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Energy Policy 31 (2003) 11511165

Oil price fluctuations and Singapore economy

Youngho Chang*, Joon Fong Wong

Department of Economics, National University of Singapore, Singapore 117570, Singapore

Abstract

This study finds that the impact of an oil price shock on the Singapore economy is marginal. Both impulse response and variance

decomposition analysis provide reasonable grounds to believe that the impact only had an insignificant adverse effect on Singapores

gross domestic product (GDP), inflation and unemployment rates. Further analysis on two oil vulnerability measures supports the

finding: the declining trend of oil intensity in Singapore since 1989 and the declining shares of the Singapores expenditure on oil

consumption as a percentage of its nominal GDP. This study identifies, however, that the impact of an oil price shock on theSingapore economy should not be considered negligible even though it is small.

r 2003 Elsevier Science Ltd. All rights reserved.

JEL: Q43

Keywords: Oil price fluctuations; Macroeconomic performance; Singapore

1. Introduction

Oil has had a unique position in the worlds economic

system. It is a vital source of energy, an irreplaceabletransport fuel, and an essential raw material in many

manufacturing processes. World oil consumption

amounted to roughly 73 million barrels per day in

1997 and it remains the largest share of world energy

consumption compared to any other energy source,

accounting for about 39 percent of the total in 1997. The

world oil consumption is forecast by the Energy

Information Administration (EIA) of the Department

of Energy in the USA to increase by a total of 39.8

million barrels per day to 112.8 million barrels per day

in 2020 (EIA, 2000).

At the regional level, oil is of particular importance to

many Asian economies as most are net importers of

energy product.1 Stable and low oil prices would thus

represent lower costs for industry feed stocks, electricity

generation and transportation for an energy importing

economy. Although low oil prices alone cannot explain

the economic growth of many Asian economies over the

past decade, it would be safe to conclude that none of

the energy importing Asian economies would have been

as successful over the past decade with a price of US$40

per barrel of oil.Singapore has been identified as one of the six Asian

economies that are considered to be seriously exposed to

world oil price fluctuation (Aoyama and Berard, 1998).

Table 1 shows all six Asian economies consume close to

50 percent of their energy consumption in the form of

oil, and the most striking statistics come from Singa-

pore, which consumes up to 95 percent of its energy

consumption in the form of oil.

Oil consumption in Singapore is estimated to be

587,000 barrels per day in 1999.2 Although Singapore is

one of the major petroleum refining centers of Asia, with

total crude oil refining capacity of 1.3 million barrels per

day, it does not produce any crude oil and is a pure net

oil importer. Oil also roughly accounts for about 8

percent of Singapore total trades in 1999. Table 2

provides an overview of Singapore trade statistics in

1999.

Given the degree of dependency on oil as revealed by

the statistical data, an investigation is carried out to

explore the relationship between oil prices and Singa-

pores macroeconomic performance in the paper. The

consequences of an oil shock could be summarized as a

*Corresponding author. Tel.: +65-874-3947; fax: +65-775-2646.

E-mail address: ecscyh@nus.edu.sg (Y. Chang).1Asian oil imbalance was estimated to be approximately 10 million

barrels per day in 1998 and the figure is expected to double over the

next decade. See Aoyama and Berard (1998) for detailed analysis on

Asian oil imbalance. 2See EIA website at http://www.eia.doe.gov/ for details.

0301-4215/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved.

PII: S 0 3 0 1 - 4 2 1 5 ( 0 2 ) 0 0 2 1 2 - 4

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fall in the general output level, higher unemployment

and an increase in the general price level. The examina-

tion on the relationship is achieved via a formal

empirical framework, modelling the variables as a

cointegrated system in a vector error correction model

(VECM). Quarterly data of four variables, namely oil

price, gross domestic product (GDP), consumer price

index (CPI) and unemployment rate, running from 1978

Quarter 1 to 2000 Quarter 3 are employed to meet this

objective.

