the macroeconomic determinants of profitability among...

Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)

An Online International Research Journal (ISSN: 2306-367X)

2015 Vol: 4 Issue 1

1414

www.globalbizresearch.org

The Macroeconomic Determinants of Profitability among

Commercial Banks in Namibia

Johannes Peyavali Sheefeni Sheefeni

Department of Accounting, Economics and Finance,

Polytechnic of Namibia,

Windhoek, Namibia.

Email: [email protected]

____________________________________________________________________________

Abstract

This paper analyses the macroeconomic determinants for commercial bank’s profitability in

Namibia. The study employed the techniques of unit root, cointegration, and impulse response

functions, and forecast error variance decomposition on the quarterly data covering the

period 2001 to 2014. The results reveal that the variables gross domestic product, inflation

rate and interest rate do not significantly influence commercial bank’s profitability in

Namibia. This suggests that the macroeconomic environment does not play a role in

influencing the profitability of the commercial banks. However, the results should be

interpreted with caution given the fact that aggregated data was used.

____________________________________________________________________________

Keywords: macroeconomic determinants, commercial bank’s profitability, Namibia, unit root,

cointegration, impulse response functions.



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1. Introduction

The role of financial intermediaries is one of the most important because of the financial

functions which they perform in an economy. The key functions ranges from providing a

payment mechanism, matching supply and demand in financial markets, dealing with complex

financial instruments and markets, provide markets transparency, perform risk transfer and risk

management functions and many more (Alper and Anbar, 2011).

Commercial banks are the most important financial intermediaries because of their ability

to offer and provide a range and bundle of different services. Particularly, commercial banks

provide various services for lenders (depositors) and borrowers. This includes providing

liquidity and safekeeping for savings, which allows depositors to smoothen consumption over

time. Furthermore, commercial banks conduct credit analysis, disburse loans and monitor

outstanding credits for borrowers who require more financing than they can raise from their

internal sources or alternative sources such as financial markets (Berger and Humphrey, 1993).

Chirwa and Mlachilla (2004) put it differently by stating that banks act as financial

intermediaries by transforming deposits into financial assets and channel funds from entities

with surplus liquidity to those with deficit liquidity, thus, they facilitate capital formation and

trade. In addition, banks also perform the function of screening borrowers and monitoring

their activities in financial system characterized by incomplete and asymmetric information.

However, the role of commercial banks goes beyond the intermediation function in that good

financial performance rewards the shareholders for their investment. This, in turn, encourages

additional investment and brings about economic growth while, poor banking performance can

lead to banking failure and crisis which have negative repercussions on the economic growth.

Therefore, of greater importance is their role of financing economic activity in most

economies (Ongore and Kusa, 2013 and Soyemi, Akinpelu and Agunleye, 2013).

According to Weersainghe and Perera (2013), banking sector all around the world has

experienced some profound changes due to innovations in technology as well globalization

which creates both opportunities for growth and challenges for banking industry to remain

profitable in this increasingly competitive environment. Consequently, bank performance has

serious effects on investment, firm growth, industrial expansion and economic development.

In this regard, profitability is essential for a bank to maintain the activity and for its

shareholders to obtain deserving returns. Therefore, the determinants of bank performance,

particularly, the profitability of the banking sector has attracted the interest of academic

research as well as of bank management, financial markets and bank supervisors/regulators.

The Namibian banking industry is characterized by an oligopolistic market structure in

which a few institutions dominate the industry (Andongo and Stork, 2005). Market structure in



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which few large firms have a large market share is believed to have a positive impact on

corporate profit. Profitability of the banks has been on the increase as reported in the Bank of

Namibia annual reports of 2012 and 2013 respectively. Furthermore, the special examination

of the banking sector better known as the stress-test shows that the banking sector is

performing well as reported in the Bank of Namibia financial stability reports of 2012.

However, in a country where the financial sector is dominated by a few large commercial

banks, any failure in the sector has enormous potential impact on the economy. This is due to

the fact that any bankruptcy in the sector has a potential contagion effect that can lead to bank

runs, crises and bring overall financial crisis and economic tribulations. This study draws its

primary interests from this in that its objective is to examine the macroeconomic determinants

of commercial banks profitability in Namibia.

