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Choosing an Estimation Methodology forNatural Rubber Price Forecasting Models

By

AYE AYE KHIN, ZAINAL ABIDIN MOHAMED, MAD NASIR SHAMSUDIN,

EDDIE CHIEW FOOK CHONG

Institut Kajian Dasar Pertanian dan Makanan

Universiti Putra Malaysia

43400 UPM Serdang, Selangor, Malaysiahttp://www.ikdpm.upm.edu.my

January 2011


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Choosing an Estimation Methodology for Natural Rubber Price Forecasting Models

Aye Aye Khin 1, Zainalabidin Mohamed 2, Mad Nasir Shamsudin 2,

and Eddie Chiew Fook Chong 21Faculty of Management, Multimedia University (MMU), Cyberjaya, Malaysia

2 Faculty of Agriculture, Universiti Putra Malaysia (UPM), Serdang, Malaysia

[email protected], [email protected], [email protected], [email protected]

Abstract

This study developed a short run econometric model of price, supply and demand of Malaysian

natural rubber. Both single and simultaneous equations will be utilized using monthly data from

January 1990 December 2008 as estimation period and data from January 2009 June 2009 will

be used as an ex-ante forecast. The data were tested for unit root and Vector Error Correction and

co-integration method was used to estimate the parameters of the model. The models

specifications were developed in order to discover the inter-relationships between NR production,

consumption and prices of SMR20 and to determine forecast price of SMR20. Comparative

analysis between the single-equation specification and simultaneous supply-demand and price

equation were made in terms of their estimation accuracy based on RMSE, MAE and (U-Thile)

criteria. The models, solved dynamically for ex ante forecasts over for the period of January 2009 -

June 2009 as indicated earlier. The results revealed that the values of the RMSE, MAE and U of

simultaneous supply-demand and price equations model were comparatively smaller than the values

generated by the single-equation model. These statistics suggested that the simultaneous equation of

supply-demand and price model was more accurate and efficient measured in terms of its statistical

criteria than the single-equation model in predicting the price of SMR20 in the next 6 months or so

Keyword: Simultaneous supply-demand and price model, econometric, Root Mean Squared Error

(RMSE), Mean Absolute Error (MAE), Theils Inequality Coefficients (U) criteria, Natural Rubber

Introduction

Natural Rubber (NR) is now produced almost exclusively in developing countries and South-east

Asia is the largest producing region. Thailand was the largest producer with an annual productionof 2.69 million MT (30.3 percent of Worlds NR production) in 2006. However, in 2008, Indonesia

has become the largest producer at 2.73 million MT (29.3 percent of Worlds NR production),

followed by Thailand at 2.63 million MT (28.2 percent) and Malaysia at 1.29 million MT (13.8

percent) (IRSG, 2008). On consumption, China was the largest consumer at 2.33 million MT, (26.2

percent of worlds NR consumption), followed by U.S.A at 1.89 million MT (10.5 percent) and

Japan at 1.03 million MT (9.1%) in 2008 (Table 1).


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Table 1. World Natural Rubber Production/Consumption and Supply Surplus/Deficit(000 MT unless otherwise indicated)

Countries 2004 2005 2006 2007 2008

Thailand 2,984 2,833 2,690 2,580 2,633Indonesia 2,066 2,271 2,450 2,600 2,733Malaysia 1,169 1,126 1,200 1,240 1,288India 743 772 830 860 890Others 1,672 1,702 1,720 1,760 1,823

World Supplya 8,634 8,703 8,890 9,040 9,340% change 8.1 0.8 2.1 1.7 3.3

China 1,630 1,826 1,990 2,150 2,325North & Latin America 1,810 1,848 1,850 1,850 1,890Japan 815 857 900 950 1,025India 745 789 840 880 925Africa 123 121 120 120 120Europe Countries 1,491 1,560 1,610 1,530 1,710

World Demanda 8,343 8,777 9,150 9,510 9,880% change 4.7 5.2 4.3 3.9 3.9

World Supplya 8,634 8,703 8,890 9,040 9,340

World Demanda 8,343 8,777 9,150 9,510 9,880

Surplus/Deficit 291 - 74 - 260 - 470 - 540aRounded to nearest 10,000 metric tones (MT)

Source: (IRSG, 2008)

World natural rubber supply was 8.6 million MT in 2004 and estimated to increase to 9.3 million MT in

2008. Conversely, world natural rubber demand was 8.3 million MT in 2004 and estimated to reach 9.9

million MT in 2008. The year 2008 shows a deficit situation in the world natural rubber supply at -0.54

million MT in 2008 (see Table 1). However, due to the current global recession, the International

Rubber Study Group (IRSG) has projected that world natural rubber supply will be only 7.87 million

MT in 2010. It is projected that total natural rubber production in Asia alone would reach 6.84

million MT with an annual growth rate of 1 percent by 2010. Conversely, world natural rubber demand

is projected at 7.91 million MT at an annual growth rate of 1.3 percent in 2010. Likewise, projections

of total rubber consumption in Asia would reach 3.88 million MT with an annual growth rate of 2.8

percent by 2010 (IRSG, 2009).

In Table 1 above, although NR production increased over the period 2004-2008, the NR situation

was unable to meet increasing global demand due to declining planted area, labour shortage, aged

smallholders, uneconomic-size holdings, low productivity, diversification away from rubber and

inadequate resources (Kamarul and Damardjati, 2009). Table 2 shows the new and replanted area

in major rubber producing countries as well as other countries over the period 2003-08. Out of

1,189,000 hectare of new planting area during 2003-08, about 88 percent (or 1,058,000 hectare)

was undertaken from 2005 onwards. On the other hand, out of 763,000 hectare of area replanted

during 2003-08, about 77 percent (or 588,000 hectare) was carried out from 2005 to 2008. Potential

impact of newly and replanted area on global NR supply cannot exert any significant impact until 2011,

due to low addition in new and replanted area planted during 2003-04. So the addition to tappable

area during 2009-2010 would be low. Although the 2005-08 new planting and replanted rate was

high, these cannot reach tappable age before 2011 due to the gestation lag.


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Table 2. Natural Rubber New Planted area and Replanted area during 2003-08 (000 ha)

Countries 2003-04 2005-08 Total

New Planted Replanted New Planted Replanted New Planted Replanted

Thailand 49 81 352 154 401 235Indonesia 0 10 171 195 171 205Malaysia 0 39 11 105 11 144India 23 15 99 45 122 60Vietnam 38 7 195 32 233 39Sri Lanka 1 3 10 17 11 20Other countries 20 20 220 40 240 60

Total 131 175 1058 588 1189 763Source: (ANRPC and MRB, 2009)

Table 3 shows that the stock of world natural rubber was 2.3 million MT in 2004 and it declined to

2.0 million MT in 2008 and it was the lowest level since 2004. Moreover, the stock of natural rubber

shows a decrease situation in year-on-year terms during this period and a decrease situation of the

world natural rubber stock was -0.038 million MT in 2008 (or the percent change of 1.8 percent) as

compared with 2007.

