# Effect Of Crude Oil Prices On Indian Economy Economics Essay

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This paper analyzes the effect of crude oil prices on the macro economic variables of the Indian economy. The oil prices have started rising significantly since the initiation of the twenty first century; one can analyze the impact of an oil price shock. As the oil prices changes there is a huge impact on the GDP, inflation, unemployment rate and industrial growth production .In short, oil price fluctuation has adverse effects on the economy .The paper seeks to find out the trends, causes of oil price hike in recent times and its impact on the macroeconomic variables of India using multiple regression as a methodology using SPSS software.

Acknowledgment

I would like to express my sincere thanks and gratitude to my mentor for Research Project -2 in Finance, Ms charu banga for her extremely valuable guidance and suggestions throughout the making of this report.

I also thank the dean Dr. Sunil Rai for providing me with an excellent opportunity to learn and present my studies in the form of this project report.

Lastly, I thank my parents for their continuous moral support and encouragement.

I have great satisfaction and immense pride in being a part of this educational institute: SVKM's NMIMS's Mukesh Patel School of Technology Management & Engineering

## Basic intro

Fossil fuels are expected to continue to supply a large amount of the energy world-wide regardless of fears of peaking oil. Oil remains a dominant energy source as its importance in the transportation and industrial sectors is increasing day by day.

2000

36.54

5.83

4.02

7.32

7.5

2001

28.8

3.9

5.4

8.1

6.8

2002

30.56

4.6

5.4

8.8

6

2003

34.94

6.9

3.8

9.5

6.5

2004

44.05

7.6

4.2

9.2

7.4

2005

58.04

9.033

4.2

8.9

7.9

2006

67.92

9.53

5.3

7.8

7.5

2007

75.12

9.99

6.4

7.2

8.5

2008

99.71

6.2

8.3

6.8

4.8

2009

63.79

6.8

0.109

10.7

9.3

2010

80.66

10.1

0.117

10.8

9.7

2011

105.8

7.2

0.089

9.8

4.8

2012

101.08

6.9

0.082

3.8

8.2

## Regression

This first table gives the mean. Standard deviation and sample space of the dependent and independent variable.

## Descriptive Statistics

Mean

Std. Deviation

N

Oil Prices

63.6162

27.69087

13

GDP

7.2756

1.95652

13

Inflation

3.6475

2.72686

13

Unemployment Rate

8.3631

1.87147

13

Industrial Production Growth

7.3000

1.51493

13

Dependent Variable: Oil prices

Independent Variable: GDP, Inflation, unemployment rate, industrial Production growth

MEAN: The Mean or Average is the central tendency of a collection of numbers taken as the sum of the numbers divided by the size of the collection.

STANDARD DEVIATION: In statistics, standard deviation (Ïƒ) shows how much variation or dispersion exists from the mean. A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data points are spread out over a large range of values.

This table gives the details of the correlation between each pair of variables.Correlations

## Pearson Correlation

Oil prices

1

0.419

-0.315

-0.273

-0.066

GDP

0.419

1

-0.165

0.151

0.516

Inflation

-0.315

-0.165

1

-0.266

-0.417

Unemployment Rate

-0.273

0.151

-0.266

1

0.12

Industrial production growth

-0.066

0.516

-0.417

0.12

1

Oil prices

0.077

0.148

0.184

0.415

GDP

0.077

0.296

0.311

0.036

Inflation

0.148

0.296

## .

0.19

0.078

Unemployment Rate

0.184

0.311

0.19

## .

0.348

Industrial production growth

0.415

0.036

0.078

0.348

## N

Oil prices

13

13

13

13

13

GDP

13

13

13

13

13

Inflation

13

13

13

13

13

Unemployment Rate

13

13

13

13

13

Industrial production growth

13

13

13

13

13

Pearson's Correlation: It is the correlation between two variables which reflects the degree to which the variables are related to each other. But we cannot conclude that just because two measurements vary together that one has caused the other, there may be some other external factor affecting that may be the cause of their relation. The most common measure of correlation is the Pearson Product Moment Correlation (called Pearson's correlation for short). Pearson's correlation reflects the degree of linear relationship between two variables. It ranges from +1 to -1.

The possible values of r and their interpretation are given below:

A value of 1 implies that a linear equation describes the relationship between X and Y which are both oil prices in the 1st case with all data points lying on a line for which Y increases as X increases.

A value of âˆ’1 implies that all data points lie on a line for which inflation, unemployment rate and Industrial production growth decreases as X increases.

A value of 0 implies there is no linear correlation between the variables.

If we have a series of n measurements of X and Y written as xi and yi where i = 1, 2, ..., n, then the sample correlation coefficient can be used to estimate the population Pearson correlation r between X and Y. The sample correlation coefficient is written

r_{xy}=\frac{\sum\limits_{i=1}^n (x_i-\bar{x})(y_i-\bar{y})}{(n-1) s_x s_y} =\frac{\sum\limits_{i=1}^n (x_i-\bar{x})(y_i-\bar{y})} {\sqrt{\sum\limits_{i=1}^n (x_i-\bar{x})^2 \sum\limits_{i=1}^n (y_i-\bar{y})^2}},

where x and y are the sample means of X and Y, and sx and sy are the sample standard deviations of X and Y.