The paper is organized as follows: the next section presents a literature review. Section 3

discusses the methodology. The empirical analysis and results are presented in section 4.

Section 5 concludes the study.

2. Literature Review

2.1 Theoretical Literature

The external factors that can affect the bank’s performance are usually beyond the control

of company. This is due to the fact that these factors are sector-wide or country-wide (Ongore

and Kusa 2013). According to Karkrah and Ameyaw (2010) macroeconomic variables form a

larger part of the external profit determinants in most studies conducted. The most common

external factors that have been identified includes among others competition/market share/firm

size, inflation, GDP growth, and interest rate (Haron, 2004). Weersainghe and Perera (2013),

Ongore and Kusa (2013) among others discussed the various external factors that impact bank

profitability as outlined below.

Competition or (Market share/market growth rate) is one of the factors in the sense that

each industry has competition forces that are peculiar to itself and these forces do affect

profitability or sometimes viewed as drivers of profitability. The same applies to the banking

industries across the globe. Accordingly firms which operates in a highly competitive

industries hardly earns favorable returns on their investment, hence the preceding argument

supports the influence of competition on profit. In fact Smith (1984) argued that intensive

competition within commercial banking industry tends to decrease profits. Particularly, in

microeconomic theory it is argued that an industry dominated by few larger firms tend to give

rise to fierce competition for higher market share and this in turn affects the profitability of an

industry.



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Another macroeconomic factor that affects bank’s profitability is inflation rate (Rovell,

1979). His argument takes a view that the effect of inflation on bank profitability depends on

the rate at which the bank’s wages and other operating expenses increase compared to

inflation. This usually depends on accuracy of the prediction of the future inflation which

enable banks manage their operating costs. Perry (1992) supports this argument by stating that

when inflation expectations are fully anticipated by the bank management, it provides room

for interest rate adjustment in order to accelerate increase in revenues faster than the costs and

subsequently, higher economic profits. On the contrary, Rasiah (2010) argues that the move by

central banks in their quest to control inflation result in increased cost of borrowing as well as

a fall in credit-creating capacity and subsequently the loans given to the commercial banks.

This trickles down to a decline in the loans given by commercial bank. Furthermore, inflation

has an adverse effect on commercial bank’s profitability as it erodes the real value of bank’s

assets relative to their liabilities, hence it affects profits.

Gross domestic product has also been identified as another factor. According to Ongore

and Kusa (2013), trend of GDP affects the demand for banks asset in the sense that when

trends are leaning towards a declining GDP growth, demand for credit falls which in turn

negatively affect the profitability of banks. On the contrary, when trends are leaning towards a

growing economy or a positive GDP growth results in high demand for credit as a

consequence of the nature of business cycle. Therefore, during boom the demand for credit is

relatively high compared to recession periods. Another view on the relationship between GDP

and commercial bank’s profitability is that by Vong and Hoi (2009) who assert that there is a

general perception that loan defaults are normally lower in times of favorable economic

growth, while higher during unfavorable economic growth and these developments do affect

the profits of the commercial banks in either direction depending on the circumstances.

As for the interest rate, the relationship between the interest rate and bank profits is said to

be positive (Vejzagic and Zarafa, 2014). In general, the variable interest rate has been cited in

most studies as profitability determinant of commercial banks. This is on the basis that net

interest income which is a difference between interest income and interest expenses largely

affects the commercial bank’s profits.

2.2 Empirical Literature

There are numerous studies that have empirically investigated the various macroeconomic

determinants of profitability among commercial banks. Below is a list of few selected

empirical studies on the abovementioned subject.

Demirguc – Kunt and Huizinga (2000) examined the impact of financial development and

structure on bank performance using bank level data of large number (80) of developed and



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developing countries for the period 1990 - 1997. The results show evidence that

macroeconomic and regulatory conditions have pronounced impact on margins and

profitability, while shallow market consumer and larger bank did not appear to exercise

market power in order to achieve high profitability performance. Furthermore, both scale and

technical efficiency appear to be dominant determinants of profitability.

