Data Presentation and Hypotheses Testing for Banks in Nigeria

4.1: Data Presentation
The employed predictor and criterion variables of banks in Nigeria (2009 to 2018) are
presented as follows
Descriptive
This section further proceeds to evaluate the underlying trend of employed data in this subsection
based on various attributes of the employed data.
EQ BDS FML OWS
Mean 0.383863 4.292308 5.696532 6.000000
Median 0.259326 4.000000 5.437776 6.000000
Maximum 8.735455 7.000000 38.99508 8.000000
Minimum -21.18190 1.000000 -7.219780 4.000000
Std. Dev. 2.338341 1.123713 6.237580 0.373544
Skewness -5.735753 0.131125 2.901713 -0.895806
Kurtosis 58.71467 3.476544 15.27821 21.66667
Jarque-Bera 17526.81 1.602624 999.0180 1904.794
Probability 0.000000 0.448740 0.000000 0.000000
Sum 49.90219 558.0000 740.5492 780.0000
Sum Sq. Dev. 705.3513 162.8923 5019.056 18.00000
Observations 130 130 130 130

-30
-20
-10
0
10
20
30
40
10 20 30 40 50 60 70 80 90 100 110 120 130
EQ BDS FML OW S
Stationarity Unit Root Test
Table 4.2 below shows the ADF stationarity unit root tests output of variables in this study via
E-View version 9.
Table 4.2: ADF Stationarity Unit Root Test Extract
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(EQ)
Method: Least Squares
Date: 09/16/21 Time: 15:21
Sample (adjusted): 4 130
Included observations: 127 after adjustments
Variable CoefficientStd. Error t-Statistic Prob.
EQ(-1) -0.642151 0.169940 -3.778693 0.0002
D(EQ(-1)) -0.558856 0.146255 -3.821102 0.0002
D(EQ(-2)) -0.223216 0.093420 -2.389374 0.0184
R-squared 0.648082 Mean dependent var 0.041496
Adjusted R-squared 0.642406 S.D. dependent var 3.712740
S.E. of regression 2.220187 Akaike info criterion 4.456398
Sum squared resid 611.2244 Schwarz criterion 4.523584
Log likelihood -279.9813 Hannan-Quinn criter. 4.483695
Durbin-Watson stat1.996212

-25
-20
-15
-10
-5
0
5
10
10 20 30 40 50 60 70 80 90 100 110 120 130
EQ
The table above indicates the result of Stationarity using Augmented Dickey Fuller (ADF) unit
root test. The results revealed that earnings quality became stationary at the zero difference (0)
with (ADF t-statistic value of --3.778693 with the critical value of 0.0002 at 5% level),
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(BDS)
Method: Least Squares
Date: 09/16/21 Time: 15:49
Sample (adjusted): 5 130
Included observations: 126 after adjustments
Variable CoefficientStd. Error t-Statistic Prob.
BDS(-1) -0.021778 0.022735 -0.957896 0.3400
D(BDS(-1)) -0.593758 0.088848 -6.682815 0.0000
D(BDS(-2)) -0.327113 0.098565 -3.318767 0.0012
D(BDS(-3)) -0.220759 0.087095 -2.534679 0.0125
R-squared 0.293893 Mean dependent var -0.015873
Adjusted R-squared 0.276530 S.D. dependent var 1.308337
S.E. of regression 1.112833 Akaike info criterion 3.082926
Sum squared resid 151.0844 Schwarz criterion 3.172967
Log likelihood -190.2243 Hannan-Quinn criter. 3.119507

Durbin-Watson stat2.013564
0
1
2
3
4
5
6
7
8
10 20 30 40 50 60 70 80 90 100 110 120 130
BDS
The table above indicates the result of Stationarity using Augmented Dickey Fuller (ADF) unit
root test. The results revealed that board size became non-stationary at the zero difference (0)
with (ADF t-statistic value of -0.957896 with the critical value of 0.3400 at 5% level),
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(OWS)
Method: Least Squares
Date: 09/16/21 Time: 15:51
Sample (adjusted): 5 130
Included observations: 126 after adjustments
Variable CoefficientStd. Error t-Statistic Prob.
OWS(-1) 0.001775 0.005452 0.325488 0.7454
D(OWS(-1)) -0.841871 0.079249 -10.62315 0.0000
D(OWS(-2)) -0.536099 0.095071 -5.638953 0.0000
D(OWS(-3)) -0.223284 0.079149 -2.821048 0.0056
R-squared 0.484842 Mean dependent var 0.015873
Adjusted R-squared 0.472174 S.D. dependent var 0.505713
S.E. of regression 0.367409 Akaike info criterion 0.866550
Sum squared resid 16.46871 Schwarz criterion 0.956590
Log likelihood -50.59263 Hannan-Quinn criter. 0.903130
Durbin-Watson stat1.987937