiscal policies and financial dynamics instead. The most likely explanation for this occurrence would be due to a consistent rise in thelevel of foreigndirectinvestments in the country, which as a policy, have to be conducted through financial institutions. However, inflation has been shown to directly influence growth in deposits in other regions. This is an occurrence noted in Asian countries (Eichengreen and Arteta, 2000), the U.S (Gambarcota, 2011) and European Union countries (Eichberger and Summer, 2005). The RBI (2012) proposed policies to control inflation as reliable measures to spur growth in deposits in both banking and nonbanking financial institutions.
The growth in employment opportunities was to be expected to lead to a rise in the amount of deposits in two major ways. Employers opt to outsource their wagebill management to financial institutions. The employees are in turn required to operate deposit accounts. In the second instance, the number of employed people increase and these form financial groups where they pool their resources for investment purposes. Financial institutions offer a perfect temporary hold for their money as they also assist the potential investors with financial advice and credit services.
The growth in the gross capital formation indicates the rate at which money is generated within the specific industries in the country. This value has been increasing in many sectors of the economy indicating that there is more money being generated in the economy. As this extra money is generated, most of it is processed through the deposit taking institutions. The level of deposits ends up rising. When the gross capital formation is consistently rising then there is more money in the economy and more opportunity for deposit taking institutions to offer their services. The gross capital formation within most of the economic sectors has been growing for the last decade (Gambacorta, 2011). The RBI (2012) also predicted that the trend would continue upwards for the next decade buoyed by a rise in domestic demand for manufactured products in the country. The high consumption of the industrial products within the country shields the country’s economy from fiscal uncertainties in the region and variation in experts.
Prediction for the deposit levels in 2020
The regression model for the effect of year on the growth in deposit returned an R^{2}= 0.8, F (1, 28) = 11.72, p < .05. And therefore was determined as a reliable model for the future prediction.
The regression equation was stated as
The use of this model in the prediction of the amount of deposits for the year 2020
Reading from the prediction model, the level of deposits in the year 2020 was found to be Rupees(Billions) 7600
Conclusion
Theregression analysis showed that it is possible to predict the future growth of the Indian deposit taking institutions using a regression analysis. It was confirmed that employment, GDP growth, manufacturing, gross capital formation, population, construction and year were all reliable as predicting factors for deposits. It is concluded therefore that these factors have significant effect on the growth in deposits.
References
Anonymous. (2012).Illiquid Banks, Financial Stability, and Interest Rate Policy. Journal of
Political Economy, 120 (3): 552–91.
Eichberger, J., Summer, M. (2005). Bank Capital, Liquidity, and Systemic Risk. Journal of the
European Economic Association, 3(2):547555.
Eichengreen, B., Arteta, C. (2000). Banking Crises in Emerging Markets: Presumptions and
Evidence: Center for International and Development Economics Research Working Paper No. 115.
Gambacorta, L. (2011).Do Bank Capital and Liquidity Affect Real Economic Activity in the
Long Run? A VECM Analysis for the US”, Economic Notes, 40(3):7591.
Freedman, David A. (2005). Statistical Models: Theory and Practice, Cambridge University
Press
Rousseau, P. L., Wachtel, P. (2002). Inflation thresholds and the finance– growth nexus; Journal
of International Money and Finance, 21:777 793.
Sen, A., Srivastava, M. (2011). Regression Analysis — Theory, Methods, and Applications,
SpringerVerlag, Berlin, 2011 (4th printing).
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28 (3): 689.
Damodharan, A. (2009). Fundamental principles of relative valuation.
Appendices
Table 1: The effect of development in construction on the growth of deposits in India.

Coefficients 
Standard Error 
T Stat 
PValue 
Intercept 
10157388.79 
1111127.455 
9.141515443 
6.71736E10 
Population 
12.92625483 
1.178345525 
10.96983402 
1.20035E11 
R^{2} = 0.81, F (1, 28) = 11.72, p =.1.20035E11
Regression equation: Deposits = 12.93 (Population) – 10157388.79
Table 2: The effect of development in employment on the growth of deposits in India.

