InternationalJournalofAgricultural

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1 IJASVM InternationalJournalofAgricultural SciencesandVeterinaryMedicine ISSN: Vol.4,No.2,May2016

2 Int. J. Agric.Sc & Vet.Med Henry Egwuma et al., 2016 Research Paper ISSN Vol. 4, No. 2, May All Rights Reserved AN ECONOMETRIC ANALYSIS OF THE DETERMINANTS OF DEMAND FOR PALM OIL IN NIGERIA Henry Egwuma 1*, Mad Nasir Shamsudin 1, Zainalabidin Mohamed 1, Nitty Hirawaty Kamarulzaman 1 and Kelly Kai Seng Wong 1 *Corresponding Author: Henry Egwuma, henry4him@gmail.com This paper empirically examines the determinants of palm oil demand in Nigeria. Within a time series framework, we adopted the Autoregressive Distributed Lag (ARDL) technique to establish the presence of long-run relationships between palm oil demand and its determinants. We find compelling evidence for the existence of a cointegrating relationship between the variables of interest. Our results further reveal that the price of palm oil and income are the major drivers of both import and domestic demand for palm oil in Nigeria. The study recommends that macroeconomic stabilization strategies and appropriate pricing policies should be designed to ensure the expansion of palm oil market share. Keywords: Autoregressive distributed lag, Cointegration, Domestic demand, Import demand, Nigeria, Palm oil INTRODUCTION Over the past several decades, the palm oil industry has seen significant growth becoming the leader among the major vegetable oils in the world. In 2014, the global supply of vegetable oils stood at 176 million metric tons of which palm oil alone accounted for around 35% followed by soybean oil which contributed about 28% (USDA, 2015). Its superior attributes for a number of food and industrial uses coupled with attractive economic and technological advantages underlie the rise of palm oil in the global oils and fats market. Consequently, there has been heightened interest in palm oil in recent years with a corresponding increase in the number of empirical research on the subject. Relative to other palm oil producing countries, the palm oil industry in Nigeria has performed dismally over the years. From being the world leader in the 1950s till mid-1960s in terms of both production and export of palm oil, Nigeria has ceased to contribute to the world s export of the commodity since the 1970s due to the use of outof-date techniques and over-aged trees. Nigeria 1 Department of Agribusiness and Bioresource Economics, Faculty of Agriculture,Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia. 1

3 lost her position to Malaysia as the world s leading producer and exporter in 1966 and 1971, respectively. According to the United States Department of Agriculture (USDA, 2015) statistics, Nigeria is currently the 5th largest producer of palm oil after Indonesia, Malaysia, Thailand and Colombia. Total production in 2014 is put at 970,000 metric tons per year while domestic consumption per annum is about 1,520,000 metric tons. Consequently, and paradoxically, a once largest producer has turned to a net importer of palm oil sourcing over 550,000 metric tons (that is, about 36% of total domestic consumption) of the commodity from the international market to meet local demand. Figure 1 shows the trend in the supply and demand of palm oil in Nigeria. A major point underscored in this figure is that while there has been a significant rise in domestic consumption over the years, the increase in domestic production has only been marginal. Thus, there is a supply-demand gap in palm oil which is met by imports. With steady population growth and rising per capita income on the one hand and an unimpressive production performance on the other, this supply-demand disparity is likely to continue. Historically, the Nigerian palm oil industry has played a prominent role in the Nigerian economy. Together with other key agricultural oil seeds like cotton and groundnut, the palm oil industry has generated considerable foreign exchange which has facilitated a number of national development programmes (Akanni et al., 2005; and Dada, 2007). However, the discovery of crude oil in the 1960s led to the neglect of the palm oil sector and consequent decline in production. This neglect is further amplified by the comparatively little attention that has been paid to research on the Nigerian palm oil industry. The poor performance of the palm oil sector notwithstanding, it is estimated that palm oil production still provides direct jobs and incomes for about 4 million Nigerians in addition to those Figure 1: Trend in Palm Oil Production, Demand and Imports in Nigeria, Source: USDA (2015) 2

