THE DETER.'n~A. ~TS OF EXPORTS IN MALAYSIA: A~ ECO~O~IETRIC STUDY

ABSTRACT

Wong Hock Tsen Sidah Idris

Merlyn Rita Anak Buncha

Exports play an important role to Malaysian economy. Therefore, it is important to identify the determinants of exports in Malaysia. Identifying the determinants of exports in Malaysia is important for policy markers. This study examines exports of Malaysia using an ARDL bounds test approach. The results show that there is a long-run relationship among the real exports, the relative prices and the world demand. Exchange rate volatility is found to have an adverse impact on exports. A change in the relative prices. the world demand and exchange rate volatility will have an impact on exports of Malaysia. Moreover, exports of Malaysia are price and income elasticities.

INTRODUCTION

Malaysia is a smaU-open developing economy. Malaysia transfonned its economy from import-substitution industrialisation in the 1960's to export-oriented industrialisation in the 1970's and to heavy industrialisation in the 1980's and 1990's. As a result of structural change in the economy, the composition of exports also changes. In the 1960's and 1970's, Malaysia exported mainly commodities such as rubber and tin. In the 1980s, exports of manufactured goods became dominant. In the 1990's and 2000's, exports of manufactured goods accounted more than half of exports of Malaysia. The importance of exports to Malaysian economy can be seen from the ratio of exports to gross domestic product (GDP). In 1970-1979, the average ratio of exports to GDP in Malaysia was 46.46 percent. The average ratio of exports to GDP increased to 57.85 in 1980-1989 and to 91.24 in 1990-1999. In 2002, the ratio was 114.07.

The export sector also provides a large number of job opportunities to Malaysian. In 1970-1979, 11.55 percent of total employment was in manufacturing sectors. In 1980-1989, 15.88 percent of total employment was in manufacturing sectors. The employment in manufacturing sectors increased in the 1990's and 2000's. In 1990-1999,24.12 percent of total employment was in manufacturing sectors. In 2002,21.68 percent of total employment was in manufacturing sectors (Ministry of Finance Malaysia, various issues). The importance of exports to the Malaysian economy is a result of the industrialisation policy towards export-oriented. Moreover, exports are

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very important because of the limited domestic market Generally, the economic growth of Malaysia is said to be export-oriented (Ghatak el a/., 1997).

One of the important issues on examining exports is the impact of exchange rate volatility. The Bretton Woods System of fixed exchange rates broke down in 1973. Most countries have adopted flexible exchange rates (Appleyard & Field, 200 1: 680-686). In general, such fleXIble exchange rates are volatile (Gotur, 1985: 475). A prime concern of both academic economists and policy-makers is that greater volatility of exchange rates appears to increase the risk and uncertainty present in international transaction. Thus, the volatility of exchange rates is often thought to have an adverse impact on international trade (Arize, 1997). Research in this area is among the most active and extensive in international economics. The issue of the adverse impact of exchange rate volatility is also closely linked with the debate as to whether it is better to adopt a flXed or a flexible exchange rate system for an economy or for the world in general (Gagnon. 1993: 269).

Most theoretical analyses argue that exchange rate volatility has a negative impact on international trade (Hooper & Kohlhagen. 1978). Nevertheless, some theoretical models show that exchange rate volatility can have either a negative or a positive impact on international trade (De Grauwe, 1988). Thus, the impact of exchange rate volatility on international trade is an empirical issue rather than a theoretical issue. However, empirical fmdings are mixed and inconclusive (International Monetary Fund, 1984; McKenzie, 1999). This suggests that the impact of exchange rate volatility on international trade should be examined on a case-by-case basis.The main objective of this study is to examine the determinants of exports in Malaysia using an autoregressive distnbuted lag (ARDL) bounds testing approach over the period 1973-2002. The sample begins in 1973 because of the breakdown of the Bretton Woods system, which most countries adopt a flexible exchange rate system. Generally, exchange rates have been fluctuating. The export model to be estimated is derived from export demand and export supply. The export model is used in many studies on exports, including the case of Malaysia (Arize el a/., 2(00). The Dickey and Fuller (1979) and Phillips and Perron (1988) unit root test statistics are used to examine the stationary of the data. This study employs the Pesaran et a/. (200 1 ) bounds testing approach to examine the long-run relationship in the mode Is. The approach is applicable respective of whether the regressors are I( 1) or 1(0). Moreover, the long-run forcing variables in the export models are examined.

