THE FACTORS
AFFECTED ON REAL
INTEREST RATE
1)

THEORETICAL ASPECTS

1. Basic Theories
According to John M. Keynes - the author of General Theory of Employment,
Interest and Money
The market interest rate depends on the demand and supply of money and also
is the price which brings into equilibrium the desire to hold wealth in cash with
the supply of cash resources, and the reward for parting with liquidity at the
same time. There are certain macro factors operating in the economic
environment that will influence the Interest rate namely Gross Domestic
Product, Inflation Rate and Broad Money. The data is collected in the year 2017
and model regressions are used for analyzing the relationship between the
interest rate and selected economic variables. The study shows that Gross
Domestic Product, Inflation Rate and Broad Money are the factors that
significantly predict the interest rate of an economy


2. Review of Literature
BARZAN OMAR ALI ( August 2018 ) : FACTORS AFFECTING INTEREST
RATE AND ITS RELATION WITH BANKS’ PROFITABILITY
a. Purpose of the research:
The aim of this study is to investigate the factors influencing interest rates and
its relationship with private banks profitability in Erbil.The researcher attempted
to find out the most of significance factor that affects interest rates and its
relationship with banks Profitability
b. Method : Quantitative research method

c. Sample size: the researcher gathered data from employees at different levels
of management at private banks in Erbil. The researcher distributed 300
questionnaires, but only 238 questionnaires were received from employees and
completed.
d. Model:

In the research, the researcher mentioned the positive relationships between all
independent variables and real interest rate which has a positive relationship
with Banks Profitability.
e. Result :


The findings of multiple regression analysis revealed that the highest value was
for economic growth (measured as GDP ), the B value =.812, this indicates that
economic growth significantly and positively affects interest rates as a result
maximizing bank’s profitability. The wage inflation is also a variable that has a
significant effect of wage inflation on interest rate with the B value equals .701.
Overall it can be concluded that the interest rate is sensitive to the fluctuations of
wage inflation and economic growth.
JAMES HAMILTON:
EXCHANGE RATES

FACTORS

INFLUENCING

INTEREST AND

The author indicates that borrowers also look at how many people are looking
for credit and if it is available. If there is an increase in demand for money, the

lender will probably increase the interest rate, not only for the profit but to
control the process. And demand is low; banks will want to incentivize
borrowers by offering them lower interest rates. Conversely, when there is an
increase in supply, the interest rate goes down because consumers have more
options, whereas less supply leads to a surge in interest.
3. Model
MODEL: IR = INF + MS + GDP + Status
A) Dependent Variable: Y : Real Interest Rate ( IR )
B) Independent Variable:
X1:Gross Domestic Product ( GDP)
X2: Inflation Rate ( INF )
X3: Broad Money M3 ( MS )
C) Dummy variable:
X4: Status
1: Developed countries
0: Developing countries

4. Research gap
In Keynes’s theory of interest rate, he highlights the importance of demand and
supply of money, but what he misses which we believe is the gap of his research
is Gross Domestic Product. Besides the factors we mentioned earlier, GDP is
also a variable that influences the interest rate greatly. Central banks have to
look at the GDP or inflation rate to adjust the interest rate, which can be affected
in some period of expansion or contraction of the economies. Because of that,


our group decided to put the GDP as an independent variable to the measure the
change in the interest rate.
Also, we add more observations which are 41 economies around the world and
add the dummy variables to discover whether there is a difference between

developed and developing countries or not.
5. Data and objectives
The data be collected on the website databank.worldbank.org and they
are carefully arranged in the file excel.
Click on the link to have more detail about our data:
The methodology: OLS estimated
The main objectives of the study are:
+) To identify the relationship between selected economic variables and the
interest rate so that we can predict the near future interest when the other
economic variables fluctuate.
+) To analyze the impact of selected economic variables on the interest rate.
+) To detect any differences between developed and developing countries in
terms of effects on real interest rate of mentioned explanatory variables.