4 involved in processing and marketing (Olagunju, 2008). In addition, the prevailing wide gap between supply and demand of palm oil presents a considerable market opportunity. To tap into the huge potentials of the palm oil industry in Nigeria, however, requires effective policy formulation based on an understanding of the dynamic nature of the structural relationships of palm oil demand and its parameters. Thus, it is important to examine factors that influence palm oil demand in Nigeria with a view to ascertaining the existence of short-run and long-run relationships between palm oil demand and its principal determinants. Achieving this objective using a time series econometrics framework is the central thrust of this paper. The organization of the remainder of the paper is as follows: We start with a brief survey of the literature in section two. The methodology applied in this study is outlined in section three while section four presents and discusses the empirical results. Finally, we conclude the paper in section five. LITERATURE REVIEW Empirical studies on the Nigerian palm oil industry are relatively few, outdated and limited in scope. In addition, the majority of the available studies are mostly descriptive in nature (Kajisa et al., 1997; Dada, 2007; Ekine et al., 2008; Olagunju, 2008; Ayodele, 2010; and Gourichon, 2013). However, there are several studies on the palm oil industry with respect to other major producing and importing countries. For example, Yusoff (1988) estimated a production and trade model for palm oil in Malaysia which consisted of five equations explaining acreage, production, domestic utilization, export demand and price. Annual data covering the period 1961 to 1981 were used and estimates were obtained through a non-linear Two-Stage Least Squares (2SLS) technique. The finding shows that the price of palm oil, Gross Domestic Product (GDP) and time trend are significant determinants of domestic utilization, with GDP exerting the greatest influence. The price of soybean oil and the world economic activity significantly influence export demand. This indicates that soybean oil is a substitute for palm oil. The study by Shamsudin and Arshad (1993) employed 2SLS in a simultaneous equation specification framework to examine the relationship of crucial variables in the Malaysian palm oil market. The 1965 to 1990 sample period was used in the estimation and the results reveal that export demand is determined by own price, substitute price (soybean oil) and the Industrial Production Index (IPI). On the other hand, only the level of economic activity proxied by IPI significantly influences domestic demand while own and substitute prices are insignificant. In their study on the Malaysian palm oil industry over the period 1970 to 1999, Talib and Darawi (2002) utilized both the Ordinary Least Squares (OLS) and 2SLS methods to estimate behavioral equations reflecting the supply and demand blocks of the palm oil model. The demand side consisted of equations explaining domestic utilization, imports, and exports. The findings from both OLS and 2SLS estimates indicate that current domestic consumption of palm oil in Malaysia is influenced by one year lagged consumption and IPI while the own price and coconut oil price, as a substitute commodity, although carrying the expected signs do not determine the level of domestic demand. With regards to palm oil export demand, however, the results show that own and substitute prices, 3

5 world population and Malaysian exchange rates have significant impacts. Furthermore, the IPI and beginning stocks were found to be the significant drivers of palm oil import demand. Particularly important is the negative sign of the coefficient of beginning stocks which suggests that imports are substitutes for declining stocks. Similarly, Shri Dewi et al. (2011) included equations for palm oil imports, exports and domestic demand in their study. With the aim of investigating the nexus between biodiesel demand and the palm oil market in Malaysia, the study included an array of variables and utilized time series data for the period 1976 to Like Yusoff (1988), the estimates were generated using the nonlinear 2SLS estimation approach in a loglinear specification. The results show that while the price of soybean oil and the beginning stocks are respectively, positively and negatively signed as expected, they do not significantly affect palm oil import demand. By contrast, the own price and GDP are significant determinants of import demand. Also, one year lagged domestic demand, the own price and GDP were found to be important factors affecting domestic demand. Export demand is determined by a number of factors including the one year lagged export demand, own and substitute prices, exchange rate, and world GDP. Hameed and Arshad (2012) examined the import demand for five major palm oil importing countries including Bangladesh, China, India, Pakistan and the USA. The study employed annual time series data over the period 1977 to 2010 and the autoregressive distributed lag (ARDL) methodology was used to estimate five single equation models. The results indicate that palm oil import demand across all the countries in the study except India depends on the price of palm oil and substitute prices (soybean and rapeseed oils). The findings also underscore the importance of income in positively shaping palm oil import demand in all the countries except the USA. Furthermore, the exchange rate is an important variable that affects palm oil import demand in China and India while government policies such as import tariff and food labeling requirement play important roles in driving demand in India, Pakistan, and the USA. In a relatively new study on the effects of the Malaysian export duty structure on the palm oil industry, Wong et al. (2014) formulated a model consisting of supply and demand equations. The ARDL technique was applied to analyse the structural equations in the model with all equations expressed in the double-log form. Relevant to the present study is the findings that the lagged one Crude Palm Oil (CPO) domestic demand, the local CPO price, the world price of soybean oil and the IPI have a significant influence on palm oil domestic demand. Additionally, the one-year lagged import demand for CPO and the IPI were found to play significant roles in shaping palm oil import demand in Malaysia. The foregoing survey of the empirical literature highlights a number of important issues. Fist, the choice of variables and the way these variables are represented is important to any econometric analysis on demand for palm oil. There is a battery of variables that previous studies have included in the estimation of palm oil demand models. Secondly, the estimation procedure applied is also very important in accounting for peculiar properties of time series data. In this study, we employ the ARDL estimation methodology which has been found to have several advantages as explained in a later section. Lastly, we are unaware of any existing study with recent data 4