This study consists six sections. The next section provides a discussion on exports of Malaysia. This is followed by a literature review of the determinants of exports with attention on the impact of exchange rate volatility. The empirical estimation and results are then discussed. Finally, this study provides some concluding remarks.

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The Determinants 0/ Exports In Malaysia

EXPORTS OF MALAYSIA

Exports of Malaysia experienced significant growth over the past decades. In 1970-1979, the average growth of exports was 20.65 per annwn. In 1980-1989, the average growth of exports was relatively low, i.e. 11.73 per annwn. Nonetheless, the average growth of exports in 1990-1999 was relatively higher, i.e. 17.27 per annum. In 2000, the growth of exports was 17.03 percent. Although the growth of exports was negative, -8.84 percent in 200 1, the growth of exports became positive, 5.69 percent in 2002 (Table 1). Generally, changes in the growth of exports in Malaysia have been mainly as a result of changes in export prices and external demand In 1997-1998, the depreciation of exchange rate also affected exports of Malaysia. Increase in export prices will lead to a decrease in exports demand An increase in external demand will lead to an increase in exports.

The composition of exports changed over the past decades. The contribution of commodity products to total exports declined while the contribution of manufactured goo,ds to total exports, particularly electrical and electronic increased. In 1970-1979, the main exports of Malaysia were crude materials and manufactured goods. They accounted 41.55 percent and 17.04 percent of total exports, respectively. In 1980-1989, the main exports were crude materials, mineral fuels, and machinery and transport equipment. Nonetheless, the contribution of crude materials and mineral fuels to total exports declined while the contribution of machinery and transport equipment to total exports increased. In 1990-1999, machinery and transport equipment (Standard International Trade Code 7) became the main exports of Malaysia. They accounted 54.3 percent of total exports. The importance of machinery and transport equipment to total exports increased in 2000-2002, which accounted 61.06 percent of total exports (Table 2).

The direction of exports was about the same for the past two decades. In 1980-1989, the United States, Singapore and Japan were the main importers. Those countries accounted 49 percent of total exports of Malaysia. In 1990-1999, the same countries accounted 47 percent of total exports of Malaysia. However, the importance of those countries to total exports declined in the 2000s. In 2002, these countries contributed to 42 percent of total exports (Table 3). The importance of those countries has declined as a result of Malaysia international trade policy to diversify its exports to stabilise exports and thus to stabilise economic growth.

Exports have played an important role to Malaysian economy. Exports of Malaysia have experienced significant growth. The export prices and external demand were two important factors that influence exports of Malaysia for the past decades. The composition of exports has changed. The contribution of commodity products to total exports has declined while the contribution of manufactured goods to total exports has increased. The direction of exports was about the same for the past two decades. The United States, Singapore and Japan were the main importers. Exports have contributed to a big share of Malaysian GDP and have provided a large number of employment opportunities in manufacturing sectors.

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LITERATURE REVIEW

Goldstein and Khan (1978) proposed an exports model, which is derived from the supply and demand for exports. The model is expressed as:

(I)

where In is the logarithm; XI is the export volume; RPI is the relative prices, expressed by the export price of the exporting country to the export price of the importing country in terms of the exporting country's currency; Y

I is the income of the importing country

and ul.l is a disturbance term. The relative prices are expected to have a negative sign and the income of the importing country is expected to have a positive sign. They estimated for eight industrial countries (Belgium, France, Germany, Italy, Japan, the Netherlands, the United Kingdom, the United States). The results show that the relative prices and the income of the importing country are respectively found to have a significant impact on exports.

