ECN5221 ECONOMETRIC METHOD
THE DETERMINANTS OF ECONOMIC GROWTH IN MALAYSIA: AN EMPIRICAL INVESTIGATION
MD AYMAN MUNIP BIN ABD MALEK (GS56325)
NURUL SYAHIDAH BINTI MUHAMMAD NASARDIN (GS55302)
Abstract
Economic growth has long been discussed in the past study among researchers. That associated
the factors behind the fluctuation in economic growth. In this study, macroeconomic variables
such as inflation, exchange rate, foreign direct investment are selected to test the relationship on
the economic growth of Malaysia, as measured using Gross Domestic Product (GDP) as the
dependent variable. By utilizing time series data (1982-2018), a series of empirical analysis
including Augmented- Dickey Fuller (ADF) (1979), Philip Peron from 1988 and KwiatkowskiPhillips-Schmidt-Shin (KPSS) unit root assessment and Auto-Regressive Distributed Lag Model
(ADRL). Our finding reveals, further tested at their first order difference level for ADF, PP and KPSS
tests showed that all variables are stationary and have no unit root at 1 percent significance level.
We conclude that in the long term, the development of the foreign direct investment leads to the
development of economic growth in Malaysia.
Keywords: Economic Growth; (GDP); inflation rate, foreign direct investment, exchange rate
1. INTRODUCTION
1.1 Economic growth
Economic growth can be used to explain the differences between the poorest and the richest
nations. The roots of this great disparity when there has the differential growth experience such
as led to a huge gap in income per capita that we measured in Gross Domestic Product (GDP) per
capita. Malaysia is known as an upper-middle income country, and on the journey toward highincome. Being an open economy, international trade has played significant role in Malaysia’s
economic development spanning over more than three decades. This paper attempts to define
the determinants of economic growth focused on Malaysia country. There are a few factors that
will determine economic growth in Malaysia such as exchange rate, foreign direct investment,
and inflation rate.
2. THEORETICAL FRAMEWORK
Economic theory suggests that growth in developing economies are driven in part by
foreign direct investment (FDI). This takes into account the technological level of the host
country, its economic stability, the host country’s investment and technology policy and that the
host country exercises a good degree of economic openness (trade openness, for example). If
productivity growth is enhanced and biased toward exports then growth and export dynamics
will change.
FDI plays an indispensable role for economic growth, considering the absorptive
capability of the host country is sufficient, taking into factor human capital in facilitating and
participating in the investment process (Borensztein, De Gregorio and Lee, 1998). O Zhang (1999)
also examined 10 Asian countries in analysing the relationship between economic growth and
FDI, suggesting there exists a unidirectional causality from FDI to GDP in the case of Hong Kong,
Taiwan, Japan, Singapore. The positive effect of FDI on economic growth is also suggested by
Moudatsou (2003) for European Union, analysing the period 1980-1996 in which the trade
reinforcement is taking place.
In the case of inflation, Khan and Senhadji (2001), using econometric methods of sample
splitting and threshold estimation (Hansen, 2000) examined the threshold effects of inflation on
economic growth, whereby for the developing countries this ranges between 7 to 11 percent and
for advanced countries is between 1 to 3 percent. The study suggests that more stable and low
inflation has always been associated with positive economic growth. This is suggested so in the
long run, although there may exist some conflicts between the need to control inflation while
promoting economic growth in the short run. Despite the potential conflicts, the consensus up
to now is in favour of maintaining price stability, i.e. controlling the inflationary effects of prices
through the existing monetary policy tools. (Mishkin, 2008)
The relationship between economic growth and inflation rate is also an important one,
among the economic indicators explored here. Several developing countries have fixed their
currency exchange to that of advanced countries; the most common being the US dollar (US).
However, the rising values of the currencies of these developing countries put them at risk of
current account deficits and eventual currency devaluation, which in turn may lead to recession.
The famous Bala-Samuelson hypothesis suggests that the increase in productivity in the tradable
sector is likely to be higher than the productivity in non-tradable one, and the real exchange rate
will reflect such changes. As posited by Ito (1999) this is not the case for emerging market
economies.