6 and the mentioned estimation procedure that has been conducted on the Nigerian palm oil industry. METHOD AND DATA The Econometric Model As earlier stated, Nigeria is a net importer of palm oil because its local production falls short of domestic utilization. Additionally, Nigeria s export of palm oil has declined considerably over the years and currently its contribution to world exports is negligible. Accordingly,the econometric analysis in this study focuses on domestic and import demand equations only. Based on economic theory, several explanatory variables have been suggested including own price, substitute price, income, exchange rate, and other relevant demand shifters. We consider dynamic specifications for the import and domestic demand equations by incorporating lagged dependent variables. Senteri (1985) citing Labys (1973) opined that specifying a demand function in a static framework presents some setbacks including the inability to distinguish between short-run and long-run demand responses, neglect of conceivable connections with inventory adjustment and failure to include the effects of past levels of demand. This argument is crucial because the responses of consumers to changes in factors influencing demand are not instantaneous but spread over some time frame. Import Demand The derivation of parameters that determine the demand for a commodity is hinged on microeconomic theory of utility maximization (Nicholson and Snyder, 2011). The demand for palm oil imports is defined as a function relating the quantity of palm oil imports to the world price of palm oil, gross domestic product, and Nigerian exchange rate. The quantity of imports lagged one period is included in the model to explain the adjustments of import to price and income changes over time. This relationship can be expressed as follows:...(1) where, NGPCQM = Palm Oil Import Demand in tons PCWP = World Price of Palm Oil in US$/ton NGXR = Nigerian Currency Exchange Rate in N/ US$ GDP = Nigerian Gross Domestic Product in Naira (N) t = Time Period It is expected, a priori, that palm oil imports would be negatively related to PCWP t and NGXR t, while its own lag (NGPCQM t-1 ) and GDP t are expected to be positive. Domestic Demand Palm oil is utilized as an input in the production of final food and non-food items such as margarine and spreads, paints, biodiesel, and other products. Based on the theory of derived demand, the Nigerian palm oil domestic demand is postulated to be related to the domestic producer price of palm oil, Nigerian GDP and the world price of soybean oil (price of a substitute). The domestic demand equation can be written as follows:...(2) where, NGPCQC = Nigerian Crude Palm Oil Domestic Demand in tons 5

7 PCDP = Domestic Price of Palm Oil in N/ton SYWP = World Price of Soybean Oil in US$/ton The a priori expected signs for NGPCQC t-1, GDP t, and SYWP t are positive while own price PCDP t is expected to exert a negative influence on palm oil demand. Model Estimation Procedure The approach used in this study is the ARDL estimation technique which accounts for the time lag effect of the dependent and independent variables in the model. The ARDL approach was formulated by Pesaran et al. (2001). A number of recent studies have used the ARDL approach as a better approach to cointegration than such methods as Engle and Granger (1987) and Johansen and Juselius (1990). The ARDL approach has been recognized to have a number of distinctive merits. First, the ARDL approach is valid irrespective of whether the underlying variables are purely I(0), purely I(1) or fractionally integrated. Second, the long run and short run components of the model can be estimated simultaneously using the ARDL approach. As noted by Banerjee et al. (1993) the ARDL approach can be used to obtain a dynamic Error Correction Model (ECM) through a simple linear transformation of the model. The ECM combines the short run dynamics with the long run equilibrium without losing long run information. Third, unlike most cointegration approaches, the ARDL approach is robust in small samples (such as used in this study). Fourth, to capture the data generating process in a general-to-specific modeling framework, the ARDL approach uses a sufficient number of lags (Shrestha and Chowdhury, 2005) The ECM is a representation of a cointegrated system that makes explicit the idea of moving together over time. To illustrate, consider the model:...(3) where, Q t, X t and Z t represent different time series and t is white noise;, and are the parameters. The unrestricted ECM involves the estimation of the following version of Equation (3):...(4) In the above equation, and are the shortrun coefficients of the ARDL model while the s capture the long run relationships. The maximum lag to be used is represented by p. The ARDL model estimates (p + 1) k number of regressions to obtain optimal lag length for each variable, where k is the number of variables in the equation. Because annual data are used in this study, we select 2 as the maximum order of the lags to be used 1. The ARDL approach involves two stages for estimating the long-run relationship between the variables. The first stage involves testing the null hypothesis of non-existence of the long run relationship given by: H 0 : 1 = 2 = 3 = 0 This hypothesis is tested against the alternative of the existence of a cointegration relationship, that is, the s are jointly different from zero. This is achieved by computing the F-statistic. However, this F-statistic has a non-standard distribution irrespective of whether the variables are stationary or non-stationary. The computed F- statistic is compared to two sets of critical value band tabulated by Narayan (2005). The upper bound assumes that all the variables are I(1) while 1. For quarterly data, 4 is selected as the maximum lag (see Peseran and Pesaran, 1997). 6

