Data test: If the P-value is high than 0.05

Data Analysis
and results:

This
chapter explains the empirical results of the study including the:

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·        
Diagnostic
tests

·        
Identification
of Multicolinearity

·        
Descriptive
statistics

·        
Selections
and explanation of the selected model results

Now
that the data under consideration in the study is panel data; where the same
entities as in this case Afghan/Pakistani banks, are observed in time, and for
the purpose of estimation there are several methods used in panel data. The
most general and frequently used models used in the panel data are as follow:

·        
Random
effect model

·        
Fixed
effect model

·        
Pooled
effect model (rarely used)

Now
in-order to see which model will work as best estimator and give consistent
results and will be more appropriate for the study, different tests have been
taken into account as:

·        
Diagnostic
test i.e. Chow test: If the P-value is high than 0.05 then this will indicate that
the null hypothesis (H0) should be rejected, where;

 H0 in the Chow
test is that the Pooled OLS is preferred over the fixed effect due to the
higher efficiency and explanatory power. 

·        
Breusch-Pagan
test: If the P-value is high than 0.05 then this will indicate that the null
hypothesis (H0) should be rejected where

 H0 in the Breusch-Pagan
test is that the Pooled OLS is preferred over the Random effect due to the
higher efficiency and explanatory power. 

·        
Haushman
test: If the P-value is high than 0.05 then this will indicate that the hull
hypothesis (H0) should be rejected where;

 H0 in the
Hausman test is that the Random effect is preferred over the fixed effect due
to the higher efficiency and explanatory power. 

Have been used in the study and
based on them appropriate model have been considered accordingly.

First the model selection and
results of the given model of Afghanistan banks will be discussed and then on
the same manner of Pakistani banks.

 

Afghanistan banks:

1.      Diagnostic tests for
Model selection

Diagnostics test

Null hypothesis

P value

Recommended Model

Chow Test

Pooled is Better than Fixed Effects

0.0001

Fixed Regression Model

Breusch-Pagan

Pooled is Better than Random Effects

0.0592

Pooled Regression Model

Hausman Test

Random Effects is better than Fixed Effects

0.0018

Fixed Effects Model

 

Based on the results of the above
diagnostic tests, Breusch-Pagan tests fails to reject null hypothesis that
Pooled is better than Fixed Effect, so recommends Pooled OLS while two other tests,
namely Chow test and Hausman test recommend that Fixed regression model will be
more appropriate and give consistent result for the study, as they both rejected
their respective null hypothesis. Based on the results of the above diagnostic
test and model selection, the model under consideration and used is Fixed
effect model for the study of determinants of bank’s profitability in Afghanistan.

 

 

 

 

 

 

 

 

2.     
Variance Inflating Factor (VIF) Test:

Table ***
VIF

Variable

VIF

1/VIF

 

LN_TA

1.621

0.617

EQ_TA

1.655

0.604

CD_TA

2.775

0.360

 

CCE_TA

1.809

0.553

 

Ln_TA

2.579

0.388

 

Multicollinearity; correlation
within linear combinations of independent variables, is a situation where a
number of independent variables in a multiple regression model are
closely/highly (but not perfectly) correlated with each other.

 

Effects of multicollinearity:

·        
Multicollinearity
doesn’t affect the consistency of the model estimates and regression
coefficients, but makes them inaccurate and unreliable.

·        
It
becomes difficult to isolate to isolate the impact of each independent variable
on the dependent variable

·        
The
standard error for the regression coefficients are inflated which results in
t-stats becoming too small and less powerful in terms of their ability to reject
null hypothesis.

 Multicollinearity can lead to skewed (positive
or negative) or misleading results when a researcher or analyst is attempting to
determine how well each one of the individual independent variable can most
effectively be utilized to predict or understand the dependent variable in a
statistical model. In general, multicollinearity can lead to
wider confidence intervals and less reliable probability values (P
values) for the independent variables.

So
in order to make sure that our variables don’t have the issue of
multicollinearity the calculated values of 
VIF should be greater than 1 and that of 1/VIF be greater than 0.1. As it
can be observed in the VIF table the calculated values in the table above
values for LN_TA, EQ_TA, CD_TA, CCE_TA, Ln_TA are all higher than 1 and their
1/VIF values higher than 0.1 in the same manner, so we can conclude that there
is no close/high correlation among the independent variables used in the model
and the results are consistent, accurate and reliable.