Afanasieff, Lhacer and Nakane (2002) examined the determinants of banks interest spreads

using macro and micro variables in Brazil. A panel data technique in particular a two-step

approach was employed to uncover the main determinants of the bank interest spreads in

Brazil. Monthly data for the period February 1997 to November 2000 were used in the study.

The study reveals that macroeconomic variables have the most impact on bank interest spread

in Brazil.

In Tunisia, Naceur (2003) investigated the impact of banks characteristics, final structure

and macroeconomic indicators on banks net interest margin and profitability for the 1983-

2000 period. Panel data techniques were used particularly both fixed effects and random

effects models were estimated. The findings of the study are that inflation and growth rates

have negative impact while stock market development has positive impact on profitability and

net interest margin.

Athanasoglou, Delis and Stakouras (2006) analysed the effect of selected set of

determinants on banks profitability in the South Eastern European region over the period

1998-2002. Using an unbalanced panel dataset, the study reveals that inflation has a strong

effect on profitability, while bank’s profits are not significantly affected by real GDP per

capita fluctuations.

In Turkey, Tunay and Silpar (2006) investigated profitability for the period of 1988-2004,

employing panel modelling technique. The study reveals that national income have positive

impact on ROE. However, Yıldırım (2008) employed multiple regression method in analysing

profitability of Turkish banking sector for the period 2002 to 2007. The findings are that there

is a positive relationship between bank profitability and industrial production index, whereas

there is a negative relationship between bank profitability and consumer price inflation

In Switzerland, Detrich and Wanzennied (2009) investigated the determinants of the

profitability of commercial banks using data of 453 banks from 1997 to 2006. In employing

panel data approach, the results from the study show that macroeconomic factors, GDP growth

variable has a positive effect on bank profitability, while the effect of tax rate and market

concentration rate has a significant negative effect on bank profitability.

Sayilgan and Yildirim (2009) investigates the relationship between the return on assets and

the return on equity ratio for a sample of Turkish banks for the 2002-2007 time period using



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monthly data. The profitability of the banking sector seems to have increased along with

declining inflation rate, consistently increasing industrial production index and improving

budget balance.

Alper and Anbar (2011) study looked at the bank-specific and macroeconomic

determinants of the bank’s profitability in Turkey over the time period from 2002 to 2010. The

bank profitability is measured by return on assets (ROA) and return on equity (ROE) as a

function of bank-specific and macroeconomic determinants. Using a balanced panel data set,

the results show that with regard to macroeconomic variables, only the real interest rate affects

the performance of banks positively, suggesting that higher real interest rate can lead to

higher bank profitability.

Acarvaci and Calim (2013) analysed the bank specific and macroeconomic factors that

affect the profitability of commercial banks in Turkish banking sector by using Johansen and

Juselius cointegration test approach. Data for the period 1998 to 2011 from the three biggest

state-owned, privately-owned and foreign banks were used for analysis. The macroeconomic

determinants of study are real gross domestic product, inflation rate, real exchange rate and

real interest rate. The results on macroeconomic factors show that real gross domestic product

and real exchange rate have been effective on the profitability.

Azeez and Gamage (2013) examined the impact of bank specific, industry specific and

macro-economic variables on net interest margin of Sri Lankan commercial banks over the

period of 1999-2011 within the dealership framework of Ho and Saunders (1981). Of the

macroeconomic variables, it was found that inflation has a positive influence on profitability

while GDP growth has a negative influencing on profitability of the commercial banks.

In Kenya, Were and Wambua (2014) assessed the determinants of interest rate spread in

the banking sector based on panel data analysis. Regression analysis were conducted to

empirically investigate the determinants of interest rate spreads by employing panel data

estimation methodology on a panel of commercial banks using annual data for the period

2002–2011. The findings show that the impact of macroeconomic factors such as real

economic growth is insignificant. The effect of the monetary policy rate is positive but not

highly significant.