8 the lower bound assumes all the variables are I(0). The null hypothesis is rejected in favour of the alternative hypothesis if the computed F-statistic is greater than the upper bound, thus, establishing that the variables are cointegrated. In contrast, if the F-statistic is less than the lower bound we fail to reject the null hypothesis of no cointegration. The result is inconclusive if the F-statistic falls within the two critical values. The second stage of the ARDL estimation procedure involves the estimation of coefficients of the variables in the equation. The lag selection criteria of the model can be based on Akaike s Information Criterion (AIC) or Schwartz-Bayesian Criterion (SBC). However, in this study, we select the model based on SBC as this statistic has been found to have a parsimonious specification that selects the smallest possible lag length (Pesaran and Smith, 1998). Furthermore, we subject our model to various diagnostic tests. The stability test utilizes the cumulative sum of squares of recursive residuals (CUSUMSQ) and the model will be estimated in the double log representation. Hence, the estimated parameters will be interpreted as elasticities. The estimated results are reported in the next section along with the R-squared, t-statistics, F-statistics and the LM test. Sources of Data The sample period covers 1970 to 2011 and the data were extracted from secondary sources including various publications of the Central Bank of Nigeria (CBN), National Bureau of Statistics (NBS), USDA and W orld Bank W orld Development Indicators. RESULTS AND DISCUSSION Unit Root and Cointegration Tests In order to avoid generating spurious regression, it is conventionally required to subject each time series to appropriate unit root tests with a view to characterizing their stationarity properties. We employ two popular unit root test approaches in the literature; the Augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test. The results of these tests are reported in Table 1. The results of the unit root tests indicate that all the variables are integrated of order one, i.e., I(1), which implies that the variables are non-stationary in level but become stationary after first difference transformation. Importantly, this result is robust regardless of the choice of the test applied. Table 1: ADF and PP Unit Root Test Results (with Intercept) Variable Level First Difference ADF PP ADF PP Remark NGPCQM *** *** I(1) NGPCQC *** *** I(1) PCWP *** *** I(1) PCDP *** *** I(1) GDP *** *** I(1) NGXR *** *** I(1) SYWP *** *** I(1) Note: *** denotes significant at 1% significance level. 7

9 Table 2 presents the ARDL Bound test of cointegration for the 2 structural equations used in this study. This test is based on the Wald test or F-statistic and is conducted against the null hypothesis of no cointegration. Thus, a rejection of the null hypothesis will imply the existence of a long-run relationship between the variables. As reflected in Table 2, the null hypothesis of no cointegration is rejected in all the equations, thereby suggesting the presence of a cointegrating relationship among the variables in our equations. Table 2: ARDL Bound Test of Cointegration Equation K F-statistic Narayan (2005) Critical Values I(0) I(1) NGPCQM *** NGPCQC * Note: *** and * denote significant at 1% and 10% levels, respectively. K is number of exogenous variables in the equation. Estimated Long-Run Coefficients Based on the existence of a long-run relationship between the variables, we used the SBC to select the model. The selected model for both equations is ARDL(1,0,0,0) and the results are presented in Table 3 along with the long-run estimates. The estimated equations appear to be satisfactory with regards to the goodness of fit as indicated by the high adjusted R 2 and the expected signs of the coefficients. In addition, the model is robust as it does not violate any of the traditional diagnostics tests namely autocorrelation, normality, heteroscedasticity and functional form. The results show that palm oil imports respond negatively to an increase in the world price of palm oil. The own price elasticity is and shows that a 1% increase in the price of palm oil will decrease palm oil import demand by about Table 3: Summary Results Using the ARDL Approach ARDL Estimates of Import and Domestic Demand Regressor Import Demand Domestic Demand Constant NGPCQMt-1 NGPCQCt-1 PCWPt PCDPt GDPt SYWPt NGXRt Constant PCWPt PCDPt GDPt SYWPt NGXRt *** (-1.585) (3.105) 0.692*** (5.679) * (-1.724) 0.495*** (4.086) * (-1.744) 0.651** 0.118*** (2.313) (2.763) * (-1.791) Estimated Long-Run Coefficients (1.604) *** (-1.261) (5.804) ** (-2.262) * (-1.810) 2.115* 0.234*** (1.776) (3.345) (-1.353) Diagnostic Tests (1.548) Adjusted-R BG-LM 1.994[0.369] 3.310[0.191] JB 0.754[0.686] 2.381[0.304] Note: ***, ** and * denote significant at 1%, 5% and 10% levels, respectively. Figures in parenthesis ( ) are t-statistics while figures in brackets [ ] are p-values. 8