Theoretically, exchange rate volatility is argued to have a negative impact on international trade where economic agents are assumed to be risk averse. Nevertheless, exchange rate volatility is also argued to have a positive impact on international trade where economic agents are assumed to be risk lover. However, it is generally agreed that exchange rate volatility has a negative impact on international trade. One of the intuitions is that exchange rate volatility implies uncertainty, which is a risk in international transactions. The risk is a cost under the risk averse because in international transactions, prices for traded goods are agreed when contracts are signed before goods are delivered in which payments will be made in the future. Exchange rate may vary between the time when contracts are signed and the time when the payments are made. Thus, an increase in exchange rate volatility will lead to an increase in uncertainty and therefore will lead to an increase in costs of international transactions for the risk aVerSe agents. This motivates the risk averSe agents to reduce international transactions and concentrate more on domestic activities, which are assumed to be less risky. This idea has been forwarded by using theoretical models developed by, amongst others, Hooper and Kohlhagen (1978) and Broil (1994).

The impact of exchange rate volatility on international trade is actively researched (International Monetary Fund, 1984; McKenzie, 1999). On the whole, empirical findings in the literature of the impact of exchange rate volatility on international trade are mixed and inconclusive. For example,llooper and Kohlhagen (1978), International Monetary Fund (1984), and Koray and Lastrapes (1989), amongst others, found that the adverse impact of exchange rate volatility on international trade is statistically insignificant. There are many factors that could contribute to the insignificant impact of exchange rate volatility on international trade. For instance,

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Th~ Determinants of Exports In Malaysia

Gagnon (1993) demonstrated in a numerical analysis that the magnitudes of exchange rate variability currently observed among developed countries do not have a significant adverse impact on international trade. Moreover, international trade is determined jointly by many factors and therefore, exchange rate volatility per se may not have a strong adverse impact on international trade. The empirical fmdings are mixed and inconclusive. This suggests that the impact of exchange rate volatility on international trade should be examined on a case-by-case basis.

The impact of exchange rate volatility on exports is usually estimated by including a measure of exchange rate volatility in an export model. For example:

(2)

where VI is a measure of exchange rate risk or volatility and u2.1 is a disturbance tenn. De Grauwe (1988) and Kumar and Dhawan (1991), amongst others estimated the standard export model by including additional independent variable or by replacing independent variable in the standard export model with different proxy. These models are static and generally focus on aggregate trade flow. The study by Chowdhury (1993) is found to be one of the earliest studies that estimates the standard export in an error correction framework by using the ordinary least squares (OLS) estimator. He examined exports of the G7 countries, namely Canada, France, Germany, Italy, Japan, the United Kingdom and the United States using quarterly data over the period from 1973, quarter I, to 1990, quarter IV. He found that exchange rate volatility has a significant negative impact on exports in each of the seven countries examined.

METHODOLOGY AND DATA

The export model to be estimated in this study is specified as:

(3)

where XI is the real exports, expressed as the exports divided by the export price; RPI is the relative prices, expressed as the export price over the import price; Y

I is the world

demand; VI is the measure of exchange rate volatility and uJ.1

is a disturbance tenn (Arize et al., 2000). The relative prices and exchange rate volatility are expected to have a negative sign whereas the world demand is expected to have a positive sign.

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WOllf HIICk T~II d ilL

where XI is the real exports, expressed as the exports divided by the export price; RPI is the relative prices, expressed as the export price over the import price; Y

I is the world

demand; VI is the measure of exchange rate volatility and u3,. is a disturbance term (Arize et 0/., 2(00). The relative prices and exchange rate volatility are expected to have a negative sign whereas the world demand is expected to have a positive sign.

This study employs two measures of the world demand. namely (i) the GOP volume of the world (YI.) and (ii) the GOP volume of the industrial countries (Y2,). More speciflCally, the export models to be estimated in this study are:

(4a)

(4b)

where U4~ and us~ are a disturbance term. respecti vely. The above models are named as Model 1 and Model 2, respectively. -

The measure of exchange rate volatility is computed by a moving standard deviation. The moving standard deviation with order three is computed as:

(5)

where el is the official exchange rate. The moving standard deviation is employed

because it is widely used as a measure of exchange rate volatility to examine the impact of exchange rate volatility on international trade. For this measure, Koray and Lastrapes (1989) showed that it captures the temporal variation in the absolute magnitude of changes in exchange rate and therefore exchange rate risk over time.