Table 3. World Natural Rubber Stocks (million MT)

Year 2004 2005 2006 2007 2008

1 Qtra 2,277 2,389 2,425 2,388 2,1542 Qtra 2,192 2,231 2,110 1,921 1,8163 Qtr

a 2,379 2,275 2,080 1,874 1,925

4 Qtra 2,413 2,258 2,084 2,064 2,201

Year 2,315 2,288 2,175 2,062 2,024

Increase/Decrease 240 -27 -113 -113 -38

percent change (%) 11.6 -1.2 -4.9 -5.2 -1.8aRounded to nearest 10,000 MT


Changes to the world stock situationwill also affect the price, supply and demand of world natural

rubber. An inverse relationship between NR price and stock levels is implied because prices tend

to peak during low levels of stock and vice versa in 2004 to 2008. As was evident, the decline in

stocks during this period led to a rise in rubber prices. The recession in world natural rubber price

started on a down trend from late July 2008 with recovery commencing as early as 2009 (Figure 1).

It was due to the slow production recovery after wintering (mainly due to heavy rains in Thailand,

Malaysia and Vietnam) and low underlying global demand as well as low demand from China and

India on the market sentiment over signs of an easing of the global economy recession during this

period. However, Malaysian natural rubber price was increased by 57 percent (US$ 1778.41 per MT) atthe end of May 2009 from their low of US$ 1376.57 per MT in December 2008. Besides, NR prices

broadly followed the same trend as crude oil prices (COP). Moreover, crude oil prices will most likely

be the determinant of direction in the natural rubber market with expensive crude oil prices keeping

synthetic rubber prices high and also affected to the tire manufacturers and the primary consumers

of natural rubber. The impact of higher oil prices on commodities is complex as it not only raises

production costs but also pushes up demand for biofuels. If oil prices stay high, major international


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tire makers will switch to natural rubber. Butadiene, a petroleum by-product, is the main raw

material for producing synthetic rubber. In mid-2008, supply-related concerns fueled a rally in

134.52 US$ per barrel of crude oil prices, leading to a surge in 3443.60 US$ per MT of Malaysian

synthetic rubber prices. The price of crude oil has been on an upward move again (Figure 1), rising

from its lowest level in over five years of US$ 40 per barrel in December 2008 to the current price of

US$ 61.02 per barrel in late May, 2009 and also the synthetic rubber price falls down to US$1551.63 per MT in December 2008 (IRSG, 2009).

0

500

1000

1500

2000

2500

3000

3500

4000

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

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2008

Observations

SMR20(US$/MT),

SyntheticRubber

Price(US$/MT)

0

20

40

60

80

100

120

140

160

Cru

deOilPrice(US$/barrel)

SMR20 (US$/MT) Synthetic Rubber Price (US$/MT) COP(US$/barrel)

Figure 1. Crude Oil, Malaysian Natural Rubber and Synthetic Rubber Prices in January 1990 to

December 2008. (Source: IRSG and Economics and International Division, Ministry of Finance, Malaysia, 2009)

Exchange rates, especially the depreciation of the US$, have contributed to the upward pressure

on world prices for most commodities traded in dollars (Short-term Economic Outlook of OECD-

FAO, June 2008). The US$would be continued to depreciate against most major currencies and,

for this paper, NR price is expressed in that currency of US$. If the price is expected to have

negative relationship with the exchange rate for Malaysia RM per US$ (RM/US$), indicating that

less Malaysia RM would be paid for an US$ when the NR price would be high again as

experienced during the forecasting period. Table 4 shows that Malaysian natural rubber price and

exchange rate relationship and natural rubber price was 2314.51 US$ per MT in June, 2007 and it

decreased to 1376.57 US$ per MT in December 2008. It clearly shows that when the NR price wasextremely low prices experienced in December 2008, indicating that more Malaysia RM was paid

for an US$ in the Malaysia natural rubber market.

Table 4. Natural Rubber Price and Exchange Rate

Year 2007-06 2007-12 2008-01 2008-06 2008-12

Natural Rubber Price (US$/MT) 2314.51 2653.55 2765.55 3457.58 1376.57Exchange Rate (RM/US$) 3.38 3.35 3.29 3.22 3.42



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Hence, price forecasting mechanism is necessary for the market participants to guide them in their

production, consumption and financing decisions. An accurate price forecasting is particularly

important to facilitate efficient decision making as there is a considerable time lag between making

output decisions and the actual output of the commodity in the market.

Meyanathan (1979) only focused on the supply characteristics of the world natural rubber industryand three separate economic forces which influenced current NR output. They were long-run in

nature, associated with acreage decisions made years prior to harvesting, the medium-runfactors

associated with yield and the short-run factors which influenced current yields. It specifically

elaborated on the short-run factors, and these were in turn utilised in the estimation of monthly

supply functions for the main producing countries. A short-runrelationship was postulated whereby

current supply St was related to lag output prices P, a time trend T in an attempt to capture the

intensity of harvest and the dummy variables for seasonal adjustments Xt, where i= 2 to 12 indicating

ith month and the short-run supply equation was as follow:

St= S (P, T, Xt)

The results indicated that a positive response to price and supply function in the study. The price

variable was significant, with the expected sign for prices lagged one to three months, indicating

the important role of prices in the production process. The time trend variable was also significant,

reflecting technological improvements which increased yields during the estimation period. All the

seasonal variables were significant, showing marked seasonality in production throughout the year.

World NR prices were used to forecast by using econometric model of the world natural and synthetic

rubbers market (Tan, 1984). The first part of the study was concerned with the specification,

estimation and validation of an econometric model of the world natural and synthetic rubber

market. The second part of the study was concerned the application of the model to forecast

natural rubber price and to analyse the implications of natural rubber price stabilisation along the

lines of the International Natural Rubber Agreement. The results showed that of the explanatory variables

identified, stock of NR in consuming countries ('000 tons) (SCCt), consumption (demand) of NR

('000 tons) (CONt) and price of NR (PNRt) (for the study, SMR20 spot price in sen/kg) in the previous two

periods, were the most important explanatory variables in the NR price forecasting model.

Barlow, Jayasuriya and Tan (1994) presented a broad economic framework and the overall rubber

industry where the supply of rubber was determined by the expected price in the market place,

together with its production capacity, input costs, and underlying technological progress. It then

interacted in a dynamic and recursive manner with demand. Demand was set by the expected

rubber price as well as by the income level in the overall economy, prices of rubber substitutes,

and prices of final goods, technology, consumer preferences, stocks, and manufacturing capacity

utilisation. They also explained that the organisational structure of production, marketing and


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consumption, and government measures towards rubber were also important, but they entered the

rubber framework through the mentioned supply and demand factors. This theoretical framework

was a good starting point for discussion and perceptive of the general rubber economy, with the

opportunity of using some of these factors later in this paper for the estimation of rubber prices.

Fatimah and Zainalabidin (1994) examined the forward pricing efficiency of the local crude palm oil(CPO) futures market. The forward pricing efficiency was measured in terms of the forecasting ability of

Malaysian crude palm oil futures price on physical price. The relative predictive power of futures price

was compared with the various forecasts estimated from proven forecasting techniques like moving

average, exponential smoothing, Box Jenkins and econometric. The result showed that CPO prices

were highly sensitive to changes in stock level and the prices were significantly related to the stock

levels, total consumption and lagged price. They suggested that the market utilised and processed

information efficiently, hence the price discovered at any point in time, can be taken as reflecting the

current supply and demand for the local crude palm oil (CPO) futures market. From the study, the

various forecasts estimated from proven forecasting techniques could provide relevant information of

the forecasting ability for this paper.