Habib, Mileva and Stracca (2016) analysed the impact of movements in the real exchange
rate on economic growth based on five-year average data for a panel of over 150 countries in the
post Bretton Woods period. The results of their study pointed out that a real appreciation
(depreciation) reduces (raises) significantly annual real GDP growth. They suggested that the
exchange rate regime as a policy lever is only useful for countries in their early stage growth and
such policy becomes less relevant as countries become more advanced and richer.
Three macroeconomic variables, particularly exchange rate, foreign direct investment
(FDI) and inflation are tested in order to identify their impact on economic growth, as measured
using Gross Domestic Product (GDP). Hence, the theoretical framework is constructed as follows:
GDP = ƒ (ER, FDI, INF)
Where GDP is economic growth (in Ringgit Malaysia); ER is exchange rate (in Ringgit
Malaysia); FDI is foreign direct investment inflows (in Ringgit Malaysia); Δ indicates the change
at First Order Difference; and subscript t-1 denotes the time series period (with 1 year’s lag).
After taking the log for the appropriate variables, the following equation is obtained:
ΔlnYt = μ + β1 ΔlnERt-1 + β2 ΔlnFDIt-1 + β3 ΔlnINFt-1 + β4 Δt-1 + ε
3. ECONOMETRIC MODEL
3.1 Unit Root Test
The first step of constructing a time series data is to determine the non-stationary
property of each variable, we must test each of the series in the levels and in the first difference
(log of GDP, log of ER, log of FDI and log of INF). First, the ADF test with and without a time trend.
The latter allows for higher autocorrelation in residuals. However, as pointed out earlier, the ADF
tests are unable to discriminate well between non-stationary and stationary series with a high
degree of autoregression. In consequences, the Phillips –Perron (PP) test (Phillips and Perron,
1988) is applied. The PP test has an advantage over the ADF test as it gives robust estimates when
the series has serial correlation and time-dependent heteroscedasticity.
In ADF and Phillips Perron tests, the null hypothesis of non-stationarity is tested the t-statistic
with critical value calculated by MacKinnon (1991). The outcome suggests that reject null
hypothesis which can conclude the series is stationary. Both ADF and PP test are applied following
Engle and Granger (1987) and Granger (1986) and subsequently supplemented by the PP test
following West (1988) and Culver and Papell (1997).
Hypothesis 1:
H0: Series contains a unit root H1: Series is stationary
Besides that, Kwiatkowski, Phillips, Schmidt, and Shin (1992) introduce such a test, and do it by
choosing a component representation in which the time series under study is written as the sum
of a deterministic trend, a random walk, and a stationary error. The null hypothesis of trend
stationary corresponds to the hypothesis that the variance of the random walk equals zero. As
one could expect, their results are frequently supportive of the trend stationarity hypothesis
contrary to those traditional unit root tests. In the KPSS test, the null hypothesis of stationarity is
tested. The outcome suggests that do not reject hypothesis, which can conclude the series is
stationary. Besides, testing for stationarity is so important in time series data is to avoid spurious
regression problems and violate the assumption of the Classical Regression Model.
Hypothesis 2:
H0: Series is stationary H1: Series contains a unit root
3.2 Auto-regressive Distributed Lag Model Test
Model 1: Impact on Gross Domestic Product (GDP) by other variables
ΔlnYt = μ + β1 ΔlnERt-1 + β2 ΔlnFDIt-1 + β3 ΔlnINFt-1 + ε
According to the study methodology, the ARDL method will be used in three stages:
1) In the first stage, co-integration is tested in the framework of the UECM error model, In the
case of a co- integration of the variables, the second stage includes the estimation of the longterm equation and then an Error Correction Model (ECM) is constructed.
2) Based on the economic theory and the applied models in previous studies on the same subject,
the following equation will be estimated for the purpose of measuring the impact of the growth
of economic during the period 1982-2018 in Malaysia.