10 2.193%. The coefficient of the exchange rate carries the expected negative sign but is statistically insignificant in the long-run with an elasticity value of Thus, depreciation of the Nigerian currency (Naira) will cause the international price of palm oil to increase thereby decreasing palm oil import demand. The findings from this study are similar to the own price and income elasticity values obtained by Hameed and Arshad (2012) and Elbeydi (2013). All the estimated coefficients of the variables in the domestic demand equation have theoretically admissible magnitudes and with the expected signs. However, only own price and income have statistically significant influence on local palm oil demand. The positive sign for the estimated GDP coefficient is in line with the argument of trade theory and also points to the fact that palm oil is a necessity good; as their income increases, consumers would consume more palm oil. This finding is consistent with the argument that consumers in developing countries such as Nigeria devote a larger chunk of an increase in income on food relative to consumers in developed countries. The estimated coefficient of the world price of soybean oil is statistically insignificant but has the correct sign with a longrun cross-price elasticity of This crossprice elasticity indicates that soybean oil is a substitute for palm oil. However, the relationship seems to be tenuous as evidenced by the low cross-price elasticity. The parameter stability test based on the cumulative sum of squares of recursive residuals (CUSUMSQ) indicates that our model for palm oil import and domestic demand in Nigeria is dynamically stable (Figures 2 and 3). Short-Run Dynamics The estimated coefficients of the error correction Figure 2: Stability Test Result for Import Demand Figure 3: Stability Test Result for Domestic Demand model of the selected ARDL equations are reported in Table 4. The estimated coefficient of the Error Correction Term (ECT) lagged one period is negative and highly statistically significant in both the import and domestic demand equations. These results offer further confirmation of the presence of long-run relationships among the variables in the equations. Specifically, the coefficient of the lagged ECT for import demand was and implies that 40.5% of the disequilibrium is corrected per year. In other words, any short-run deviation will take about 2.5 9

11 Table 4: Short-Run Dynamic Error Correction Representation for the Selected ARDL Models Regressor Constant PCWP t PCDP t GDP t SYWP t NGXR t ECT t-1 Import Demand ( NGPCQM t ) Note: ***, ** and * respectively denote significant at 1%, 5% and 10% levels. Figures in parenthesis ( ) are t-statistics. indicates the first difference of variables and denotes the error correction term. years to adjust to long-run equilibrium. In the case of domestic demand, about 57% of the disequilibrium from the long-run is corrected in every period; thus, the adjustment to long-run equilibrium is quite fast. CONCLUSION Domestic Demand ( NGPCQC t ) (-0.910) (0.106) ** (-2.182) (-1.043) 1.340* (1.746) (0.452) (-1.482) (0.977) *** *** (-4.143) (-3.933) This paper utilizes econometrics techniques under the ARDL approach to cointegration to examine the determinants of demand for palm oil in Nigeria. The paper also estimates error correction models to further establish the existence of cointegration among the variables. The results reveal that there is a long-run relationship between the quantity of import and domestic demand and their determinants. The price of palm oil and income level were found to be important determinants of palm oil demand. Thus, it is imperative for policymakers to adopt appropriate pricing policies that will position the palm oil industry in Nigeria to expand its market share. Additionally, relevant mechanisms should be put in place to ensure the stability of the macroeconomic environment that will guarantee income growth of consumers. REFERENCES 1. Akanni K, Adeokun O and Akintola J (2005), Effects of Trade Liberalisation Policy on Nigerian Agricultural Exports, Journal of Agriculture and Social Research (JASR), Vol. 4, No. 1, pp Ayodele T (2010), African Case Study: Palm Oil and Economic Development in Nigeria and Ghana; Recommendations for the World Bank s 2010 Palm Oil Strategy, pp Initiative for Public Policy Analysis, Lagos, Nigeria. 3. Banerjee A, Dolado J J, Galbraith J W and Hendry D (1993), Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data, Oxford University Press Catalogue. 4. Dada L A (2007), The African Export Industry: What Happened and How Can it be Revived? Case Study on the Nigerian Palm Oil Industry, FAO, Agricultural Management, Marketing and Finance, Working Document, p Ekine D, Onu M and Unaeze H (2008), Marketing of Palm Oil in Ikwerre and Etche Local Government Areas of Rivers State, Nigeria, Journal of Agriculture and Social Research (JASR), Vol. 6, No. 1, pp Elbeydi K (2013), The Determinants of Libyan Red Meat Import Demand Equation 10