Multiple forecasts for autoregressive-integrated moving-average (ARIMA) models are useful in

many areas such as economics and business forecasting. Mad Nasir and Fatimah (1998) provided

some short term ex ante forecasts of Malaysian crude palm oil prices. The forecasts were derived

from the univariate autoregressive integrated moving average (ARIMA) model which integrates a

multivariate autoregressive-moving average (MARMA) model for the residuals into an econometric

equation estimated beforehand. The results showed that the MARMA model produces a relatively

more efficient forecast than the univariate and other econometric models. The forecast figures were

discussed in relation to the current and expected fundamentals of the palm oil market.

Several attempts have been made to forecast the long-term and short-term natural rubber market

(Burger and Smit, 1997 and 2000). The essential elements of NR long-term supply modelare: new

planting, replanting and uprooting area, the age of the area and the yield profiles, technical

progress, other factors influencing normal production and prices. The explanatory variables of NR

long-term demand model are included the total rubber consumption, total tyre production, total

general NR products, GDP (Gross Domestic Product), population size in the particular region, total

vehicle production, total vehicles in used, the ratio of total tyre production to total vehicles in used

and prices. The important exogenous variables of NR long-term price modelare: NR production

per country or region, NR consumption per country or region and changes in stocks. The short-

term supply model (the log of the ratio of actual and normal production) was included the

endogenous variable namely, the log specification related to seasonal dummies, the log of the ratio

of the world market price of NR converted into local currency and adjusted for export duties of the


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particular country and the domestic consumer price-index. The variables used for short-term

demand model(the log of the NR share in total world rubber consumption) namely, the log of the

ratio of the price of NR (in US$) and the US export unit value of SBR (Styrene-Butadiene Rubber)

(in US$) and the log of the trend. The short-term price model of Singapore RSS1was included world

natural rubber production, world total rubber consumption, exchange rate, private world stocks,

and a dummy (taking in time trend). Therefore, the study included the economies of key players inthe natural rubber market both on the demand side, on the supply side and price fluctuations.

Moreover, it aims at providing an empirical conceptual framework basis for establishing a link

between the economic theorisation and the empirical work as contained in this study.

Lim (2002) estimated the short-term NR prices and evaluated the relative performance of 19 models

based upon three different forecasting techniques, and four information sets. The generalized

autoregressive conditional heteroscedasticity regression (or ARCH-type) models were generally

better than the simple regression models and the results can potentially be beneficial to

participants in the NR futures market.

Krichene (2005) has argued that a relationship exist between crude oil prices, changes in the nominal

effective exchange rate(NEER) of the U.S. dollar, and the U.S. interest rates. The study used the

simultaneous equations model (SEM) for world crude oil and natural gas markets and found that

both interest rates and the NEER were shown to influence crude prices inversely. The result

explained that demand and supply for both crude oil and natural gas were highly price inelastic in

the short run, leading to excessive volatility in crude oil and natural gas market. From the study, a

SEM model estimation methodology could provide realistic and relevant information for this paper.

This paper presents a short run econometric model of price, supply and demand of Malaysian

natural rubber. Both single and simultaneous equations will be utilized using monthly data from

January 1990 December 2008 as estimation period and data from January 2009 June 2009 will

be used as an ex-ante forecast. The data were tested for unit root and Vector Error Correction and

co-integration method was used to estimate the parameters of the model. The models

specifications were developed in order to discover the inter-relationships between NR production,

consumption and prices of SMR20 and to determine forecast price of SMR20. Comparative

analysis between the single-equation specification and simultaneous supply-demand and price

equation were made in terms of their estimation accuracy based on RMSE, MAE and (U-Thile)

criteria. The models, solved dynamically for ex ante forecasts over for the period of January 2009 -

June 2009 as indicated earlier.


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Methodology

Conceptual Framework

Natural rubber models were actually based on the supply and demand theory and they generally

consisted of a number of components, which reflected supply, demand and price determinants(Burger and Smit, 1997 and 2000). They argued also that, however, the analysis of commodity

price behavior was normally divided between the long-term price, which could be termed the

equilibrium or trend price, and the short-term price, which was associated with speculation and

cyclical or random price movements. On the other hand, the study will develop some short-term ex-

ante forecasts of the single and simultaneous equations of natural rubber supply, demand and price

methodology in the world market. Also, the models will be determined and estimated the price of

SMR20 which between the single-equation specification and simultaneous supply-demand

equation is more efficient and analyse and compare individually in terms of their estimation

accuracy. The framework will identify the appropriate variables and forecasting techniques to be

used for the monthlyshort-term ex-ante forecastof this forecasting study.

Burger and Smit (1997 and 2000) conducted the structure of short-term natural rubber models are

conceptually using quarterly data that world NR supply, demand and prices would be used to

forecast by using econometric model of the world natural rubber market. The short-termsupply model

(the log of the ratio of actual and normal production) was included the endogenous variable

namely, the log specification related to seasonal dummies, the log of the ratio of the world market

price of NR converted into local currency and adjusted for export duties of the particular country

and the domestic consumer price-index.

The short-term supply modelwas hereby constructed. Its purpose provided a theoretical basis for

establishing a link between the NR supply and the factors as contained in this study. The short-

term supply functionwas as follow:

log qqt= (log pt, log qqt-1, dt, et)

where,

log qqt = the log of the ratio of actual production and normal production

log pt = the log of the ratio of world market price of NR, converted into local currency and adjusted

for export duties of the particular country and the domestic consumer price-index

dt = the dummy variables taking the value of 1 in the first, second and third quarter of each year,

respectively and the value of zero otherwise.

The variables used for the short-term demand modelwere the log of the NR share in total world

rubber consumption, the log of the ratio of the price of NR (in US$) and the US export unit value of


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SBR (Styrene-Butadiene Rubber) (in US$) and the log of the trend. The short-term demand function

was as follow:

log snwt= (log pt, log snwt-1, logt, et)

where,

log snwt = the log of NR share in total world rubber consumption

log pt = the log of the ratio of the price of NR (in US$) and the US export unit value of SBR(Styrene-Butadiene Rubber) (in US$)

log t = the log of the trend

The NR short-term price model included world natural rubber average production, world total

rubber consumption, price index of minerals, ores and metals, real exchange rate, private world

stocks, the lag of NR price (US$/tonne) and a dummy (taking in time trend). It included the

economies of key players in the natural rubber market both on the demand side, on the supply side

and price fluctuations. Moreover, the short-run NR supply response to price was not certain due to

factors such as the economic and weather conditions that can be very different from country to

country. This was accounted mainly by its inflexible capacity, long gestation period and the

existence of a very large number of smallholding tappers, for many of whom the rubber production

constituted the only source of income due to the relative scarcity of attractive alternatives.