The objective of this study is to examine the relation-

ship between oil prices and Singapores macroeconomic

performance. Particularly, we are interested to find out

whether oil price shock had an impact on Singapore

macroeconomy. Section 2 considers the impact of oil

price fluctuations on the Singapore economy. The

impulse response and variance decomposition (VDC)

analysis are followed. A review of the Singapores

experiences with past oil price shocks is given to support

the findings of the empirical analysis. Section 3

concludes with a brief review of the principal findings

and a discussion of directions for further study.

2. Oil price fluctuations and macroeconomy

Since the first oil shock in 1973/74, much research has

been undertaken into the oil pricemacroeconomy

nexus. These studies have reached different conclusions

over time. Earlier works (Darby, 1982; Hamilton, 1983;

Burbidge and Harrison, 1984) have achieved statistically

significant empirical relationships between oil prices and

aggregate economic performance, principally GDP/

GNP growth. Following the collapse of oil prices in

1986, it was argued that the oil pricemacroeconomy

relationship has weakened. In addition, an asymmetric

oil pricemacroeconomy relationship was established

(Mork, 1989; Mork et al., 1990, 1994). Later studies

from 1995 onwards devoted much attention to investi-

gate the weakening of the oil pricemacroeconomy

relationship. Particularly, Lee et al. (1995) and Hooker

(1996, 1999) argued strongly that the fundamental oil

pricemacroeconomic relationship identified in earlier

studies had eroded.It is noted that much of the research on oil price

macroeconomy relationship have been done concentrat-

ing on either the United States (US) or Organization for

Economic Cooperation and Development economies.

There are very few studies that investigate the oil price

macroeconomy relationship for Singapore.3 Ito and Tay

(1992) simulated the impact of oil shocks on Singapore

economy using a computable general equilibrium model.

They concluded that Singapore, like many other

countries, is vulnerable to oil price disturbance. How-

ever, these adverse impacts could be offset under two

conditions: firstly, if worldwide inflation rate during the

oil shock is greater than the domestic inflation rate, and

secondly, if Singapore could maintain a stable wage rate

policy in spite of the rising oil prices.

Abseysinghe and Wilson (2000) using a multicountry

econometric model, found that Singapore is susceptible

to an oil price hike. Their results showed that oil prices

do have a direct positive effect on inflation and a

negative impact on GDP growth. However, they

estimated that the negative impact on GDP growth

Table 1

Energy consumption by source 1999 (million tonnes of oil equivalent)

Oil Natural gas Coal Nuclear energy Hydroelectric Total

Japan 258.8 67.1 91.5 82.0 8 507.4

South Korea 99.9 16.9 38.1 26.6 0.5 182.0

Taiwan 39.9 5.6 24.8 9.9 0.8 81.0

Thailand 35.7 14.8 8.5 0.3 59.3Philippines 18.0 o0.05 2.9 0.7 21.6

Singapore 28.3 1.4 29.6

Source: BP Amoco (2001) Statistical Review of World Energy, 2000.

Table 2

Summary of Singapore trade statistics 1999: oil/nonoil

Type of trade Value in S$ thousand

Total trade 382,431,176

oil 29,020,308

nonoil 353,410,868

Imports 188,141,561oil 17,074,177

nonoil 171,067,384

Total exports 194,289,615

oil 11,946,130

nonoil 182,343,485

Domestic exports 116,324,952

oil 11,754,368

nonoil 104,570,584

Reexports 77,964,664

oil 191,762

non oil 77,772,901

Source: Singapore Trade ConnectionAnnual CD ROM (2000).

3Three out of the four studies review in this section only presents

their results with no documentation on their methodology.

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could be offset by an indirect positive effect resulting

from trade with the oil-exporting countries if the oil

price hike does not last longer than 1 year.4

Two Singapore government bodies (Ministry of Trade

and Industry (MTI) and the Monetary Authority of

Singapore (MAS)) also estimated the likely impact of

the recent price hike on the economy. Both studiespresented very similar results. Although both inflation

rates and GDP growth would be adversely affected by

the oil price surge, the estimated degree of the impacts is

very marginal. MTI (2000) and MAS (2000) estimated

that a year-long increase of US$10/b in oil prices would

only raise inflation in Singapore by 0.8 percent points

and 1 percent point, respectively. As for the impact on

output, both studies estimated that the US$10/b oil

price increase would reduce real GDP growth by about

0.6 percent point.

