Use of CLRM and its Assumptions to identify Factors affecting Net Profit of ONGC

1. Analysis of Factors affecting
Net Profit of ONGC
Submitted By => KARAN SHAH
Enrollment No. => 1011517039
Date => 06/09/2016
Submitted To => Dr. Abhay Raja

2. Objective of the Study
 The objective of this study is to
analyze the impact of Sales, Interest
Exp. and Average Oil Prices
(USD/barrel) on the Quarterly net
profits of ONGC. Also, to study
whether each quarter significantly
impacts the Net Profit of the company.

3. Identifying Dependent and
Independent Variable
 Dependent Variable: - Net Profit
after Tax of ONGC for last 15 Quarters
 Independent Variables:- Sales, Interest
Exp. and Average Oil Prices
(USD/barrel)
 Dummy Variables:- Q1, Q2, Q3 and Q4

5. Dependent Variable: NET_PROFIT
Method: Least Squares
Date: 09/05/16 Time: 21:59
Sample: 6/01/2001 3/01/2016
Included observations: 60
Variable Coefficient Std. Error t-Statistic Prob.
C 223.3362 513.3234 0.435079 0.6652
SALES 0.177025 0.046482 3.808433 0.0003
INTEREST_EXP_ -0.618125 6.693777 -0.092343 0.9268
OIL_PRICES_$_BBL_ 16.43866 7.052968 2.330744 0.0234
R-squared 0.643520 Mean dependent var 3964.752
Adjusted R-squared 0.624423 S.D. dependent var 1646.938
S.E. of regression 1009.314 Akaike info criterion 16.73627
Sum squared resid 57048076 Schwarz criterion 16.87589
Log likelihood -498.0881 Hannan-Quinn criter. 16.79088
F-statistic 33.69724 Durbin-Watson stat 1.854935
Prob(F-statistic) 0.000000

6. 

7. Dependent Variable: NET_PROFIT
Method: Least Squares
Date: 09/05/16 Time: 22:02
Sample: 6/01/2001 3/01/2016
Included observations: 60
Variable Coefficient Std. Error t-Statistic Prob.
C 195.6652 413.1428 0.473602 0.6376
SALES 0.178715 0.042355 4.219452 0.0001
OIL_PRICES_$_BBL_ 16.34077 6.911926 2.364141 0.0215
R-squared 0.643466 Mean dependent var 3964.752
Adjusted R-squared 0.630956 S.D. dependent var 1646.938
S.E. of regression 1000.498 Akaike info criterion 16.70309
Sum squared resid 57056763 Schwarz criterion 16.80781
Log likelihood -498.0927 Hannan-Quinn criter. 16.74405
F-statistic 51.43629 Durbin-Watson stat 1.850975
Prob(F-statistic) 0.000000
 As we can see the significance levels of Sales and Avg.
Oil Prices (USD/Barrel) have changed but still they
significantly impact the Net Profit of ONGC.

8. Regression Output after adding
Dummy Variables
 As we can see that even after adding dummy
variables “Interest Exp.” is not significant
Dependent Variable: NET_PROFIT
Method: Least Squares
Date: 09/05/16 Time: 22:08
Sample: 6/01/2001 3/01/2016
Included observations: 60
Variable Coefficient Std. Error t-Statistic Prob.
C 247.1278 514.1621 0.480642 0.6327
SALES 0.172291 0.043671 3.945242 0.0002
INTEREST_EXP_ 0.511423 6.353854 0.080490 0.9362
OIL_PRICES_$_BBL_ 17.15152 6.628872 2.587397 0.0124
DUMMY_QRT_1 -631.7632 345.4176 -1.828984 0.0730
DUMMY_QRT_2 11.05170 347.8075 0.031775 0.9748
DUMMY_QRT_3 552.5482 346.0343 1.596802 0.1163
R-squared 0.709052 Mean dependent var 3964.752
Adjusted R-squared 0.676114 S.D. dependent var 1646.938
S.E. of regression 937.2884 Akaike info criterion 16.63314
Sum squared resid 46561007 Schwarz criterion 16.87748
Log likelihood -491.9942 Hannan-Quinn criter. 16.72871
F-statistic 21.52714 Durbin-Watson stat 1.800765
Prob(F-statistic) 0.000000

9. Output Without Int. Exp.
Dependent Variable: NET_PROFIT
Method: Least Squares
Date: 09/05/16 Time: 22:12
Sample: 6/01/2001 3/01/2016
Included observations: 60
Variable Coefficient Std. Error t-Statistic Prob.
C 269.5238 428.3717 0.629182 0.5319
SALES 0.170908 0.039777 4.296679 0.0001
OIL_PRICES_$_BBL_ 17.22663 6.502214 2.649348 0.0106
DUMMY_QRT_1 -628.6620 340.0897 -1.848518 0.0700
DUMMY_QRT_2 8.697621 343.3726 0.025330 0.9799
DUMMY_QRT_3 554.4691 342.0199 1.621160 0.1108
R-squared 0.709016 Mean dependent var 3964.752
Adjusted R-squared 0.682073 S.D. dependent var 1646.938
S.E. of regression 928.6260 Akaike info criterion 16.59993
Sum squared resid 46566698 Schwarz criterion 16.80936
Log likelihood -491.9979 Hannan-Quinn criter. 16.68185
F-statistic 26.31544 Durbin-Watson stat 1.805818
Prob(F-statistic) 0.000000