12 for the Period ( ), Paper Presented at the 2013 AAAE Fourth International Conference, September 22-25, Hammamet, Tunisia. 7. Engle R F and Granger C W (1987), Cointegration and Error Correction: Representation, Estimation, and Testing, Econometrica: Journal of the Econometric Society, pp Gourichon H (2013), Analysis of Incentives and Disincentives for Palm Oil in Nigeria, Technical Notes Series, MAFAP, FAO, Rome. 9. Hameed A A A and Arshad F M (2012), An Empirical Analysis of the Import Demand for Palm Oil in the Five Leading Importing Countries, International Review of Business Research Papers, Vol. 8, No. 7, pp Johansen S and Juselius K (1990), Maximum Likelihood Estimation and Inference on Cointegration with Applications to the Demand for Money, Oxford Bulletin of Economics and Statistics, Vol. 52, No. 2, pp Kajisa K, Maredia M K and Boughton D (1997), Transformation versus Stagnation in the Oil Palm Industry: A Comparison Between Malaysia and Nigeria, Michigan State University, Department of Agricultural, Food, and Resource Economics. 12. Narayan P K (2005), The Saving and Investment Nexus for China: Evidence from Cointegration Tests, Applied Economics, Vol. 37, No. 17, pp Nicholson W and Snyder C (2011), Microeconomic Theory: Basic Principles and Extensions, Cengage Learning. 14. Olagunju F (2008), Economics of Palm Oil Processing in Southwestern Nigeria, International Journal of Agricultural Economics and Rural Development, Vol. 1, No. 2, pp Pesaran M H and Shin Y (1998), An Autoregressive Distributed-Lag Modelling Approach to Cointegration Analysis, Econometric Society Monographs, Vol. 31, pp Pesaran M H, Shin Y and Smith R J (2001), Bounds Testing Approaches to the Analysis of Level Relationships, Journal of Applied Econometrics, Vol. 16, No. 3, pp Senteri Z B (1985), An Econometric Analysis of the United States Palm Oil Market, Unpublished Ph.D., Dissertation, The Ohio State University. 18. Shamsudin M N and Arshad F M (1993), Malaysian Palm Oil Market Model, Malaysian Agricultural Commodity Forecasting and Policy Modelling, Center for Agricultural Policy Studies. 19. Shrestha M B and Chowdhury K (2005), ARDL Modelling Approach to Testing the Financial Liberalisation Hypothesis. 20. Shri Dewi A A, Arshad F M, Shamsudin M N and Hameed A A A (2011), An Econometric Analysis of the Link Between Biodiesel Demand and Malaysian Palm Oil Market, International Journal of Business and Management, Vol. 6, No. 2, p Talib B A and Darawi Z (2002), An Economic Analysis of the Malaysian Palm Oil Market, Oil Palm Industry Economic Journal, Vol. 2, No. 1, pp

13 22. USDA (2015), United States Department of Agriculture Online Data, available at: navid=data_statistics 23. Wong K K S, Shamsudin M N, Mohamed Z and Sharifuddin J (2014), Effects of Export Duty Structure on the Performance of the Malaysian Palm Oil Industry, Journal of Food Products Marketing, Vol. 20 (sup1), pp Yusoff M B (1988), Production and Trade Model for the Malaysian Palm Oil Industry, Asean Economic Bulletin, pp

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