The Dickey and Fuller (1979) and Phillips and Perron (1988) unit root test statistics are used to examine the stationary of the data. The Augmented Dickey-Fuller (ADF) unit root test statistic is computed by estimating the following auxiliary regression:

(6)

where !J. is the frrst difference operator; Y, is a series being examined; J.l is a drift parameter; t is a time trend; and p is the number of lagged differences included such that the disturbance term. Eu in equation (6) is white noise. Ifp is equal to zero, then equation (6) is equivalence to the Dickey-Fuller (OF) unit root test. The Dickey and

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The Determinants of Exports In Malaysia

Fuller (1979) t-statistic (t) is to test the null hypothesis of a unit root or equivalently to test the coefficient of y = 0 against the alternative hypothesis of y < O. If the null hypothesis is accepted then YI is said to be a difference stationary series.

The Phillips and Perron (1988) unit root test statistic is computed by estimating the following equation:

(7)

where a o is a drift parameter and £2.1 is a disturbance term. The test statistic, Z(t) is computed to test the null hypothesis of a unit root or equivalently to test the coefficient ofa

2= 1.

Pesaran et al. (2001) proposed the bounds testing approach based on the Wald or F-statistic for cointegration analysis. The bounds testing approach does not impose restrictive assumption that all the independent variables are to be integrated of the same order. In other words, independent variable could be 1(0) or 1(1). More specifically, the bounds testing approach is conducted in the following way. Firstly, the unrestricted error correction model (UECM) is estimated:

(8)

where u6

is a disturbance term. .1

Secondly, the Wald or F-statistic is computed to test the null hypothesis, Ho: J36~ = J366 = J367 = J361 = 0 against the alternative hypothesis, HI: J36~;t ~66:;t ~67:;t J361 :;t O. The critical bounds values can be obtained from Pesaran et 01. (2001). If the Wald or F­statistic falls outside the upper bound, the null hypothesis of no cointegration is rejected. In other words, In XI' In RP loin YI and Vlare said to be cointegrated together.

However, no conclusive inference could be made for the Wald or F-statistic falls inside the critical bounds, unless the order of integration of the independent variables is known. If the Wald or F-statistic falls below the lower bound, the null hypothesis of no cointegration cannot be rejected In the presence of co integration, the long run coefficients for In RPI, In YI and VI are respectively derived from J3JJ3w J36!J3" and J361/J365.

The cointegrating rank is restricted to unity on dependent variable given the assumption that the independent variables are assumed to consist oflong-run forcing

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variables dependent variable. Banerjee et 01. (1998) suggested that the test for the long-run forcing variables. In order to test the long-run forcing variables on exports, the following equations are estimated:

+U,.1 (9a)

(9b)

(9c)

where ui.I (i = 7,8,9) is a disturbance term. If the t-test for Ho: P7S EO 0, Ho: PIS = 0, and Ho: P9S .. 0 cannot be rejected then In RP , In Y and V are respectively conflnned to be the

I I I long-run forcing variables for In XI.

The data are annual over the period 1973-2002. The year 1973 is chosen as the beginning of the sample because of the breakdown of the Bretton Woods system, which exchange rates are generally fluctuating. The data for exports was obtained from Ministry of Finance Malaysia. The export price (1995 OK 100) and import price (1995 = 100) were obtained from the World Bank. The world demand measures, namely the gross domestic product (GDP) volume of the world (1995 = 100) and the GDP volume of the industrial countries (1995 = 1 (0), and the official exchange rate (1995 = 1 (0) were obtained from International Monetary Fund (IMF).