In Ghana, Antwi and Apau (2015) investigated the determinants of financial performance

of Rural and Community banks. Thirty (30) rural and community banks across the country

were purposefully selected for the period 2006-2010. Panel data was used in regression

analysis model to examine the variables that could affect the performance of RCBs. The

variables of the regression include credit risk, capital adequacy, portfolio composition, bank

size, operational efficiency, gross domestic product as well as inflation (consumer price



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index). Of the macroeconomic variables, the findings are that gross domestic product and

annual rate of inflation are significant drivers of RCBs’ profitability in Ghana. Similarly, GDP

is less significant factor in explaining the variation in the profitability of the banks and it is

inversely related to profitability per this study. Unlike GDP, inflation rate, in the economy

over the period seems to have impacted profitability in a positive way showing how well

managers in the sector are incorporating inflation in their price build-ups.

Petria, Capraru and Ihnatov (2015) assessed the main determinants of banks’ profitability

in EU27 over the period 2004-2011 using panel data approach. The study looked at bank-

specific (internal) factors and industry specific and macroeconomic (external) factors. The

empirical findings show that economic growth has influence on bank profitability, both on

ROAA and ROAE.

The results of the studies presented above vary significantly because of the differences in

the environment and data used in the analysis. However, there are common factors influencing

profitability as identified in several studies. Moreover, the findings from these studies range

from no relationship, positive relationship, negative relationship, significant impact,

insignificant impact etc. There are also different methodological approaches used in the

studies. However, there seem to be no study on Namibia that has specifically looked at this

subject. It is against this background this study intends to fill the gap and add to empirical

literature for Namibia.

3. Methodology

In analysing the long-run relationship between macroeconomic determinants and

profitability variables, this study adopted the model used by Acaravci and Calim (2013). In

this regard, time series econometric techniques such as unit root, cointegration and impulse

response function, and forecast error variance decompositions have been used within the

vector autoregression (VAR) framework. The process is outlined in the next subsection.

3.1 Econometric or Analytical Framework and Model Specification

VAR is a system of dynamic linear equations where all the variables in the system are

treated as endogenous. The reduced form of the system gives one equation for each variable,

which specifies each variable as a function of the lagged values of their own and all other

variables in the system. The vector autoregression process is described by a dynamic system

whose structural form equation is given by:

tptpttt ByyyAy .....2211 (1)



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Where, A is an invertible )( nn matrix describing contemporaneous relations among the

variables; ty is an )1( n vector of endogenous variables such that; ntttt yyyy ,...,,( 21

);

is a vector of constants; i is an )( nn matrix of coefficients of lagged endogenous

variables ),...,3,2,1( pi ; B is an )( nn matrix whose non-zero off-diagonal elements

allow for direct effects of some shocks on more than one endogenous variable in the system;

and t are uncorrelated or orthogonal white-noise structural disturbances i.e. the covariance

matrix of t is an identity matrix 1)',( ttE

. Equation (1) can be rewritten in compact

form as:

titt ByLAy )( (2)

Where, )(L is a )( nn finite order matrix polynomial in the lag operator .L

The standard practice is that the main uses of the VAR model are the impulse response

analysis and forecast error variance decomposition. The analysis is carried out in the following

order. The first step requires a test for the univariate characteristics of data. This is done by

using some formal testing namely, Augmented Dickey Fuller (ADF) test, the Phillips-Perrons

(PP) and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests are applied (Pindyck &

Rubinfeld, 1991; Gujarati, 1995; 2003). Thereafter, the following step would be to conduct

tests for co-integration, ie if two or more series have long-run equilibrium. The Johansen

cointegration test is used to determine the number of cointegration relations for forecasting

and hypothesis testing. If co-integration is found among the variables, the adjustment of the

short-run to the long-run equilibrium is obtained through the vector error correction model

(VECM) and if no cointegration found then the VAR is estimated. However, there are many

steps that must be followed before applying the Johansen test. First it is necessary to determine

the number of lags since this has a big effect in the analysis. There are five criteria:, the

sequential likelihood ratio (LR), Akaike information criterion (AIC), Schwarz information

criterion (SC), Final prediction error (FPE) and Hannan Quinn information criterion (HQ).