10. Interpretation of the Output
 As we can see the probability values of all the dummy variables are
greater than 0.05, which means all the dummy variables are
insignificant at 95% confidence level.
 Hence, we can say that with the change in quarter of a year, the net
profit of ONGC does not get impacted significantly or else we can say
that net profit is not dependent on the quarter of a year.
 Also, with the addition of dummy variables the other independent
variables (Sales and Oil Prices) remain significant.

11. Regression Equation
Net Profit = 269.52+0.1709(Sales) +
17.2266(Oil Prices
USD/bbl) -
628.662(Q1Profit) + 8.697
(Q2 Profit) + 4.4691(Q3 Profit)

13. 1.


14. 


15. Dependent Variable: NET_PROFIT
Method: Least Squares
Date: 09/05/16 Time: 22:43
Sample: 6/01/2001 3/01/2016
Included observations: 60
Weighting series: SALES
Weight type: Variance (average scaling)
White heteroskedasticity-consistent standard errors & covariance
Variable Coefficient Std. Error t-Statistic Prob.
C 236.5418 244.4042 0.967830 0.3374
SALES 0.184491 0.051022 3.615944 0.0007
OIL_PRICES_$_BBL_ 14.87050 8.996688 1.652886 0.1042
DUMMY_QRT_1 -525.0760 264.7367 -1.983389 0.0524
DUMMY_QRT_2 -44.10333 248.3946 -0.177554 0.8597
DUMMY_QRT_3 453.8547 192.3781 2.359180 0.0220
Weighted Statistics
R-squared 0.768712 Mean dependent var 3627.495
Adjusted R-squared 0.747296 S.D. dependent var 1066.254
S.E. of regression 806.7917 Akaike info criterion 16.31865
Sum squared resid 35149297 Schwarz criterion 16.52808
Log likelihood -483.5594 Hannan-Quinn criter. 16.40057
F-statistic 35.89493 Durbin-Watson stat 1.770084
Prob(F-statistic) 0.000000 Weighted mean dep. 3426.924
Wald F-statistic 49.35065 Prob(Wald F-statistic) 0.000000
Unweighted Statistics
R-squared 0.706290 Mean dependent var 3964.752
Adjusted R-squared 0.679095 S.D. dependent var 1646.938
S.E. of regression 932.9654 Sum squared resid 47002915
Durbin-Watson stat 1.778441

16. 2. AUTO CORRELATION

17. Interpretation
 All the prob. levels are greater than
0.05, hence we accept the null
hypothesis that “there is no auto
correlation between the observations”.
Hence, this assumption of CLRM is
fulfilled.

18. 3. TEST OF NORMALITY
0
2
4
6
8
10
-3000 -2000 -1000 0 1000 2000
Series: Residuals
Sample 6/01/2001 3/01/2016
Observations 60
Mean -3.52e-13
Median -8.215070
Maximum 2216.133
Minimum -2766.761
Std. Dev. 888.4065
Skewness -0.171539
Kurtosis 3.818639
Jarque-Bera 1.969682
Probability 0.373499

19. Interpretation
 To test the normality of the data, we use Jarque –
Bera test. Here, the prob. of Jarque Bera is
0.3734, which is greater than 0.05, hence we
reject the null hypothesis that “data is not normal”.
 The Kurtosis Value is 3.8186, which is marginally
greater than 3, hence we can safely assume that
data is normal. Thus, this assumption of CLRM

20. 4. Test of Multicollinearity
Variance Inflation Factors
Date: 09/05/16 Time: 23:09
Sample: 6/01/2001 3/01/2016
Included observations: 60
Coefficient Uncentered Centered
Variable Variance VIF VIF
C 183502.3 12.76765 NA
SALES 0.001582 27.34730 2.693477
OIL_PRICES_$_BBL_ 42.27879 15.90254 2.702630
DUMMY_QRT_1 115661.0 2.011854 1.508891
DUMMY_QRT_2 117904.7 2.050882 1.538162
DUMMY_QRT_3 116977.6 2.034756 1.526067

21. Interpretation
 As we have removed the “Interest Exp.” variable because its
insignificance came up in the first part of our analysis, running
Multicollinearity test by including it would be useless.
 From, the above analysis we see that there is a 78.73% linear
relationship between the two variables, but the Centered VIF of Sales
and Oil Prices is 2.693 and 2.702 resp. so we can safely say that the
Multicollinearity can be ignored between the two variables. Also,
logically we can’t remove either of the variables because both of
them are significant in predicting the net profit of the company.

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