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The Determinants of Exports In Malaysia

EMPIRICAL RESULTS AND DISCUSSIONS

The results of the Dickey and Fuller (1979) and Phillips and Perron (1988) unit root test statistics are reported in Table 4. The lag length used to compute the Dickey and Fuller (1979) test statistics is based on Akaike (1973) information criterion. For the Phillips and Perron (1988) unit root test statistics, the results that are reported are based on three truncation lags, which are used to compute the test statistics after considering truncation lags one to three in computing the test statistics. The results ofthe Dickey and Fuller (1979) and Phillips and Perron (1988) unit root test statistics show that all variables are integrated of order one, except exchange rate volatility, which is integrated of order zero.

The results of the Dickey and Fuller (1979) and Phillips and Perron (1988) unit root test statistics show that the present of a mixture ofI( 1) and 1(0) independent variables. Therefore, the Pesaran et al. (2001) bounds testing approach is used. Moreover, most studies focus on short-term volatility. However, it has been recognised that it is long-term swings in real exchange rates or currency misalignments that are more likely to generate uncertainty and exert a significant impact on export volumes (De Vita & Abbott, 2004: 69-70).

The results of the F-statistic for the bounds testing approach (Pesaran et al., 2001) are reported in Table 5. On the whole, all the F-statistics fall outside the upper bound and statistically significant at 1 percent level. Thus, evidence of cointegration among the variables is not rejected. The results of the unrestricted error correction models are reported in Table 6. The results show all the models to have a high adjusted R 2. The adjusted R2 for Modell is 0.68391 and the adjusted R 2

for Model 2 is 0.86288. All the models fulfil the conditions of no-autocorrelation, and normality and homoscedasticity of disturbance terms, except no-functional form for Modell. The long-run relative prices and income elasticities are more than one. Exchange rate volatility is found to have a negative impact on exports.

The results of the testing for long run forcing variables are reported in Table 7. The t-statistic for the coefficient of the lagged variable of the real exports in level falls below the lower critical value in all the cases examined. Thus, the assumption of a unique cointegrating vector among the variables in the export model cannot be rejected.

This study has examined the determinants of exports in Malaysia. The results show that there is a long-run relationship among the real exports, the relative prices and the world demand. More specifically, an increase in the relative prices will lead to a decrease in the real exports. An increase in the world demand would lead to an increase in the real exports. Furthermore, the relative prices and the world demand are respectively price and income elasticities. This implies that exports of Malaysia are sensitive to the chances in the relative prices and the world demand Arize et al. (2000) also reported that the relative prices and the world demand are important for exports of Malaysia.

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Wong Hock Tsen et aL

An increase in costs of production leads to a decrease in the real exports. Therefore, it is important for Malaysia to avoid an increase in wage without a parallel increase in productivity. In order to sustain exports, focus shall be given on exporting high value-added products.

The policy to shift from an input-driven strategy to a productivity-driven strategy is important The contribution of total factor productivity during the Seven Malaysian Plan was 24.8 percent of gross domestic product (GDP) growth, while the contribution oflabour was 25.0 percent and that of capital was 50.2 percent (Malaysia. 2001). This indicated that growth continued to be input-driven, particularly from capital. In the Eight Malaysian Plan, additional measures were adopted to increase productivity, which included the allocation of more resources for research and development. expansion of education and training, and technology improvements (Malaysia, 200 I).

Exports of Malaysia are affected by the world demand. Exports of Malaysia are mainly to industrial countries. Economics recession in those countries will reduce exports demand from Malaysia. As a result. Malaysian economy would recess. In the long run. Malaysia shall diversify its exports not only to the traditional market but also the new market such as ASEAN Free Trade Area (AFTA).

Exchange rate volatility is found to have a negative impact on exports. This finding is consistent withArize et a1. (2000) and De Vita and Abbott (2004), amongst others. Arize et al. (2000) used the Iohansen (1988) cointegration method and reported the adverse impact of exchange rate volatility on exports of Malaysia. De Vita and Abbott (2004) used Pesaran et al. (2001)ARDL bound test approach and reported an increase in exchange rate volatility would lead to a decrease in exports of United States. Thus, generally, an increase in exchange rate volatility would lead to a decrease in exports of a country. Therefore, a more stable exchange rate is important for promoting exports. The move to fix Malaysian ringgit against American dollar is important not only to curb the currency speculative activities to stabilise economic growth but also it is important to promote economic growth. However, Malaysia removed fixed peg of Malaysian ringgit to American dollar on 21- July 2005 in favour of managed float based on a basket of currencies, after fixed peg of its for about seven years, as Malaysian ringgit is getting less volatile and generally people are more confident on its. Further, balance of payment of Malaysia is stronger and economy is growing. Moreover, China has removed fixed peg of its renrninbi to American dollar.