The short-term price function(in logs) was as follow:

psdt= (pmomt-1, psdt-1, xsdrt-1, ctwt-1, sqt-1, zndzbt-1, dt, et)

where,

psdt = NR price in US$/tonne

pmom = price index (current US$) of minerals, ores and metals

xsdr = real exchange rate between US$ and SDR (US$/SDR)

ctw = world total rubber consumption (1000 tonnes), adjusted for seasonal fluctuations (1000

tonnes)

sq = average world production of quarters t-3 through t (1000 tonnes)

zndzb = znpt-1 zbwt + zbwt-1, where znp are private world stocks (1000 tonnes), seasonally

adjusted; zbw is the total world buffer stock (1000 tonnes)

dt = the dummy variables taking the value of 1 in the first, second and third quarter of each

year, respectively and the value of zero otherwise.

The review of the single-equations of supply, demand and price relationship were based on earlier

studies developed by Meyanathan (1979), Tan (1984), Fatimah and Zainalabdin (1994), Barlow,

Jayasuriya and Tan (1994), Mad Nasir et al. (1998), Ferris (1998), Burger and Smit (1997 and

2000) and Lim (2002). For this model, short-term ex-ante forecasts of the single-equations of

econometric models of monthlynatural rubber supply, demand and price methodology is described


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and it consists of three behavioral single-equations.

Supply

The supply of natural rubber (TPNR) is a function with related factors (in logs) as follows:

TPNRt = (PSMR20t-i, TPNRt-i, T, e ti) (1)

where:TPNR = Total production of natural rubber (Total Supply) (000 metric tonnes) (MT)

PSMR20 = Real monthly price of SMR20 in Malaysia (US$ /MT) deflated by the CPI.

T = Time trend, 1990 Jan: to 2008 Dec:

ei = error terms

Demand

The demand of natural rubber (TCNR) as a function of the related factors (in logs) as follow:

TCNRt = (PSMR20t, RSS1t, TCNRt-i, T, eti ) (2)

where:

TCNR = Total consumption of natural rubber and synthetic rubber (Total Demand) (000 MT)


RSS1 = Real monthly price of RSS1 in New York (US$ /MT) deflated by the CPI.


ei = error term

Price

From the NR price (PSMR20) determination single-equation, which was derived based on the

related factors (in logs), we have:

PSMR20t= (TPNRt, TCNRt, STONRt, COPt, EXMt,PSMR20t-i, T, eti) (3)

where:


TPNR = Total production of natural rubber (Total Supply) (000 metric tonnes) (MT)

TCNR = Total consumption of natural rubber and synthetic rubber (Total Demand) (000 MT)

STONR = World total stock of natural rubber (000 MT)

COP = Crude oil monthly price (US$/barrel)

EXM = Real monthly average exchange rate (Malaysia Ringgit (RM) per US$) (RM/US$)


ei = error term

Therefore, the single-equation econometric models of the short-term supply, demand and price


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forecasting can be specifically described of the Malaysian natural rubber market in Figure 2.

Figure 2. The Short-term Supply, Demand and Price Forecasting Model in the Malaysian NaturalRubber Market.(Source: Own Findings)

Based on earlier studies, the price forecasting model equations can come from many sources: they

can be simple identities, they can be the result of estimation of single-equations, or they can be the

result of estimation using any one of multiple equation estimators. As mentioned when discussing

the specification of the price forecasting single-equation model in verbal terms as spelled out in the

previous sections, it is the intention to estimate and analyse the relationship between the prices of

natural rubber and total production of natural rubber, total consumption of natural rubber and synthetic

rubber, world total stock of natural rubber, and to include the crude oil monthly price and real monthly

average exchange rate as an important explanatory variables. The price forecasting single-equation model

will be used to ex-anteforecast of the short-term monthly natural rubber price of SMR20 (US$ /MT) in

the Malaysian Natural Rubber market.

MalaysianCurrent NR

Price

Current WorldTotal Stock

Current TotalProduction of

NR

Current TotalConsumptionof NR & SR

Real Price ofNR

World Price

Real AverageExchange

Rate

Crude OilPrice

Real Price ofNR

Real Price ofRSS1


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Therefore, in the previous section it was primarily shown with single-equation models of supply,

demand and price of NR. Here, it is needed to contemplatively justify of the price forecasting

single-equation model specification and perhaps also some comparisons with other model

specifications which are for the forecasting performance of the estimated model is satisfactory and

to diagnose the variation in the errors in a set of forecasts. Moreover, in a single-equation model,

the dependent variable is related to a set of explanatory variables and they do not explain theinterdependencies that may exist between the explanatory variables or show how these

explanatory variables are related to other variables. In addition, single-equation models explain

causality only in one direction; i.e., explanatory variables determine a dependent variable, but there

is no feedback relationship between the dependent variable and the explanatory variables.

Therefore, the standard econometric issues related to the identification in simultaneous supply-

demand equation model which is clearer to explain for the interrelationships within a set of

variables and how the problem of endogeneity occurs. It means that whether the independent

variable is correlated with the error term in the model or not. Also, the variables refer to a lagged or

contemporaneous observation and to improve communication and presentation of the paper.

Simultaneous Supply- Demand Equation Model

The simultaneous equation model is a two-equation model of market demand and supply where

price and quantity are both endogenous variables (Ferris, 1998), (Pindyck and Rubinfeld, 1998) and

(Gujarati, 2003). The model deals with directly to the interaction of supply and demand in

establishing prices without separately using the single-equations of supply, demand and price. Price

and supply are endogenous. Besides, jointly determined price and demand and they are also

endogenous variables. Others are exogenous variables. A simultaneous-equation model includes

several endogenous variables which are simultaneously determined by an interrelated series of

equations.If any time there will be a change in the variance of the residuals (et),there is a simultaneous

change in price (p). Therefore, the simultaneous equations model will be substantially compared to the

single-equation of the supply, demand and price forecasting model are considered in this paper.

Following is the model (in logs) with price dependent supply and demand illustrating the dynamics

of such models.

Supply ; TPNRt= a0+ a1PSMR20t-1+ a2 TPNRt-1 + et (4)

Demand ; TCNRt= b0- b1PSMR20t+ b2TCNRt-1- b3 RSS1t+ et (5)

Assuming the sign on a1is positive and on b1, b3 is negative. Therefore, we can write for the price

dependent equation for supply in supply equation (6) as.

PSMR20t-1= a0+ (a1+ a2)(TPNRt-1) + et (6)

Equation (6) will be substituted into demand equation (5). We can see the demand simultaneous

equation (7) as:

TCNRt= (b0+ b1a0) - b1 (a1+ a2)(TPNRt-1) + b2TCNRt-1- b3 RSS1t+ et (7)


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Moreover, the model with price dependent equation for demand in demand equation (5) and then

we can write as follows:

PSMR20t= b0- (b1 + b2) (TCNRt-1) + b3 RSS1t+ et (8)

Equation (8) will be substituted into supply equation (4). We can see the supply simultaneous equation

as follows:

TPNRt= (a0+ a1b0) - a1 (b1 + b2) (TCNRt-1) + a1b3 RSS1t+ a2 TPNRt-1+ et (9)

If exports and imports are negligible, Supply = Demand. Therefore, supply equation (4) and demand

equation (5) will be

a0+ a1PSMR20t-1+ a2 TPNRt-1+ et= b0+ b1PSMR20t+ b2TCNRt-1+ b3 RSS1t+et

Therefore, we can write the price simultaneous equation (in logs) as follows:

(a1- b1) PSMR20t= (-a0+ b0) - a2 TPNRt-1+ b2TCNRt-1+ b3 RSS1t

PSMR20t= - (a0- b0)/(a1- b1) + a2 TPNRt-1- b2TCNRt-1+ b3 RSS1t (10)

Seasonality Test

Before making to forecast of the monthly time series data, we need to look for data patterns as;

time series data are included historical pattern and random variation. There are four basic patterns

of data: level or horizontal, trend, seasonality, and cycle pattern. It is needed to describe and

explain by using such as autocorrelations (ACs) and partial-autocorrelations (PACs) functions.