In empirical applications, the main use of the VAR is the impulse response function which

function traces the response of the endogenous variables to one standard deviation shock or

change to one of the disturbance terms in the system. Variance decomposition is an alternative

method to the impulse response functions for examining the effects of shocks to the dependent

variables. This technique determines how much of the forecast error variance for any variable

in a system, is explained by innovations to each explanatory variable, over a series of time

horizons (Stock & Watson, 2001:106).



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3.2 Data, Data Sources and Data Measurements

The data used in this paper are quarterly data for the period 2001:Q1 to 2014:Q2.

Secondary data were obtained from the Bank of Namibia’s various statutory publications,

Namibia Statistical Agency’s statutory publications and World Bank.

In analysing bank performance, three measures of profitability were used, Return on Assets

(ROA), Net Interest Margin (NIM) and Return on Equity (ROE). ROA is a ratio between net

income and total assets, NIM is defined as net interest income divided total asset. ROE is

defined as Profit before Taxation (PBT) divided by shareholders’ fund. The three above

mentioned variables are the regressand or dependent variables.

The regressors or explanatory variables used in this study are gross domestic product,

interest rate and inflation.

With respect to Gross domestic product (LNGDP), a positive relationship between bank

profitability and level of economic activities as expected during upswings as demand for

lending increases and negative relationship during downswings when demand for credit

facilities is expected to slow down. Consequently positive relationship between GDP growth

rate and bank profitability is expected.

Interest rate (RR) is also expected to positively influence profitability of the bank because a

rise in interest rate increases the amount of income a bank accrues on new asset it acquires. It

also hinges on the amount of loans and securities hold. For this reason, a high interest rate is

associated with higher interest margins and profitability.

Inflation rate (IR) may have effect on profitability because a rise in inflation rate reduces

the net present value of future cash flow. Hence, it erodes the real value of money reserves.

However, an inflation rate that is fully anticipated raises profits because it allows banks to

adjust interest rate in order to increase revenues.

4. Empirical Analysis and Results

4.1 Unit Root Test

Table 1 shows the results of the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP)

test statistic. The results show that ROA and ROE are stationary in levels, implying that they

are integrated of order 0. Meanwhile, the other variables NIM, LNGDP, IR and RR only

became stationary after being differenced once, meaning they are of order of integration 1.

The concept of being stationary or not containing unit root implies that the variables has zero

mean, constant variance and the residuals uncorrelated over time.



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Table 1: Unit root tests: ADF and PP in levels and first difference

Variable

Model

Specification ADF PP

ADF

PP

Order of

Integratio

n

Levels Levels

First

Difference

First

Difference

ROAt

Intercept -3.63** -.40** -9.44** -4.33** 0

Intercept and

trend -3.87** -4.82**

-9.42**

-5.75**

0

ROEt

Intercept -5.07** -5.06**

-8.96**

-19.90**

0

Intercept and

trend -5.52**

-

5.49**

-8.87**

-22.12**

0

NIM

Intercept -2.03 -2.16 -7.78** -8.28** 1

Intercept and

trend -2.12 -2.00

-7.88**

-16.36**

1

LNGDPt

Intercept -0.70 -0.75 -10.28** -10.23** 1

Intercept and

trend -2.78 -2.70

-10.19**

-10.14**

1

IRt

Intercept -3.09 -2.32 -3.98** -4.07** 1

Intercept and

trend -3.09 -2.26

-3.96**

-4.47**

1

RRt

Intercept -2.02 -1.43 -4.02** -3.97** 1

Intercept and

trend -2.61 -2.37

-3.93**

-3.87**

1

Source: author’s compilation and values obtained from Eviews

Notes: (a) ** means the rejection of the null hypothesis at 5%.

4.2 Testing for Cointegration

The results for the Johansen cointegration test based on trace and maximum Eigen values

test statics are presented in tables 2, 3 and 4 respectively. The results for the maximum Eigen

values test statistic show no cointegrating equations. Similarly, the results of the trace statistic

also show no cointegrating equations. This suggests that the null hypothesis of no

cointegration could not be rejected. Furthermore, this implies that only short run analysis can

be conducted by estimating a VAR model.