2.1. Methodology

The key difference between the existing studies and

this study is the methodology that we have employed to

examine the impact of oil shocks on the Singapore

macroeconomic performance. The method we employ

for this study is a vector error correction model

(VECM). The essence of the VECM model lies in the

implication that the series being studied is cointegrated,

thus implies the existence of long-run relationships

between the integrated time series. In statistics, the

presence of cointegration among the relevant variables

indicates that a linear combination of nonstationarytime series exhibits a stationary series, thus avoiding the

problem of spurious regression. An error correction

mechanism is incorporated in the model to capture the

variations associated with adjustment to a long-term

relationship.

More importantly, variance decomposition (VDC)

and impulse responses are applied to verify the relation-

ship between oil price shocks and aggregate economic

activity. A VDC analysis apportions the variance of

forecast errors in a given variable to its own shocks and

those of the other variables in the VECM; it assesses the

relative importance of oil price shocks to the volatility of

other variables in the system. And the impulse response

function (IRF) traces over time the effects on a variable

of an exogenous shock to another variable. Thus, the

IRF allows us to examine the dynamic effects of oil price

shocks on the Singapore macroeconomic activity and

inflation. The vector autoregressive (VAR) model lacks

this advantage of capturing a possible long-term

relationship between the series of variables. The more

detailed description on the methodology is presented in

the later part of this section.

2.2. Data

The data is obtained from International Financial

Statistics (IFS) CD-ROM 2000 and the Singapore

Department of Statistics (DOS). A total of four data

series, which include oil prices and three Singapore

macroeconomic variables namely GDP, CPI, and

unemployment rates are applied in this paper to examine

the relationship between oil prices and the Singapore

macroeconomy. The sample period spans from the first

quarter of 1978 to the third quarter of 2000, consisting

of a total of 91 quarterly observations for each variable.

1978 is chosen as the starting point because earlier

quarterly data for most of the series are not available.

Nonetheless, the sample period effectively covers the1978/79, 1990 and the 2000 oil price shocks.

The Dubai crude oil price is chosen as the oil price

variable. It is chosen for two main reasons. Firstly,

lower-frequency average oil price data is not freely

available. And secondly any other choice of other crude

oil prices would not significantly affect the analysis since

crude oil prices have been observed to fluctuate in the

same direction empirically. The original Dubai crude oil

price collected is quoted in US dollar. To make sure that

all the data series are quoted in the same currency,

quarterly exchange rate in the respective quarters are

collected from IFS CD-ROM 2000 to convert the oil

prices to Singapore dollars.

The three macroeconomic variables are chosen based

on the impact of an oil price shock as discussed in the

above section. GDP represents the level of output

produced within an economy in a given year. The use of

GDP, rather than GNP, is perceived to be more

appropriate because an economys total energy con-

sumption depends on goods and services produced

within the economy, and not outside the economy. CPI

serves as a measurement of the economys inflation level.

To test for the impact in the labor market, the

unemployment rate is chosen as a desirable proxy.

However, official quarterly data for unemployment rateis available only from 1986.5 Thus to extend the series

back to 1978, the equal-step method is applied to

convert annual data to quarterly data (Gaynor and

Kirkpatrick, 1994).

Both oil prices and GDP are entered into the

econometric model in real terms. The oil price data are

transformed into real terms using the Singapores CPI

and the real GDP are obtained directly from the DOS.4Abseysinghe and Wilson (2000) estimated that in the first year of anoil shock, the oil-exporting countries like Malaysia and Indonesia will

enjoy a net positive gain on GDP growth. This would translate into

higher demand for Singapore exports, thus mitigating to a certain

degree the direct negative effect of an oil price shock on Singapore.