Model estimation
A. Semi-logarithmic regression function

Interpretation:
1) Real interest rate will decrease -0.268 percent when inflation increases 1
percent, increase 0,0089 percent when money supply increases by 1
percent.
2) When GDP increases by 1 percent, the real interest rate will increase
-0,024 percent.
3) Interest rate will equal to 22,52 percent ( coefficient of intercept) when all
explanatory variables equal to zero
4) p-value( < = 0.05 → reject H0) of t-test of dummy variable said that there
is a difference in the relationship between independent and dependant
variables, between developed and developing countries.



B. Linear regression function

Interpretation:
1) Real interest rate will decrease -0.31 percent when inflation rate
increases 1 percent ( negative relationship), while GDP growth rate
increases 1 percent can make real interest rate decline 0.16 percent.
2) In regards to the money supply growth rate, when it increases 1
percent, the dependent variable will increase 0,018 percent.
(positive relationship)
3) The dummy variable provided the information that effects on the
interest rate of dependent variable in developed countries will be
different from that in developing countries.


C. Quadratic regression function

Interpretation:
1) In order to increase real interest rate -0.31 percent, inflation decreases 1 percent
(negative relationship), when GDP or MS increase by one percent, real interest rate
will decrease -0,02 percent and increase 0,0043 percent, respectively.
2) Squared money supply growth rate increases 1 percent then the real interest rate
increases 0,0025 percent.
3) The Dummy variable still provides the same information like the other functions.


Testing for the most efficient model
● Ramsey RESET Test:
● Following results, we want to know whether our models are unbiased or


not.
1.

Semi-logarithmic regression

β0 + β1INF +β2developed + β3Log(GDP) + β4Log(MS) + β5MS01
Hypothesis pair:{ H1: β6=0 ; H1: β6 ≠ 0 }
IR =

Value

df

Probability

t-statistic

1.976099

35

0.0561

F-statistic

3.904968

(1, 35)

0.0561


Likelihood ratio

4.442503

1

0.0351

p-value= 0.0561
α = 0.05

● Conclusion:
→ p-value > α
→ Accept H0
→ The model is unbiased or has correct form.


2. Linear regression function
IR = β0 + β1inf +β2Developed + β3GDP_Growth_Rate + β4MS
Hypothesis pair:{ H1: β5=β6=0

; H2: β25 +β26≠ 0 }

● Conclusion:
p-value= 0.2556> ∝= 0.05
→ Accept H0
→ The model is unbiased or has correct form.
3. Quadratic regression function
IR = β0 + β1INF +β2Developed + β3Log(GDP) + β4Log(MS) +

β5MS2
Hypothesis pair:{ H1: β6=0

; H2 : β6 ≠ 0 }

● Conclusion:
p-value= 0.1247> ∝= 0.05
→ Accept H0
→ The model is unbiased or has correct form.


● WHITE test:
Following results, we want to know whether the variance of the errors in a
regression model is constant or not.
1) Semi-logarithmic regression function
IR = β0 + β1INF +β2developed + β3Log(GDP) + β4Log(MS) + β5MS01
Hypothesis pair:

{H0: ; H1: }

We can see that p-value of F-test > �=0,05 → Accept H 0 → means

that function does not

contain .
2) Linear regression function
IR = β0 + β1inf +β2Developed + β3GDP_Growth_Rate + β4MS
Hypothesis pair:

{H0: ; H1: }


We can see that p-value of F-test > �=0,05 → Accept H 0 → means

that function does not

contain .
3) Quadratic regression function
IR = β0 + β1INF +β2Developed + β3Log(GDP) + β4Log(MS) +
β5MS2
Hypothesis pair:

{H0: ; H1: }
We can see that p-value of F-test > �=0,05 → Accept H 0 → means

contain .

that function does not


Selecting the best efficient model:

Because all three models are unbiased and heteroskedasticity, we use five
criteria to select the most efficient model.

