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. 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