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Table 2: The Johansen co-integration test based on trace and maximal Eigen value - ROA

Maximum Eigen Test Trace Test

H0: rank

= r

Ha: rank

= r

Statistic 95%

Critical

Value

H0: rank

= r

Ha: rank

= r

Statistic 95%

Critical

Value

r = 0 r =1 20.65 27.58 r = 0 r >=1 46.45 47.86

r <=1 r = 2 16.96 21.13 r <= 1 r >= 2 25.80 29.80

r <=2 r = 3 6.88 14.26 r <= 2 r >= 3 8.85 15.49

r <=3 r = 4 1.97 3.84 r <= 3 r >= 4 1.97 3.84


Note: Both Max-Eigen value and Trace tests indicates no cointegrating equations at the 0.05 level.

Table 3: The Johansen co-integration test based on trace and maximal Eigen value -ROE


H0: rank

= r

Ha: rank

= r

Statistic 95%

Critical

Value

H0: rank

= r

Ha: rank

= r

Statistic 95%

Critical

Value

r = 0 r =1 20.57 27.58 r = 0 r >=1 43.36 47.86

r <=1 r = 2 13.70 21.13 r <= 1 r >= 2 22.79 29.80

r <=2 r = 3 7.99 14.26 r <= 2 r >= 3 9.09 15.49

r <=3 r = 4 1.11 3.84 r <= 3 r >= 4 1.11 3.84



Table 4: The Johansen co-integration test based on trace and maximal Eigen value - NIM


H0: rank

= r

Ha: rank

= r

Statistic 95%

Critical

Value

H0: rank

= r

Ha: rank

= r

Statistic 95%

Critical

Value

r = 0 r =1 20.47 27.58 r = 0 r >=1 43.45 47.86

r <=1 r = 2 14.52 21.13 r <= 1 r >= 2 22.99 29.80

r <=2 r = 3 6.46 14.26 r <= 2 r >= 3 8.46 15.49

r <=3 r = 4 2.00 3.84 r <= 3 r >= 4 2.00 3.84



4.3 Impulse Response Functions

The results for the IRF show how each measure of profitability respond to the shocks.

Figure 1 shows the response of ROA, to innovations in the LNGDP, IR and RR. The effects of

the shocks in the macroeconomic determinants are transitory on all the profitability measures,

as they die out in the short run. In particular, the effects on all the profitability measures die

out after five quarters and thereafter, converge toward the steady state as the horizon extends.

The short run effects show that there is indeed a relationship between the macroeconomic



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variables and commercial bank’s profitability in Namibia. However, the results show that

though the effect is visible and short-lived, it does not appear to be of greater magnitude or

statistical significance. Moreover, the relationship cannot be definitively stated as the effects

appear to dwindle on both positive and negative grids. Hence, one may conclude that when

ROA is used as a measure of profitability, there is no definitive or pronounced directional

relationship between the variables.

Figure 1: Impulse response functions for return on assets

-.4

-.2

.0

.2

.4

5 10 15 20

Response of D(ROA) to D(LNGDP)

-.4

-.2

.0

.2

.4

5 10 15 20

Response of D(ROA) to D(IR)

-.4

-.2

.0

.2

.4

5 10 15 20

Response of D(ROA) to D(RR)

Response to Generalized One S.D. Innovations ± 2 S.E.

Source: author’s compilation using Eviews

Figure 2 below shows the response of return on equity to shocks in LNGDP, IR and RR.

The results for the IRF show that ROE is almost non-responsive to innovations in the LNGDP



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and IR. However, it appears to be responsive to RR, though dwindling between both positive

and negative grids. Hence, one cannot conclude a definite directional relationship. Moreover,

the effects of the shocks are transitory, as they die out in the short run after ten quarters to be

specific.