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The Determinants of Exports In Malaysia

CONCLUDING REMARKS

This study has investigated the relationship between the real exports and its determinants in Malaysia. The results of the Dickey and Fuller (1979) and Phillips and Peron (1988) unit root test statistics show that all variables are found to be integrated of order one, except exchange rate volatility, which is integrated of order zero. Therefore, the presence of a mixture of an 1(1) and 1(0) variables in the system. Pesaran et al. (200 1) bound testing approach for cointegration analysis is used. The results show that there is a cointegration among the real exports and its determinants. Moreover, the results of the testing for long run forcing variables show that a unique co integrating vector among the variables in the export model cannot be rejected.

The relative prices and the world demand are respectively price and income elasticities. Therefore, exports of Malaysia are sensitive to changes in prices in the international markets and the world demand. Exchange rate volatility is found to have a negative impact on exports. A more stable exchange rate is important for promoting exports. In tum, it is expected to promote economic growth (Ghatak et al., 1997).

ACKNOWLEDGEMENT

This study is part of the research under the fundamental project grant, A-002-06-ERl U055 ofUniversiti Malaysia Sabah (VMS). We like to thank VMS for fmancing the research. The authors would like to thank TC Tang from Monash University Malaysia for providing the export price and the import price.

REFERENCES

Akaike, H. (1973). Maximum Likelihood Identification of Gaussian Autoregressive Moving Average Models, Biometrika 60(2): 255-265.

Appleyard, D.R. & Field,AJ. (2oo1}.lnternational Economics, 4th Edition, Singapore: McGraw-Hill

Arize, A.C. (1997). Conditional Exchange-Rate Volatility and the Volume ofF oreign Trade: Evidence from Seven Industrialized Countries, Southern Economic Journal 64: 235-254.

Arize,A.C., Osang, T. & Slottje, DJ. (2000). Exchange-Rate Volatility and Foreign Trade: Evidence from Thirteen LDC's, Journal of Business and Economic Statistics 18(1): 1~17.

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Banerjee, A., Dolado, J. & Mestre, R. (1998). Error-Correction Mechanism Tests for Cointegration in Single-Equation Framework, Journal of TIme Series Analysis 19: 267-283.

Brol1, U. (1994 ). Foreign Production and Forward Markets, A ustralian Economic Papers 33: 1-6.

Cbowdhwy,A.R. (1993) Does Exchange Rate Volatility Depress Trade Flows? Evidence from Error-Correction Models, The Review of Economics and Statistics 75: 7~706.

De Grauwe, P. (1988). Exchange Rate Variability and the Slowdown in Growth of International Trade, International Monetary Fund Staff Papers 35: 63-84.

De Vita, G & Abbott, A. (2004). Real Exchange Rate Volatility and US Exports: AnARDL Bounds Testing Approach, Economic Issues 9(1): 69-78.

Dickey, D.A. & Fuller, W.A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root, Journal of the American Statistical Association 74(366): 427-431.

Gagnon, J.E. (1993). Exchange Rate Variability and the Level ofintemational Trade, Journal of International Economics 34: 269-287.

Ghatak, S., Milner, C. & Utkulu, U. (1997). Exports, Export Composition and Growth: Cointegration and Causality Evidence for Malaysia, Applied Economics 29: 213-223.

Goldstein. M. & Khan, M.S. (1978). The Supply and Demand for Exports: A Simultaneous Approach, The Review o/Economics and Statistics 60: 275-286.

Gotur, P. (1985). Effects of Exchange Rate Volatility on Trade: Some Further Evidence, International Monetary Fund Staff Papers 32: 475-512.