3.     
Results and discussion:

 

·        
Model:
Fixed-effects, using 56 observations where the data taken into account is for 8
banks in Afghanistan, out of population of 12, over 7 years

·        
Included
8 cross-sectional units, sample of 8 Commercial banks in Afghanistan

·        
Time-series
length = 7 (2010-2016)

·        
Dependent
variable: ROA_ where ROA is used as proxy for bank profitability in
Afghanistan.

Table
***       Fixed Effect Model

ROA

Coefficient

Robust
Std. Error

T-Value

P-Value

Const.

0.00934

0.01314

0.711

0.481

LN_TA

0.0297949

0.0253057

1.177

0.2455

EQ_TA

0.0636793

0.0173056

3.680

0.0006  **

CD_TA

0.0439047

0.0219900

2.997

0.0422  *

CCE_TA

0.00423444

0.0296916

0.1426

0.8873

Ln_TA

0.00251001

0.00126129

-.990

0.0430  *

Observations

F(
12, 43)

Prob
> F

R-squared

Adjusted
R-sq

56

5.403771

0.0000

0.6013

0.49001

 

Regression
equation:

The regression equation
obtained for the fixed effect model after the analyses of the above data is as
follows:

Y=
? + ?1 (LN_TA) + ?2
(EQ_TA) + ?3 (CD_TA)
+ ?4 (CCE_TA) + ?5 (Ln_TA)
+ ?

Where:

·        
Y=
ROA (dependent variable) proxy used for bank profitability

·        
LN_TA=
Loan/Total assets

·        
EQ_TA=Equity
Capital/Total Assets

·        
CD_TA=Customer
deposits/Total assets

·        
CCE_TA=Cash
and Cash Equivalent/Total Assets

·        
Ln_TA=
natural Log of total assets the proxy for bank size

 

Coefficients:

The
coefficients (the ?s) show the nature of relationship and the extent of change caused
in the dependent variable (in this case the ROA) by the independent variables
(in this case five independent variables used). In the coefficients table the ?
shows the amount of percentage change and the sign shows the positive or
negative impact on the dependent variable. The table shows that if all the
independent variables are zero still Afghan banks will be able to be somehow
profitable where profitability of a certain bank will be around 0.00934 units
for each unit of assets deployed in the given bank. Further it explains that
all the variables i.e. capital adequacy, size, deposits, liquidity and asset
composition, have positive impact on the overall profitability of Afghanistan
banks.

F-
Test (Test of over-all model significance):

F
test is a part of ANOVA table which shows the significance of the over-all
model. Now the decision rules based on F-test are as follow:

·        
If the F-test calculated
value is more than the F test tabulated value i.e. Fcal  > 
Ftab, then we can conclude that the over-all model is significant

·        
If the F-test
calculated value is lower than the F-test tabulated value i.e.  Ftab > 
Fcal, then we can conclude that the over-all model is insignificant

Now
based on the above ANOVA table, calculated value for the F-test is 5.40 which
is greater than 4 (tabulated value), therefore we can concluded that the model
as whole is significant. The p-value also approves this fact as its value is
less than 0.05 i.e. (0.0000) F

R-squared

Adjusted
R-sq

84

17.45435

0.0000

0.806509

0.760302

 

Regression
equation for Pakistan banks:

The regression equation
obtained for the fixed effect model after the analyses of the above data is as
follows:

Y=
? + ?1 (LN_TA) + ?2
(EQ_TA) + ?3 (CD_TA)
+ ?4 (CCE_TA) + ?5 (Ln_TA)
+ ?

Where:

·        
Y=
ROA (dependent variable) proxy used for bank profitability

·        
LN_TA=
Loan/Total assets

·        
EQ_TA=Equity
Capital/Total Assets

·        
CD_TA=Customer
deposits/Total assets

·        
CCE_TA=Cash
and Cash Equivalent/Total Assets

·        
Ln_TA=
natural Log of total assets (proxy for bank size)

 

Coefficients:

The
table shows that if all the independent variables are zero still Pakistani
banks will be able to be somehow profitable where profitability of a certain
bank will be around 0.00500134 units for each unit of assets deployed in the
given bank.

 Further it explains that two variables namely
capital adequacy and deposits have positive impact while rest three variable;
liquidity, bank size and asset composition, have negative impact on the overall
profitability of Pakistani banks.

F-
Test (Test of over-all model significance):

Now
based on the above ANOVA table, calculated value for the F-test is 17.45435   which is greater than 4 (tabulated value),
therefore we can concluded that the model as whole is significant. The p-value
also approves this fact as its value is less than 0.05 i.e. (0.0000)

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