5It is noted from Dr. Soon Tech Woon (DirectorEconomics

Account, Singapore Department of Statistics) that the Ministry of

Manpower only started their quarterly labor survey in the mid 1980s.

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All variables with the exception of unemployment rate

are transformed by natural logarithms. The unemploy-

ment rate figures are seasonally adjusted using X12AR-

IMA module developed in PC-GIVE Professional 9.3.6

Seasonally adjusted GDP quarterly series are provided

by the DOS and no further adjustment is made to the

series. As for the CPI series, it is noted from the DOSthat seasonality is not significantly present.

A description of all the variables is summarized in

Table 3.

2.3. Tests for stationarity

Cointegration test requires a certain stochastic

structure of the individual time series. The focus is on

first-order nonstationary integrated processes, I1

processes, which require first differences to become

stationary.7 Thus, to test for the presence of stochastic

nonstationary in the data used here, it is necessary to

investigate the order of integration of the individual

time series preceding any other tests, known as unit

root tests. Two types of unit root tests are conducted:

DickeyFuller/Augmented DickeyFuller (ADF) test

(Dickey and Fuller, 1979, 1981) and PhillipPerron

test.

2.4. Cointegration analysis

A system of nonstationary individual series in the

levels can, however, share common stochastic trends. It

is quite possible for there to be a linear combination of

integrated variables that are stationary, and suchvariables are said to be cointegrated (Engle and

Granger, 1987). Put simply, two or more nonstationary

time series are cointegrated if a linear combination of

these variables is stationary (converges to an equilibrium

over time). Theoretically, it is quite possible that

nonlinear long-run relationships exist among a set of

integrated variables. Cointegration can be interpreted as

a specification of models that include beliefs about the

movement of variables relative to each other in the long

run. Thus, a common stochastic trend in the system of

oil price and macroeconomy variables could be inter-

preted to imply that the stochastic trend in oil prices is

related to the stochastic trend in the macroeconomy

variables.

In this study, the investigation of the existence of

common stochastic trends in a system of oil price and

macroeconomic variables is conducted by means of the

Johansen (1988) method of cointegration test.8 This

procedure provides more robust results than other

cointegrating methods when more than two variables

are used.

2.5. A (VAR) vector autoregressive model with

cointegrated variables

Following the cointegration analysis,9 this study

proceeds to consider a K-dimensional VAR model of

the form

yt A1yt1 ? Apytp ut; 1

where yt y1t;y;yKt0 is the column vector of lagged

endogenous variables, p is the number of lags and the Aiare K K coefficient matrices parameters which can

be represented by

Ai

a11;i ? a1K;i

^ & ^

aK1;i ? aKK;i

2

64

3

75

and ut u1t;y; ukt0 have mean zero, Eut 0; and a

nonsingular covariance matrix Su Eutu0t for all t:

Furthermore, ut and us are uncorrelated for tas: Aprocess ut with these properties is often called vector

white noise. It is also assumed that the first differences

Dyt yt yt1 are stationary if it has bounded means

and covariance matrices and that the polynomial is

defined by the determinant

detIK A1z A2z2 ? Apz

p 2

has all its roots outside the complex unit circle except

for possibly some roots that are unity.10 In other

words,

P IK A1 ? Ap; 3

where P is the polynomial in Eq. (2) which may be

singular, say of rank rpK:The K K matrix P can be expressed as a product

of a K r matrix B and an r K matrix C; whichhave both rank r; that is, P BC: Here C is a matrixrepresenting the cointegration relations such that Cyt is

stationary. Commonly, Cyt is interpreted as the long-

run equilibrium relation between the y variables. Since

this relation is often of interest, the model is usuallyreparameterized in one of several equivalent forms.

Following L .utkepohl and Reimers (1992), we use the

following representation:

Dyt G1 Dyt1 ? Gp1 Dytp1 Pytp ut; 4

6PC-Give Professional 9.3 is an econometric software developed by

J.A Doornik and D.F Henry.7A series is said to be stationary if the mean and autocovariances of

the series do not depend on time.8For more detailed discussion on the Johansen procedures, refer to

Harris (1995), using cointegration analysis in econometric modelling.