Figure 2: Impulse response functions for return on equity

-8

-4

0

4

8

5 10 15 20

Response of D(ROE) to D(LNGDP)

-8

-4

0

4

8

5 10 15 20

Response of D(ROE) to D(IR)

-8

-4

0

4

8

5 10 15 20

Response of D(ROE) to D(RR)



Figure 3 below shows the response of net interest margin to shocks in LNGDP, IR and RR.

The results show that net interest margin is almost non-responsive to such shocks and there

seem to be no effects as there was no deviation or movement from the initial equilibrium.



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Figure 3: Impulse response functions for net interest margin

-.0005

.0000

.0005

.0010

5 10 15 20

Response of D(NIM) to D(LNGDP)

-.0005

.0000

.0005

.0010

5 10 15 20

Response of D(NIM) to D(IR)

-.0005

.0000

.0005

.0010

5 10 15 20

Response of D(NIM) to D(RR)



To sum up the results, first, the results show that there is a relationship between the

macroeconomic variables and commercial bank’s profitability in Namibia when ROA is used

as a measure of profitability. However, the results show that though the effect is visible and

short-lived, it does not appear to be of greater magnitude or statistical significance. Secondly,

when ROE and NIM are used as measures of profitability, there are almost very minimal or

unnoticeable responses. Furthermore, the responses are insignificant. These results are similar



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to those of Athansoglou, Delis and Stakouras (2006), Were and Wambua (2014) as well as

Antwi and Apani (2015). In particular, these studies found GDP to be insignificant in

explaining profitability. In the Namibian case, all the variables are insignificant.

4.4 Forecast error variance decomposition

Table 5 shows the results of the forecast error variance decomposition over the horizon of

24 quarters. The forecast error variance decomposition for ROA is mostly attributed to itself in

the first quarter with LNGDP and RR increasing their contributions as soon as at six quarters.

However, the fluctuations caused by the two variables are not very significant. Similarly, the

fluctuations in ROE are mainly attributed to itself but LNGDP and RR with RR showing

relative high contribution than LNGDP as the forecast error horizon extends. In the same

fashion, the forecast error variance decomposition in NIM are largely due to itself in the first

quarter but after 6 quarters the variables LNGDP and RR contributed to the fluctuations in

NIM as the horizon extend but with less magnitude.

Table 5: Variance Decomposition

Variance Decomposition of ROA

Quarter ROA LNGDP IR RR

1 100 0 0 0

6 82.19 8.94 1.58 7.29

12 81.27 9.43 1.55 7.75

18 81.16 9.47 1.54 7.83

24 81.15 9.47 1.54 7.84

Variance decomposition of ROE

Quarter ROE LNGDP IR RR

1 100.00 0 0 0

6 88.07 2.16 1.82 7.94

12 85.75 2.42 1.81 10.01

18 85.63 2.45 1.82 10.10

24 85.62 2.45 1.82 10.12

Variance decomposition of NIM

Quarter NIM LNGDP IR RR

1 100.00 0 0 0

6 88.29 3.85 1.67 6.19

12 87.56 4.15 1.73 6.55

18 87.50 4.16 1.77 6.58

24 87.49 4.16 1.77 6.58


5. Conclusion

This study examined the macroeconomic determinants for commercial bank’s profitability

in Namibia. This was done with the purpose of establishing which of the determinant affects

bank’s profitability with greater impact. The study was based on quarterly data covering the



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period 2001:Q1 to 2014:Q2, utilizing the technique of unit root, cointegration, impulse

response functions and forecast error variance decomposition. The results reveal that the

following variables gross domestic product, inflation rate and interest rate do not influence

commercial bank’s profitability in Namibia. This suggests that the macroeconomic

environment does not affect the commercial bank’s profitability in the Namibian context.

Irrespective of the results, the study recommends that the macroeconomic environment should

continue to be monitored as it has linkage to many economic sectors including the banking

sector. Moreover, macroeconomic environment is critical for the financial sector and banking

sector. Thus, it cannot be ignored at any expense. For this particular study, however, there is

room for improvement in the sense that the data used are aggregated. Future studies should

consider using disaggregated data and compare the results thereof with those in the current

study

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