Coefficients 
Standard Error 
T Stat 
PValue 
Intercept 
4666639.838 
1942715.485 
2.40212212 
0.02317825 
Employment(Millions) 
183397.219 
53567.86307 
3.423642618 
0.001921582 
R^{2} = .29508904, F (1, 28) = 11.72, p = .001921582
Regression equation=183397.219 (Employment [millions]) 4666639.838
Table 3: The regression results for Gross capital formation (Rupees)
Coefficients  Standard Error  T Stat  PValue  
Intercept 
697507.2395 
89951.03957 
7.754298814 
1.90338E08 

Gross Capital Formation(Rupees Billion) 
316.9105534 
8.662165475 
36.58560372 
3.46998E25 

R^{2} = 0.979509792, F (1, 28) = 11.72, p=3.46998E25
. Regression equation=316.91 (gross capital formation (Rupees billion)) 697507.2395
Table 4: The effect of development in construction on the growth of deposits in India.

Coefficients 
Standard Error 
T Stat 
PValue 
Intercept 
118271.6655 
92867.8576 
1.273547905 
0.213294951 
Construction(Rupees Billion) 
848.7471253 
29.32714987 
28.94066178 
2.08536E22 
R^{2} = 0.97, F (1, 28) = 11.72, p= 2.08536E22
. Regression equation=848.7471 (Construction (Rupees billion)) + 118271.6655
Table 5: the effect of development in GDP on the growth of deposits in India.

Coefficients 
Standard Error 
T Stat 
PValue 
Intercept 
80384.09122 
112566.0254 
0.714106152 
0.481072612 
GDP(Rupees Billion) 
70.45130997 
2.817487571 
25.0050118 
1.07757E20 
R^{2} = 0.96, F (1, 28) = 11.72, p =1.07757E20
Regression equation= 70.4513 (GDP (Rupees billion)) 80384.09122
Table 6 the effect of development in inflation on the growth of deposits in India.

Coefficients 
Standard Error 
t Stat 
Pvalue 
Intercept 
2307713.784 
1106747.15 
2.085131898 
0.046293219 
Inflation 
60540.98848 
150359.4249 
0.402641793 
0.690269444 
R^{2} = 0.005756683, F (1, 28) = 0.162120414, p =. 0.690269444
Regression equation=2307713.78460540.98848 (Inflation)
Table 7 the effect of financial year on the growth of deposits in India.

Coefficients 
Standard Error 
t Stat 
Pvalue 
Intercept 
422712.4674 
39955.62 
10.5795 
2.74E11 
YEAR 
212.4606986 
19.99262 
10.62696 
2.47E11 
R^{2} = 0.005756683, F (1, 28) = 112.9322245, p =. 2.47E11
Regression equation=212.4607(YEAR)422712.4674
Table 8 the effect of all factors in construction on the growth of deposits in India.
Regression Statistics 

Multiple R 
0.99054456 
R Square 
0.981178525 
Adjusted R Square 
0.976268575 
Standard Error 
321875.2432 
Observations 
30 

Coefficients 
Standard Error 
T Stat 
PValue 
Intercept 
2284599.183 
1783432.876 
1.28101215 
0.212955892 
Employment(Millions) 
47513.06445 
40688.79172 
1.167718737 
0.254883355 
Gross Capital Formation(Rupees Billion) 
357.0909971 
146.3332728 
2.440258393 
0.022796183 
Manufacturing(Rupees Billion) 
249.5568941 
351.6160495 
0.709742614 
0.484997715 
Construction(Rupees Billion) 
111.6239601 
710.8700591 
0.157024422 
0.876595886 
GDP(Rupees Billion) 
4.536306322 
42.6080255 
0.106466007 
0.916135803 
Population 
3.900710315 
3.670002927 
1.062862999 
0.298876874 
F (6, 23) = 199.8347307, p =1.17371E18