Computation of the autocorrelations (ACs) and partial-autocorrelations (PACs) functions for the

monthly natural rubber price of SMR20 (US$ /MT) variable indicates seasonality of the data series

and this is obvious from Table 5.

Table 5. Seasonal autocorrelations (ACs) and partial-autocorrelations (PACs) functionsfor the monthly natural rubber price of SMR20 (US$ /MT)

Lag ACs PACs

12 0.58 -0.0224 0.21 0.0736 -0.13 -0.02

Source: Own Data Calculation

Table 5 shows the possibility of seasonality in the world natural rubber price SMR20 with the ACs

and PACs functions. They measures how strongly time series values at a specified number of

periods apart are correlated to each other over time. In Table 5, the ACs and PACs for the price

series are small and the type of seasonality indicates an additive seasonal pattern. Additive

seasonal patterns are somewhat rare in nature, but a time series data that has a natural

multiplicative seasonal pattern is converted to one with an additive seasonal pattern by applying a

logarithm transformation to the original data. Therefore, if we are using seasonal adjustment in

conjunction with a logarithm transformation for forecasting procedures, we probably should use

additive rather than multiplicative seasonal adjustment.


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Unit Root Test

Next, it is also needed to develop the time series into a stationary one by using the unit root test for

NR price in a series. Pindyck and Rubinfeld (1998), Ferris (1998), Clements and Hendry (2001),

Gujarati (2003), and Enders (2004) explained that most of time series variables are non stationary, with

mean and variance non constant (unit root). If the data contained unit root, the data are called non

stationary, which lead to spurious regression result. Therefore, the unit root test checks for stationarity

of the data series. The natural rubber price SMR20 variable (PSMR20) for unit root was tested in Table 6.

The natural rubber price SMR20 variable (PSMR20) has been tested for stationary, using

Augmented Dickey Fuller (ADF) and Phillips-Perons tests (PP) for unit root. The results of the unit

root test, which are presented in Table 6. The natural rubber price SMR20 variable (PSMR20) at the

level data (original data form) only is not stationary for unit root and the price variable is significant

stationary at the first difference form at the 0.01 level using Augmented Dickey Fuller (ADF) and

Phillips-Perons tests (PP) for unit root.

Table 6. Unit-root tests for the monthly natural rubber price of SMR20 (US$ /MT)

Variables Unit Root Test Stationary

Level 1stdiffer Level 1

stdiffer

ADF P-P ADF P-P

PSMR20 -1.93 -1.97 -6.36*** -9.09*** St

1% critical value -3.46 -3.495% critical value -2.87 -2.94

Source: Own Data CalculationNote: St: Stationary included

**: Statistically significant at the 0.05 level.***: Statistically significant at the 0.01 level.

ADF: Augmented Dickey-Fuller test statisticP-P: Phillips-Perron test statistic

Furthermore, the estimation method of the monthly short-term natural rubber supply, demand and

price forecasting models will be explained using Vector Error Correction Method (VECM) with

cointegration characteristics of the data.

Model Estimation

Vector Error Correction (VECM) Method

A vector error correction (VEC) method was a restricted vector autoregression (VAR) designed for use

with non-stationary series that were known to be cointegrated (Gilbert, 1986 and Hendry and Ericsson,

2001). The VEC had cointegration relations built into the specification so that it restricted the long-

run behavior of the endogenous variables to converge to their cointegrating relationships while

allowing for short-run adjustment dynamics. The cointegration term was known as the error

correction term since the deviation from long-run equilibrium was corrected gradually through a

series of partial short-run adjustments (Engle and Granger, 1987). An ECM was developed in two


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stages. First, a general autoregressive distribute lag equation was specified, which explained an

endogenous variable by itscurrent and own lagged exogenous variables. Second, this equation

was manipulated to reformulate it in terms that were more easily interpreted, producing a term

representing the extent to whether the long-term equilibrium was met. The last term, one of the

unique features of this approach, was called an error-correction term since it reflected the current

"error" in achieving long-run equilibrium. Therefore, the relationship between variables, and with thisspecific relationship, there will be a series of residuals. If the residual has a pattern, and if residual

are stationary, the two variables are cointegratedand there is a long run relationship between the

two variables and if residuals are random walk, the two variables are not cointegrated.

Model Simulation

The comparison of the forecast accuracy of the natural rubber supply, demand and price

forecasting models were evaluated to generate and firstly, the data used from January 1990 to

December 2006 for estimation, with observations from January 2007 to June 2007 reserved for ex-

post forecastin Figure 3. Similarly, the data used from January 1990 to June 2007 for estimation,

with observations from July 2007 to December 2007 reserved for ex-post forecast. The datawas

subsequently employed for ex-post forecastfrom July 2008 to December 2008. Only data up from

January 1990 to December 2008 was generated for estimation, with observations from January

2009 to June 2009 reserved for ex-anteforecasts.

Figure 3. Simulation time horizons(Source: Own Data Calculation)

Time, t

T3

(Today)December 2008

T2December 2006

T1

January 1990

Estimation period

Backcasting

Ex-post simulation or

Historical simulation Ex-post forecast Ex ante forecast

(FORECASTING)

Jan07 to June07

July07 to Dec07Jan 2009 to June 2009

Jan08 to June08

July08 to Dec08


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Model Evaluation

Performance of the model is measured by the validity of its estimate on the basis of its forecasting

power (Makridakis, 1983) and (Pindyck and Rubinfeld, 1998). The forecasting ability is tested

based on the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE) and Theils

Inequality Coefficients (U) criteria. In ex-ante forecast, the RMSEof all the endogenous variablesare less than onepercent and the values of MAEare all small. The values of the Uare all nearly

zerowhich is that the forecasting performance of the estimated model is satisfactory. The MAE

and the RMSE can be used together to diagnose the variation in the errors in a set of forecasts.

The values of fraction of error due to bias (how far the mean of the forecast is from the mean of the

actual series) Um are also all very close to zero, indicating the non-existence of a

systematic bias. The values of fraction of error due to variation (how far the variation of the forecast

is from the variation of the actual series) Us and the values of fraction of error due to

covariation (the covariance proportion measures the remaining unsystematic forecasting errors)

Uc are also smalland less than onewhich indicated that the model is able to replicate the

degree of variability in the variable of interest.