Model

R2

Adjusted R2


AIC

HQ

SC

(1)

0.421725

0.341409

5.758634

5.849623

6.006873

(2)

0.331067

0.258750

5.856649

5.832474

6.063515


(3)

0.412250

0.330619

5.774886

5.865875

6.023124

According to the table above, the most efficient model is model (1) (semilogarithmic regression model).
Interpretation:
● Real interest rate will decrease -0.268 percent when inflation
increases 1 percent, increase 0,0089 percent when money supply
increases by 1 percent. ( when everything else stay constant)
● When GDP increases by 1 percent, the real interest rate will
increase -0,024 percent.( when everything else stay constant)
● Interest rate will equal to 22,52 percent ( coefficient of intercept)
when all explanatory variables equal to zero( when everything else
stay constant)
● p-value( < = 0.05 → reject H0) of t-test of dummy variable said
that there is a difference in the relationship between independent
and dependant variables, between developed and developing
countries.( when everything else stay constant)
● R squared equals 42%, which means that independent variables
can explain up to 42% dependent variable.
Test of economic assumption:


* Following the result of the best model, whether the natural logarithm of GDP
has an impact on the real interest rate?
Hypothesis pair:


We can see that p-value of t-test of log_GDP equals 3%< �=5%
→ reject H0 → the

natural logarithm of GDP has truly an impact on the real

interest rate
* Following the result of the best model, whether the natural logarithm of GDP
and logarithm of Money supply have a profound impact on the real interest
rate?
Hypothesis pair: {H0: �4=�5=0; H1: �52+ �42 ≠ 0}

In eviews �3 and �4 are C(4) and C(5) respectively
p-value= 0,08> �=0,05 → accept H0 → Meaning that natural logarithm of GDP
and Money supply are jointly insignificant and they do not have impact on the
real interest rate.


Conclusion (suggestion and prediction)
The growth potential of international tourist movements depends on a number of
variables that include economic, demographic, technological, psychological,
socio-political, etc. It is impossible to quantify the interrelationships among all
these variables so as to be able to carry out a complete analysis of trends in
international tourism. However, using as a yardstick tourism’s appeal as a
qualitative social practice, and if current trends continue and come to be
realized, then one should expect a bigger increase in demand, both in the near

and in the more-distant future. In the analysis above, an effort is made to
identify those variables that influence tourist demand.
In this study, we tested and compared 3 models:
(1) Linear function: in_tour_exp = β0 + β1ipc + β2er + β3tc + β4status + u
(2) Quadratic function: in_tour_exp = β0 + β1ipc + β2er + β3er2 + β4tc +
β5status + u
(3) Semi Logarithm function: in_tour_exp = β0 + β1log(ipc) + β2er + β3tc +
β4status + u
to find the most efficient model to evaluate variables that influence tourist
expenditure.
We employed the Ramsey RESET test to see whether our models were
unbiased. The result suggested that all 3 models had its correct form. Next, the
WHITE test was used to gauge whether the variance of the errors in a
regression model is constant. Since all three models were found to be unbiased
and heteroskedasticity, we used five criteria: namely R2, Adjusted R2, AIC,
HQm, SC to find out the most efficient model.
According to the result was showed at table 3.1, the most efficient model was
model (3).
In the econometric model used, per capita income was not found to have as
significant an impact on tourist expenditure as one might expect. This means
that these countries continue to attract tourists even as their income declines
during the economic downturn.
Next, the official exchange rate of the currency of the countries of origin vis-avis the landing countries does not appear to play a substantial role. In fact, it was
found to have a zero impact on the international tourism expenditure.
Transportation costs had a noticeable influence on tourist demand. The
continuously increasing cost paid for transportation of tourists, have resulted
into increase in the total cost.
The general conclusion is that the tourist host-countries have to face a more
demanding, more competitive, and an intensely differentiated tourist market



which forces policy-makers to draw and apply a tourist policy employing
diligence, timely planning, responsibility, and realism.