Where:
Y = β0 + β1ER + β2FDI + β3INF + ε
Y = Gross Domestic Product (GDP)
ER= Exchange Rate
FDI= Foreign direct investment
INF= Inflation rate
ε = random error.
It should be noted that the use of the ARDL model does not require stationarity tests, but it
requires that there are no stable time series at the second difference, which may affect the
accuracy of the results. Appropriate lag periods will be found in accordance with the AIC and SC
standards
4. DATA DESCRIPTION
To account for the given variables, data is collected from World Development Indicators (WDI)
of the World Bank. The date collected is Gross domestic product ( GDP is measured in US dollar
at constant prices), exchange rate (denoted as ER, where the average official exchange rate is
used as the proxy for ER), inflation rate (denoted INF, using consumer price index, CPI also
measured as average for the given year), foreign direct investment (FDI), where we obtained FDI
net inflow. To summarize, GDP, ER, FDI and INF
5. EMPIRICAL RESULTS
5.1 Unit Root and Stationary Test: The Augmented Dickey-Fuller (ADF), Phillip Peron (PP) and
Kwiatkowski-Phillips-Schmidt-Shin (KPSS) Approach
Based on the test result in Table 1, the problem of unit root is present in some of the
variables in their levels. Using the ADF test, only FDI is stationary at 5 percent level of
significance and INF is stationary at 1 percent level of significance for models with intercept.
Results based on the PP test indicate FDI stationary at 5 % level of significance and INF and N
stationary at 1% level of significance. Based on KPSS only INF is stationary at 1% level of
significance. As for models with intercept and trend, the ADF test shows that FDI and INF is
stationary at 1% level of significance. Using PP test, FDI is stationary at 5 percent level of
significance and INF is stationary at 1 percent level of significance for models with intercept and
trend. For the KPSS test, Y, ER, FDI and INF are stationary at 1 % level of significance. Then, these
variables were further tested at their first order difference level. ADF, PP and KPSS tests for both
models showed that all variables are stationary and have no unit root at 1 percent significance
level.
Table 1: Result of Unit Root Test
ADF
PP
KPSS
Constant,
Constant,
without trend with trend
Constant,
without trend
Constant,
with trend
Constant,
Constant,
without trend with trend
LY
-0.5973
-2.1851
-0.6100
-2.392
0.7068
0.0533***
LER
-1.2637
-1.8969
-1.3689
-1.8969
0.6448
0.0967***
LFDI -3.4607**
-4.976***
-3.433**
-4.976**
0.5757
0.0725***
LINF -4.422***
-4.3605***
-4.441***
-4.3239***
0.0755***
0.0718***
Level
First Difference
LY
-5.0887***
5.0051***
-5.0969***
-5.0144***
0.0570***
0.05326***
LER
-4.6527***
-4.5761***
-4.5689***
-4.4857***
0.0609***
0.05756***
LFDI -6.6544***
-6.541***
-18.3445***
-17.923***
0.2398***
0.2390***
LINF -4.8283***
-4.7311***
-9.026***
-8.859***
0.0703***
0.0704***
Note: ***, **, * correspond to the level of significance at 1 percent, 5 percent and 10 percent.
ADF refers to Augmented Dickey Fuller test, PP refers to Philip-Perron, KPSS refers to
Kwiatkowski-Phillips-Schmidt-Shin
5.2 Auto-Regressive Distributed Lag Model (ADRL)
(a) Time lags number was three lag period for the LY variable, four periods for the LER variable,
four lag period for the LFDI and four lag period for the LINF.