Results and Discussion

Supply

Table 7 shows the estimated structural equation of supply log-linear model. The equation as a whole

explains about 60 percent of the variation in supply. The coefficient of price of SMR20 measures the

proportional change in total production of natural rubber (TPNR) for a given proportional change in price

of SMR20. Therefore, a 1 percent increase in price of SMR20 in Malaysia, other things unchanged,

increases total production of natural rubber (TPNR) by 0.12 percent with statistically significance at the

0.01 level. Burger and Smit (2000) also reported that for the short-term supply log-linear model, a 1 percent

increase in price of RSS1 in Singapore, other things unchanged, increases the total production of natural

rubber (TPNR) by 0.15 percent, 0.06 percent, 0.18 percent and 0.07 percent in Malaysia, Indonesia,

Thailand and Philippines, respectively.

The results of the ex-ante forecastof Malaysian natural rubber production using single econometric

equation model is presented in Figure 4. Based on these forecasts, Malaysian natural rubber

production in June 2009 is predicted to decrease to around 6.4 million metric tonnes (MT), a decrease

of 14.7 percent (around 7.5 million MT) when compared with December 2008.


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Table 7. Results of short-term NR supply model to determine structural equation

Vector Error Correction Estimates

Sample (adjusted): 1990M03 2008M12

Included observations: 226 after adjustments

Error Correction: D(TPNR) D(PSMR20)

CointEq1 -0.144604*** -0.033987***

(0.10382) (0.00173)

[-11.9742] [-5.55254]

D(TPNR(-1)) 0.070497 0.013772

(0.06774) (0.00113)

[1.48813] [1.22361]

D(PSMR20(-1)) 0.116990*** 0.383814***

(0.00487) (0.06322)

[5.23286] [6.07071]

C -0.416555 0.022681

(0.00692) (0.07160)

[-0.42487] [0.31678]

R-squared 0.603134 0.156383

Adj. R-squared 0.597747 0.144931

Source: Own Data CalculationNote: Adjustment coefficients are in bold. Standard errors in ( ) and t-statistics in [ ].Note: *** Statistically significant at the 0.01 level, ** at the 0.05 level, and * at the 0.10 level.

0

100

200

300

400

500

600

700

800

900

1000

2008M06

2008M07

2008M08

2008M09

2008M10

2008M11

2008M12

2009M01

2009M02

2009M03

2009M04

2009M05

2009M06

Periods

Mala

ysianNaturalRubber

P

roduction(000MT)

TPNR (000, MT) Actual TPNR (000, MT) Ex Ante Forecast

Figure 4. Ex-ante Forecast of Malaysian Natural Rubber Production (000, MT)from June 2008 to June 2009.

Demand

The estimated structural equation of demand log-linear model is shown in Table 8. The equation as

a whole explains about 70 percent of the variation in demand. Moreover, a 1 percent increase in

price of SMR20 in Malaysia, other things unchanged, decreases total consumption of rubber (TCNR)

by 0.039 percent with statistically significance at the 0.01 level. Also, a 1 percent increase in price of

RSS1 in New York, other things unchanged, decreases total consumption of rubber (TCNR) by

0.028 percent with statistically significance at the 0.01 level. However, the total consumption of

rubber in the previous period is not significant at the 0.01 level in the demand model. Burger and Smit


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(2000) also reported that the short-term demand log-linear model for the world as a whole, a 1

percent increase in price of RSS1 in Singapore, on average, has the adverse effect of decreasing

the total consumption of rubber (TCNR) by 0.026 percent.

Table 8. Results of short-term NR demand model to determine structural equations




Error Correction: D(TCNR) D(PSMR20) D(RSS1)

CointEq1 -0.134517 0.042613*** 0.038434***

(0.00124) (0.00219) (0.00182)

[-1.08693] [5.26168] [5.11697]

D(TCNR(-1)) 0.266442 -0.170198 -0.177400

(0.06473) (0.11445) (0.09493)

[3.11590] [-4.48712] [-3.86882]

D(PSMR20(-1)) -0.039660*** 0.187813 0.065440

(0.06021) (0.10645) (0.08829)[-6.15695] [4.76437] [2.74119]

D(RSS1(-1)) -0.028345*** 0.156029 0.317592

(0.07107) (0.12565) (0.10422)

[-5.00370] [1.24173] [3.04730]

C -0.742562 0.024164 0.029208

(0.20141) (0.06974) (0.06386)

[-3.68690] [0.34649] [0.45737]

R-squared 0.702320 0.203352 0.187172

Adj. R-squared 0.696908 0.188868 0.172394

Source: Own Data Calculation

Note: Adjustment coefficients are in bold. Standard errors in ( ) and t-statistics in [ ].Note: *** Statistically significant at the 0.01 level, ** at the 0.05 level, and * at the 0.10 level.

The results of the ex-ante forecastof Malaysian total rubber consumption using single econometricequation model is presented in Figure 5. The forecasts predict that Malaysian total rubberconsumption would increase to around 15.7 million MT in June 2009, an increase of 11.5 percent(around 13.9 million MT) from December 2008.

0

200

400600

800

1000

1200

1400

1600

1800

2000

2008M06

2008M07

2008M08

2008M09

2008M10

2008M11

2008M12

2009M01

2009M02

2009M03

2009M04

2009M05

2009M06

Periods

Malay

sianTotalRubber

Cons

umption(000MT)

TCNR (000, MT) Actual TCNR (000, MT) Ex Ante Forecast

Figure 5. Ex-ante Forecast of Malaysian Total Rubber Consumptionfrom June 2008 to June 2009.


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Price

Table 9. Results of short-term NR price model to determine structural equation




Error Correction: D(PSMR20) D(TPNR) D(TCNR) D(STONR) D(COP) D(EXM)

CointEq1 -0.020331 -0.161465*** 0.710524*** 0.590223 -0.141512 0.383251

(0.00234) (0.00295) (0.00116) (0.00148) (0.00262) (0.00069)

[-0.86900] [-5.47181] [ 6.12647] [ 3.97572] [-0.54069] [0.55840]

D(PSMR20(-1)) 0.280764 0.098304 -0.043525 -0.030100 0.114720 -0.064877

(0.06985) (0.08811) (0.03463) (0.04433) (0.07815) (0.02046)

[ 4.01942] [1.11563] [-1.25685] [-0.67901] [ 1.46802] [-3.17134]

D(TPNR(-1)) 0.076951 0.043678 -0.149403 0.042041 0.011733 -0.013201

(0.04934) (0.06224) (0.02446) (0.03131) (0.05520) (0.01445)

[0.15597] [ 0.70180] [-6.10810] [ 1.34269] [ 0.21257] [-0.91363]

D(TCNR(-1)) -0.102621 -0.903993 0.131083 -0.136772 0.048424 -0.032060(0.12529) (0.15805) (0.06212) (0.07952) (0.14017) (0.03669)

[ -0.81904] [-5.71955] [2.11027] [-1.72008] [ 0.34546] [-0.87370]

D(STONR(-1)) -0.061975 0.189204 -0.122740 0.080140 0.137328 -0.015161

(0.12503) (0.15772) (0.06199) (0.07935) (0.13988) (0.03662)

[-0.49567] [1.19961] [-1.98013] [1.00997] [0.98177] [-0.41404]

D(COP(-1)) 0.054152 0.020602 0.127800 0.026371 0.340415 0.008239

(0.06106) (0.07703) (0.03027) (0.03875) (0.06831) (0.01788)

[ 0.88680] [ 0.26746] [ 4.22158] [0.68050] [ 4.98304] [ 0.46068]

D(EXM(-1)) -0.458843 -0.116587 -0.048145 -0.072181 0.101559 0.182269

(0.22612) (0.28524) (0.11210) (0.14350) (0.25297) (0.06622)

[ -2.02920] [-0.40873] [-0.42948] [-0.50300] [0.40147] [ 2.75236]

C 0.000123 -0.003912 -0.000567 0.002519 0.001212 0.000974

(0.00492) (0.00620) (0.00244) (0.00312) (0.00550) (0.00144)

[0.02492] [-0.63063] [-0.23265] [ 0.80715] [ 0.22022] [ 0.67652]

R-squared 0.394987 0.284244 0.309640 0.160368 0.145401 0.095869

Adj. R-squared 0.365927 0.261261 0.287472 0.133407 0.117960 0.066837Source: Own Data CalculationNote: Adjustment coefficients are in bold. Standard errors in ( ) and t-statistics in [ ].Note: *** Statistically significant at the 0.01 level, ** at the 0.05 level, and * at the 0.10 level.