Hooper, P. & Kohlhagen, S.W. (1978). The Effect of Exchange Rate Uncertainty on the Prices and Volume of International Trade, Journal 0/ International Economics 8:483-511.

International Monetary Fund (1984). Exchange Rate Volatility and World Trade, International Monetary Fund Occasional Paper 28.

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Johansen, S. (1988). Statistical Analysis of Co integration Vectors, Journal of Economic Dynamics and Control 12(2-3): 231-254.

Koray, F. & Lastrapes, W.D. (1989). Real Exchange Rate Volatility and U.S. Bilateral Trade: A VAR Approach, The Review of Economics and Statistics 71: 708-712.

Kumar, R. & Dhawan, R. (1991). Exchange Rate Volatility and Pakistan's Exports to the Developed World, 1974-85, World Development 19: 1225-1240.

MacKinnon, J.G. (1996).Numerical Distribution Functions for Unit Root and Coiritegration Tests, Journal of Applied Econometrics 11(6): 601-618.

Malaysia (2001). Rancangan Malaysia Kelapan: 2001-2005, Kuala Lumpur: Kementerian Penerbitan Malaysia.

McKenzie, M.D. (1999). The Impact of Exchange Rate Volatility on International Trade Flows, Journal of Economic Surveys, vol. 13, pp. 71-106.

Ministry of Finance Malaysia (Various Issues). Economic Report.

Pesaran, M.H., Shin, Y. & Smith, R.J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships, Journal of Applied Econometrics 16: 289-326.

Phillips, P.C.B. & Perron, P. (1988). Testing for a Unit Root in Time Series Regression, Biometrika 75(2): 335-346.

Table 1: Growth of Exports

Year Growth of Exports (%) 1970 -1979 20.69@ 1980-1989 11.73@ 1990 - 1999 17.27@

2000 17.03 2001 -8.84 2002 5.69

Notes: Exports refer to exports of goods and services. @' Denotes the average growth of exports

Source: Economic Report, Ministry of Finance Malaysia

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Table 2: Composition of Exports (RM million I Percentage)

srrc AYengt Avenge AYengt Avenge Avenge Avenge Awrage A~

(R\1 (e;.) (RM (e/e) (RM (e/e) (R"'f r/.) lWIion) nilUon) nilUon) ailIion)

~I 633.6 5.70 1,853.9 4.67 5,066.4 287 8.117.0 2.29 2 4,635.6 41.55 9,155.5 23.~ 10,m2 6.14 8.757.0 247 3 1,405.5 1260 9,148.5 23.06 15,33&.5 8.70 32,945.3 9.32 4 1,306.2 11.70 4,403.3 11.10 11,353.4 6.44 14,4020 4.07 5 75.0 0.67 554.4 1.40 5,1821 294 15,!R7 4.26 6 1,90l.4 17.04 3,477.9 8.n 15,038.4 8.53 24,9\6.0 7.04 7 746.9 6.70 8.929.3 22.SO 95,806.1 54.30 216,oso.O 6J.()6

8 356.9 3.20 1,9527 4.92 16,098.7 9.12 29,849.3 8.43 9 93.9 0.84 200.5 O.SO 1,693.1 096 3.7543 1.()6

T<UI 11,\55.0 100.0 39.677.0 100.0 176398.9 100.0 353,879.67 100.0

Notes: SITC is the standard international trade code. 0+ 1 - Food, Beverages and Tobacco. 2" Crude Materials, Inedible. 3 ., Mineral Fuels, Lubricants, etc. 4 - Animal and Vegetable Oil and Fats. S ., Chemicals. 6 - Manufactured Goods. 7 - Machinery and Transport Equipment. 8 OK Miscellaneous Manufactured Articles. 9 - Miscellaneous Transactions and Commodities.Source: Economic Report, Ministry of Finance Malaysia.

Table 3:Direction of Exports (percentage)

United The Year States SlO2aDOre Japan Netbertand:s

1980- 13 18 18 4 1989&

1990- 17 18 12 3 19998 2000 18 16 II 4 2001 17 15 II 4 2002 17 IS 10 3

Note: • Denotes the average of percentage of exports. Source: Economic Report, Ministry of Finance Malaysia.