(b) The co-integration model according to ARDL. After estimating the UECM model according to
the ARDL method, the following results were obtained: ΔlnYt = 0.721335 + 0.290071 ΔlnERt-1 +
0.013393 ΔlnFDIt-1 – 0.020276 ΔlnINFt-1 + ε
R-squared
0.999430
F-Statistic
1364.716
Prob (F- Statistic)
0.00000
The results of the statistical tests of the regression equation shown above indicate that the
estimated model is good as the coefficient of determination is equal to 0.99, meaning that the
model interprets 99% of the changes in the rate of economic growth. -The results also indicate
that the relationship between the dependent variable and the explanatory variables is not false,
the value of F-statistics has a significant value of 1364.716. However, it is necessary to test
whether the residuals have a self-serial correlation through the Breusch-Godfrey Serial
Correlation LM Test as follows:
Table 2: Breusch-Godfrey Serial Correlation LM Test for French data
Breusch-Godfrey Serial Correlation LM Test:
F-Statistic
2.531914
Prob. F (2,12)
0.1210
Obs*R-Square
9.793015
Prob. Chi-Square (2)
0.0075
According to the above we can accept the null hypotheses which assumes that there is no serial
correlation between the residuals, p-value is greater than 0.05.
(c) The structural stability of the estimated ARDL model is also tested to ensure that the data
used in this study are free of any structural changes over time using the Cumulative Sum of
Recursive Residual (CUSUM) test. The structural stability of the estimated coefficients of the
UECM is achieved if the CUSUM graph is within the critical limits at a significant level of 5%. The
figure 1 shows that estimated coefficients for the ARDL model are structurally stable over the
period of study.
Cusum Test
Cusum Square Test
Figure 1: The Cumulative Sum of Recursive Residual (CUSUM) and The Cumulative Sum of
Recursive Residual Square test.
(d) Estimation of long-term equation
The long-term relationship between the variables is verified using the Wald-F test, which tests
the hypothesis of no co-integration of the variables versus a co-integration to detect the longterm equilibrium relationship between variables. Co-integration is tested in equation (1)
through the following assumptions:
H0: LY=LER=LFDI=LINF=0 H1: LY≠LER≠LFDI≠LINF) ≠0
The rejection of the null hypothesis is based on comparing the value of F calculated by the tabular
values according to the critical limits proposed by (Pesaran et al., 2001) where the table consists
of two extremes, in the case of the current model, is 2.86 as a minimum and 4.01 as maximum
at level of significance 5%. Since the value of f-statistic = 5.38 is greater than the upper limit,
there is a long-term relationship between the variables in the following form:
ΔlnYt = μ + β1 ΔlnERt-1 + β2 ΔlnFDIt-1 + β3 ΔlnINFt-1 + ε
And this equation can be estimated as follows:
ΔlnYt = 9.890343 + 0.796831 lnERt-1 + 0.745547 lnFDIt-1 - 0.032685lnINFt-1
The variable of FDI has a significant level of 1%, if the FDI increases by 1%, the growth of the
economic will increases 7.5%. We conclude that in the long term, the development of the foreign
direct investment leads to the development of economic growth.
(e) Estimation of ARDL error correction model
After obtaining the long-term relationship according to the co-integration model, the ECM model
is estimated to test the short-term relationship between the independent variables and the
dependent variable according to the following formula:
ΔlnYt = μ + β1 ΔlnERt-1 + β2 ΔlnFDIt-1 + β3 ΔlnINFt-1 + ε
is the lagged value of the error term in the long-term relationship model. Based on the estimation
of the ECM model under the ARDL model, the following results were obtained:
ΔlnYt = -0.0729 – 0.2901lnERt-1 – 0.0134 lnFDIt-1 + 0.0203lnINFt-1
R-squared
F-Statistic
0.9729
5.3829
It is clear from the previous results that the model explains only 97% of the changes in economic growth
in the short term as R2 is 0.97 which means that 97% of the changes in economic growth in the short term
can be explained by the variables in the model.
There is also a significant negative relation between both of ER and FDI, and Y according to p-value values.
There is a positive significant relation between the inflation rate (INF) and the real GDP growth rate (Y) at
a level of significance (5%). It is also worth that the error correction coefficient is significant and takes a
negative value of 0.07 indicating the speed of return to the long-term equilibrium. Which means that any
deviation from the long-term equilibrium path between the explanatory variables and the independent
variable in period t-1 will be compensated in period t.
From the above we can say that in the long term, there is a positive relationship between the foreign
direct investment and economic growth but the case isn’t the same for the short run. If we want more
economic growth, we have to pay attention to foreign direct investment.
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