Table 9 also shows the single-equation model of short-term monthly natural rubber price PSMR20

and the explanatory variables accounted for about only 39 percent of the variation in the monthly

natural rubber price. Therefore, a 1 percent increase in price of SMR20 in Malaysia (US$/MT), other

things unchanged, increases total production of natural rubber (TPNR) by 0.16 percent with

statistically significance at the 0.01 level. Moreover, a 1 percent increase in price of SMR20 in

Malaysia (US$/MT), on average, has the adverse effect of decreasing the total consumption of

natural rubber and synthetic rubber (TCNR) by 0.71 percent with statistically significance at the

0.01 level. Therefore, they are cointegratedmeaning that there is a long run relationship between the


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total production of natural rubber (TPNR), total consumption of natural rubber and synthetic rubber

(TCNR) and price of SMR20 in the single-equation of price econometric forecasting model.

Simultaneous Supply- Demand Model

Table 10. Results of simultaneous supply-demand model of short-term NR price to determine structural

equations

System Equations

Sample: 1990M02 2008M12

Included observations: 227

Dependent Independent Summary Statistics of the Regression Coefficients

Variable Variable Coefficient Std. Error t-Statistic Prob.

Supply (TPNRt) PSMR20t-1 0.130861 0.070854 2.043560 0.1653TPNRt-1 0.656709 0.052606 12.48356 0.0000***TCNRt-1 -0.377657 0.080028 -4.719083 0.0000***RSS1t-1 0.141314 0.072569 1.194727 0.3457C -0.605758 0.381198 -1.589090 0.1124

R-squared 0.801406 Mean dependent var 6.345908Adj: R-squared 0.797827 S.D. dependent var 0.222053

S.E. of regression 0.099843 Sum squared resid 0.213044

Durbin-Watson stat 0.063575

Demand (TCNRt) PSMR20t-1 -0.036796 0.029960 1.228191 0.2197TPNRt-1 -0.053375 0.022244 -4.399543 0.0166**TCNRt-1 0.886147 0.033839 26.187421 0.0000***RSS1t-1 -0.033272 0.030685 -1.084325 0.2785C -0.483504 0.161185 -2.999684 0.0028

R-squared 0.922172 Mean dependent var 7.267020

Adj: R-squared 0.920769 S.D. dependent var 0.149984



Price (PSMR20t) PSMR20t-1 0.970446 0.053889 18.00837 0.0000***TPNRt-1 0.011217 0.040010 0.280357 0.7793TCNRt-1 -0.076001 0.060865 -1.248667 0.2121RSS1t-1 0.001512 0.055193 0.027399 0.9781C 0.413974 0.289922 1.427880 0.1537





Price (RSS1t) PSMR20t-1 0.098660 0.044446 2.219800 0.0267TPNRt-1 0.029517 0.032999 0.894498 0.3713TCNRt-1 -0.097800 0.050200 -1.948207 0.0517RSS1t-1 0.879103 0.045521 19.31190 0.0000***C 0.460344 0.239118 -1.925172 0.0545





STONRt= STONRt-1+ TPNRt TCNRtSource: Own Data CalculationNote: *** Statistically significant at the 0.01 level, ** at the 0.05 level, and * at the 0.10 level.


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Table 10 also shows the results of the short-term natural rubber price monthly simultaneous

supply-demand equation model by using the system equations and all the estimated coefficients in

the equations show the expected signs. Firstly, the explanatory variables accounted for about 80

percent of the variation in the monthly natural rubber supply model. Estimations reveal that the

explanatory variables, namely the total production of natural rubber in the previous period and total

consumption of natural rubber and synthetic rubber (TCNR), were the most important explanatoryvariables with statistically significance at the 0.01 level in the supply model.

Likewise, the explanatory variables accounted for about 92 percent of the variation in the monthly

natural rubber demand model. Estimations reveal that the explanatory variables, namely the total

production of natural rubber (TPNR) and total consumption of natural rubber and synthetic rubber

in the previous period, were the most important explanatory variables with statistically significance at

the 0.01 level in the demand model. Moreover, the explanatory variables accounted for about 97

percent of the variation in the monthly natural rubber price (SMR20) model. Estimations reveal that

the explanatory variable, namely the price of SMR20 in the previous period was the most important

explanatory variable with statistically significance at the 0.01 level in the price SMR20 model.

Likewise, the explanatory variables accounted for about 98 percent of the variation in the monthly

natural rubber price (RSS1) model. Estimations reveal that the explanatory variable, namely the

price of RSS1 in the previous period only was the most important explanatory variable with

statistically significance at the 0.01 level in the price RSS1 model.

The results of the comparison of ex-ante forecast of Malaysian natural rubber SMR20 (PSMR20)

monthly price (US$ per MT) using single and simultaneous supply-demand equation models are presented

in Table 11 and Figure 6. The comparative forecasting power was based on the forecasting power of

the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Theils Inequality Coefficients (U)

criteria and fraction of error due to bias (Um), fraction of error due to variation (Us)

and fraction of error due to covariation (Uc). The results revealed that the values of the RMSE,

MAE and U of simultaneous supply-demand equation model were comparatively smaller than the values

generated by the single-equation of the price econometric model. These statistics suggested that the

simultaneous equation of supply-demand model was more efficient measured in terms of its statistical

criteria than the single-equation of price econometric model.

The models solved dynamically for ex-ante forecasts, over for the period of January 2009 - June 2009

from the econometric, and simultaneous supply-demand models are presented in Figure 6 and the

estimations made are based on data from period January 1990 December 2008. The Malaysian

natural rubber price of natural rubber (SMR20) is expected to increase to around US$ 1700 per MT in

June 2009, an increase of 28.8 percent from December 2008 with US$ 1376.57 per MT.


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In addition, the values of fraction of error due to bias (Um) were also all very close to zero,

indicating the non-existence of a systematic bias. The values of fraction of error due to variation

(Us) and fraction of error due to covariation (Uc) were also small and less than one

indicating that the model was able to replicate the degree of variability in the variable of interest.

Thus a revision of the model was not necessary. The Theils Inequality Coefficient (U) was less

than one which meant that the forecasting performance of the estimated model was satisfactory.