This table gives us the value of R, R Square and adjusted R square which helps in determining the relation between the dependent and independent variables.Model Summary

Model

R

R Square

Std. Error of the Estimate

1

.805a

.648

.472

20.12243

a. Predictors: (Constant), Industrial production growth, Unemployment Rate, Inflation, GDP

## R, R Square, Adjusted R Square

R is a measure of the correlation between the observed value and the predicted value of the dependent variable.

R Square (R2) is the square of this measure of correlation and indicates the proportion of the variance in the dependent variable which is accounted for by the model. In essence, this is a measure of how good a prediction of the dependent variable we can make by knowing the independent variables. This is an overall measure of the strength of association and does not reflect the extent to which any particular independent variable is associated with the dependent variable.

However, R square tends to somewhat over-estimate the success of the model when applied to the real world, so an Adjusted R Square value is calculated which takes into account the number of variables in the model and the number of observations (participants) our model is based on. This Adjusted R Square value gives the most useful measure of the success of our model. So in our case, for example we have an Adjusted R Square value of 0.472 we can say that our model has accounted for 47% of the variance in the dependent variable.

Standard Error of the Estimate (SEE) is a measure of the accuracy of the regression predictions. It estimates the variation of the dependent variable values around the regression line. It should get smaller as we add more independent variables, if they predict well.

http://cs.gmu.edu/cne/modules/dau/stat/regression/multregsn/see.gif

Analyzing the above Model Summary box we can conclude the following:

The correlation between the observed and predicted value of the dependent variable is 80.5% because value of R=0.805.

To measure the overall strength of the model and to see how well the independent variables are associated with the dependent variable we see the value of R2 which is 0.648. Therefore, we can say combined effect of the independent variables on dependent variable is 64.8%.

Adjusted R square takes into consideration the number of independent variables and sample space so from the above adjusted R square value we can conclude that our model has accounted for 47.2% of the variance in the dependent variable.

## ANOVAb

Model

Sum of Squares

Df

Mean Square

F

Sig.

1

Regression

5962.112

4

1490.528

3.681

.055a

Residual

3239.297

8

404.912

Total

9201.409

12

a. Predictors: (Constant), Industrial Production Growth, Unemployment Rate, Inflation, GDP

b. Dependent Variable: Oil prices

This table helps us in determining the overall significance of the model. It doesn't give much information about the success of the model helps in deciding whether to accept or reject the null hypothesis. This table reports an ANOVA, which assesses the overall significance of our model. As p < 0.05 our model is significant

Model

Unstandardized Coefficients

Standardized Coefficients

B

Std. Error

Beta

t

Sig.

1

(Constant)

151.268

45.924

GDP

10.102

3.5

0.714

2.886

0.02

Inflation

-5.872

2.424

-0.578

-2.422

0.042

Unemployment Rate

-6.807

3.245

-0.46

-2.098

0.069

Industrial production growth

-11.343

4.874

-0.621

-2.327

0.048

The Standardized Beta Coefficients give a measure of the contribution of each variable to the model. A large value indicates that a unit change in this independent variable has a large effect on the dependent variable. The t and Sig (p) values give a rough indication of the impact of

each independent variable - a big absolute t value and small p value suggests that a predictor variable is having a large impact on the criterion variable.

## Beta (standardized regression coefficients)

The beta value is a measure of how strongly each independent variable influences the dependent variable. The beta is measured in units of standard deviation. For example, a beta value of 2.5 indicates that a change of one standard deviation in the independent variable will result in a change of 2.5 standard deviations in the dependent variable. Thus, the higher the beta value the greater the impact of the independent variable on the dependent variable. When you have only one independent variable in your model, then beta is equivalent to the correlation coefficient between the independent and the dependent variable. This equivalence makes sense, as this situation is a correlation between two variables. When you have more than one independent variable, you cannot compare the contribution of each independent variable by simply comparing the correlation coefficients. The beta regression coefficient is computed to allow you to make such comparisons and to assess the strength of the relationship between each independent variable to the dependent variable.

These are the standardized coefficients. These are the coefficients that you would obtain if you standardized all of the variables in the regression, including the dependent and all of the independent variables, and ran the regression. By standardizing the variables before running the regression, you have put all of the variables on the same scale, and you can compare the magnitude of the coefficients to see which one has more of an effect. You will also notice that the larger betas are associated with the larger t-values and lower p-values.

## This table helps in finding the regression equation or the coefficients of independent variables.

Constant is the intercept of the equation.

Therefore, the equation with unstandardised coefficients is:

Oil prices= 151.268+10.102*GDP-5.872*inflation-6.807*unemployment rate-11.343*industrial production growth

Equation with standardized equation is as follows:

Oil prices=0.714*GDP-0578*inflation-0.46*unemployment rate-0.621*industrial production growth