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Hone Kong

2

4

4

4

S

TocaJ Other Unor1s

44 100

46 100

47 100 49 100 SO 100

Th~ Determinants of Exports In Malaysia

Table 4: The Results of the Dickey and Fuller (1979) and Phillips and Perron (1988) Unit Root Test Statistics

t.,-trend Z(t.,) - trend In Xc -2.4529(1) -2.0007(3)

~InXc -4.2024·(0) -5.0879··(3) In RP, -3.1304(0) -3.4281(3)

aIn RP, -5.2978"(1) -7.6414"(3) In Yl, -3.0786(1) -2.9601(3)

a In Yl, -3.7305·(1) -3.7520·(3) In Y2t -3.1771(2) -3.3476(3)

~ln Y2t -3.9132·(3) -3.6030·(3) v, -4.4079··(1 ) -0.4948(3)

~VI -5.2845"(3) -4.9331"(3)

Notes: tT is the Dickey-Fuller (DF) or Augmented Dickey-Fuller (ADF) t-statistic. Z(t) is the Phillips and Perron (1988) t-statistic. Values in parentheses are the lag

7 length used in the estimation of the unit root test statistics. Critical values for t7 Z(t7) with a drift and a time trend (trend) at 1 % and 5% for sample size 35 are -4.24 and -3.54, respectively (MacKinnon, 1996) .•• Denotes significance at 1 percent level. * Denotes significance at 5 percent level.

Table 5: The Results of Bounds Testing Approach for Cointegration

Model 1 2

F-statistic 28.83·· 12.59··

Notes: The critical values for bounds testing approach are from Pesaran et al. (2001). The critical values for unrestrictive intercept and no trend case with three regressors at 5 percent level are 3.23 for lower critical bound and 4.35 for upper critical bound .•• Denotes significance at 1 percent level.

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Table 6: The Results of the Unrestricted Error Correction Models

Model 1 2 constant -.93822 -2.7723

(-1.0597) (-4.1855)" ~ In RP, -.25962 -

(-1.0968) ~ In RPI-I .54033 .99056

(2.2687)· (4.7678)·· ~ In RPI-l .75409 1.1825

(3.6508)" (6.8219)" ~ In RPI-3 - .60548

~In Y, 2.7370 (3.9774)"

1.4004 (2.6301)· (2.5625)·

~ In YI-I -3.1598 -.83459 (-3.3105)·· (-1.5539)

~ In YI-Z - -1.8656 (-3.2988)"

~In YI-3 1.4457 1.0236 (1.8512) (1.8468)

~ VI -.37085 -.71543 (-3.6615)" (-5.4696)"

~ VI-I - .48163 (3.3170)··

~ VI-Z -.065366 -~ In x.-I

(-.45228) - -.47029

(-2.6877)· ~ In Je.-z -.77155 -1.1085

(-3.0526)· (-4.4424)" ~ In Je.-l - -.71953

In Xt-I (-3.9748)"

-.053901 -.079685

InRP'.1 (-.43519) -.75309

(-1.2772) -.88196

(-3.1181)" (-5.1825)" In YI-I .33554 .84071

(.84756) (3.5072)" VI-I -.40096 -1.1455

(-2.4740)· (-4.5046)"

184

TIr~ Determinants of Exports In Malaysia

Notes: Adj. Rl is the adjusted Rl. LM is the Lagrange Multiplier test of error tenn serial correlation. Reset is the test offunctional fonn. Nonnal is the test of the nonnality of error tenn. Hetero is the test of beteroscedasticity. Values in parentheses are the t­statistic .•• Denotes significance at 1 percent level. • Denotes significance at 5 percent level.

Table 7: The Results of Bounds Testing Approach for Co integration

Model In RPI In YI VI

I 2.0316 0.9394 1.7273 2 2.0019 1.9751 2.1254

Notes: The critical values for wuestrictive intercept and no trend case with three regressors at 5 percent level are -2.86 for lower critical bound and -3.78 for upper critical bound.

185

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