Table 11. Ex-ante forecast of monthly Malaysian natural rubber price SMR20 (US$ per MT)from June 2008 to June 2009 and model evaluations

Period Actual Price Single-equation of PriceForecast Econometric Model

Simultaneous Equation of PriceForecast Econometric Model

2008.06 3457.5800 3367.6977 3477.8518

2008.07 3530.9611 3624.1565 3578.5458

2008.08 3266.2761 3135.1684 3260.5059

2008.09 3144.3619 2992.8272 3169.4698

2008.10 2150.9165 2023.5626 2125.3833

2008.11 1891.6306 1847.3433 1960.7289

2008.12 1376.5662 1228.1343 1412.1854

2009.01 1508.1695 1576.8292

2009.02 1634.2466 1620.8233

2009.03 1759.6314 1656.6692

2009.04 1849.2314 1702.2697

2009.05 1927.6314 1780.9653

2009.06 1924.8314 1775.2114

RMSE 0.334 0.093

MAE 0.276 0.068

U-STAT 0.058 0.019

(Um) 0.000 0.000

(Us) 0.068 0.013

(Uc) 0.932 0.987

Source: Own Data Calculations

0

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2009.0

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2009.0

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Periods

Natur

alRubberPriceSMR20

(US$perMT)

Acutal Price Econometr ic Simultaneous

Figure 5. Ex-ante Forecast of Malaysian Natural Rubber Price SMR20 (US$ per MT)from June 2008 to June 2009.


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Conclusion

Based on the results of the above analysis, simultaneous supply-demand equation models ex-ante

forecast was more efficient measured either in terms of its statistical criteria or even by visual

proximity with the actual prices. Being such an important commodity to Malaysia and World

market, an accurate estimation methodology for natural rubber are vital to forecast the NR supply,demand and price for decision-making process in economic planning including a price stability

mechanism. The results show that Malaysian natural rubber production would be on a down trend.

Iit would be interesting to factor for the natural rubber production increased of the Malaysian NR

industry as well as the government assistance programs for smallholders, which increased tree and land

productivity, ensuring of full government support and eradicating of poverty level to them, and finally

well regulated NR trading.

The results show that Malaysian total rubber consumption would be on an increasing trend and it is

due to Chinas demand which grew by 5.1 percent annually and estimated to reach nearly 1.6

million MT by 2010. World rubber consumption is forecast to increase 4.0 percent annually to 26.5

million metric tons in 2011. Gains will directly benefit from solid growth in world motor vehicle

production, as well as a strong global economy. The China, US and Japan dominate global rubber

consumption, and will continue to do so, collectively accounting for more than half of the market in

2011. China has become the leading consumer of rubber worldwide, following more than a decade

of strong growth in motor vehicle production and industrial goods manufacturing. The country

overtook Japan as the second largest rubber market in the late 1990s and by 2001 had essentially

caught up to the US as the worlds leading consumer. While China will continue to extend that

lead, the US and Japan will remain leading markets worldwide, because of their extensive motor

vehicle and tire industries (The World Tire & Rubber Market Report, 2007).

The results revealed that Malaysian natural rubber price SMR20 predicted to increase and it would likely

lead to higher production. The Malaysian rubber industry would be produced positive net trade flows,

provided steady employment and also consistent earnings for the natural rubber producing

countries. If the extremely low prices experienced during these years and it would be contributed to

increase rural poverty in many countries, especially rubber smallholders in South East Asia and

also due to the result of a widespread global recession, with low underlying global demand.

A forecast if found to be way off target when actual data become available may lead to model

revision. The forecasting is related to the current and expected fundamentals of the natural rubber

producers and consumers as well as traders and planners for new investment decisions in the

natural rubber markets. Hence, a price forecasting mechanism is necessary to guide market

participants in their production, consumption and financing decisions. Forecasts using other price


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forecasting models and such as short-term and together with long-term price forecasts, which were

not attempted for this study, could also be potentially beneficial for future work.

References

1. ANRPC (Association of Natural Rubber Producing Countries) (2009). Monthly Bulletin of RubberStatistics, 1(3).2. Burger, K., and H. P. Smit. (1997, 2000). Long-term and short-term analysis of the natural

rubber market.Department of Econometrics, Economic and Social Institute, Faculty of Economicsand Business Administration, Vrije University, De Boelelaan 1105, 1081 HV Amsterdam, TheNetherlands.

3. Barlow, C., Jayasuriya, S. and Tan, C. S. (1994). The World Rubber Industry. London: Routledge.4. Christ, C. F. (1994). Simultaneous Equation Estimation: International Library of Critical Writings

in Econometrics,xi - xxii.5. Clements, M. P. and Hendry, D. F. (2004). A Companion to Economic Forecasting.Blackwell

Publishing, 350 Main Street, Malsen, MA 02148-5020., USA.6. Djoko Said Damardjati. (2009). Outlook on global supply of natural rubber, National Rubber

Economic Conference 2009, Malaysian Rubber Board.

7. Enders, W. (2004). Applied Econometric Time Series. University of Alabama.www.wiley.com/college/enders.s8. Fatimah Mohd, Arshad and Zainalabidin Mohamed. (1994). Price discovery through crude palm

oil futures market: An economic evaluation. In Erdener Kaynak and Mohamed Sulaiman inproceedings on Third Annual Congress on Capitalizing the Potentials of Globalization Strategies and Dynamics of Business, Penang.

9. Ferris, John N. 1998. Agricultural Prices and Commodity Market Analysis.10. Gujarati, D. (2003). Basic Econometrics (4th Edition). McGraw-Hill, Inc.11. International Rubber Study Group (IRSG), Rubber Industry Report: April/June/July, 2009.12. King, T. M. (2002). Using simultaneous equation modeling for defining complex phenotypes.13. Krichene, N. (2005). A simultaneous equations model for world crude oil and natural gas

markets. IMF Working Paper WP/05/32.14. Kamarul Baharain bin Basir. (2009). Sustainability and competitiveness of the Malaysian NR

industry, National Rubber Economic Conference 2009, Malaysian Rubber Board.15. Lim, J. Y. (2002). An Evaluation of Alternative Forecasting Models for Natural Rubber Prices,

Curtin University of Technology, Australia.16. Mad Nasir Shamsudin and Fatimah Mohd Arshad. (1998). Short Term Forecasting of Malaysian

Crude Palm Oil Prices, http://www.econ.upm.edu.my/-fatimah/pipoc-.html.17. Makridakis, S. (1983). Forecasting: Methods and Application. New York: John Wiley & Sons, Inc.18. Malaysia Rubber Board (MRB). (2009). Surviving the Economic Doldrums: The Way Forward;

National Rubber Economic Conference Papers.19. OECD-FAO. (2008). Short-term Economic Outlook. Agriculture and Rural Development:

Agricultural Trade Policy Analysis unit. http://ec.europa.eu/agriculture/publi/map/index_en.htm20. Pindyck, Robert S. and Rubinfeld, Daniel L. (1998). Econometric Models and Economic

Forecasts.(4thed.). Copyright by the McGraw-Hill Companies, Inc.21. Tan, C. S. (1984). World rubber market structure and stabilization: An econometric study.

World Bank Staff Commodity Papers. No. 10.22. World Tire & Rubber Market Report. (2007). http://www.reportlinker.com/p075716/World-Tire-

and-Rubber-Market.html

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