9The VAR model presented in this section is similar to that

described earlier in the Johansens procedures. However, this section

discusses the VAR model in greater detail with emphasis on the IRF

and the VDC.10See Judge et al. (1988) for a further discussion on the stationarity

properties. Mathematical proof of the process is available in L .utkepohl

(1993).

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where again Gi is the matrix of parameter coefficients

which can be represented by

Gi IK A1 ? Ai; i 1;y;p 1:

Again, P is the matrix of coefficients of the cointegra-

tion vectors. In this representation, it becomes obvious

that the deviations from the equilibrium relations Cytpform a stationary process.11

The quantities of interest in the following are the

impulse responses (or dynamic multipliers) and the

VDCs that represent the effects of shocks in the variablesof the system. As in the stationary case, the IRFs are

most easily obtained from Eq. (1) and are defined as

Fnjik;n Xn

j1

FnjAj; n 1; 2;y; 5

where F0 IK; Aj 0 for j> p and jik;n is the ikthelement of Fn and represents the response of variable

yii 1;y; K to an initial shock in variable K; nperiods ago. (L .utkepohl and Reimers, 1992).

To use the impulseresponse functions and the VDC

procedure it is necessary to identify the shocks for each

and every variable in the system. In more general terms,KK 1=2 restrictions are needed to exactly identifythe model (where K is the number of variables in the

model).12 In many econometric studies, responses to

orthogonalized impulses are preferred. They are defined

as Yn yik;n FnP; where P is the lower-triangularCholeski decomposition of Su, that is, PP

0 Su.

Obviously, there is some degree of arbitrariness when

constructing shocks in this manner.13 Again yik;n is

interpreted as response of variable ii 1;y; K toan impulse in variable K; n periods ago. Theseimpulses can be thought of as transformed residuals

of the form represented by wt P1ut which have

identity covariance matrix, Ewtw0t IK: Thus, a unit

impulse had size of one standard deviation (SD) in

this case. For both types of impulse responses, the

difference to the stationary case is that the effect of a

shock in one of the variables will, in general, not die out

in the long run, that is, the variables may not return to

their initial values even if no further shocks occur. In

other words, a one-time impulse may have a permanent

effect in the sense that it shifts the system to a new

equilibrium. Therefore, Fn and Yn cannot be interpreted

as moving average coefficient matrices and their sums

will, in general, not be finite (L .utkepohl and Reimers,

1992).

In addition to the impulse response, forecast error

VDCs system dynamics are performed in our VAR

analyses. VDC is the complement to impulse response.

The decomposition of forecast error variance provides

an estimate of the amount of influence variables have in

the system. It is noted that VDC does not demonstratethe impacts of the shock (as the impulse response

provides), but instead this technique yields the cumula-

tive effect of one variable on another in a system over

time, measured in terms of a proportion or percentages.

L .utkepohl and Reimers (1992) have pointed out that the

VDCs are also available for the cointegrated system.

VDCs can be computed using the same formulas as in

the stationary case. Specifically, the n-period ahead

forecast error from a VAR is

utp C1utp1 C2utp2 ?Cp1ut1;

i 1;y;p 1;6

where Ci are the n-period ahead forecast coefficient

parameters and with mean square error

O C1OC01 ?Cp1OC

0p1

PP0 C1PP0C01 ?Cp1PP

0C0p1

XK

j1

pjp0

j C1pjp0

jC01 ?Cp1pjp

0jC

0p1; 7

Table 3

Definitions of variables

Variables Definitions of variables Source

LOIL Natural logarithms of quarterly real Dubai crude oil prices in Singapore dollar (S$) (in 1990 prices) IFS CD-ROM 2000

LGDP Natural logarithms of quarterly real Singapore gross domestic product in S$ (in 1990 prices) DOS

LCPI Natural logarithms of quarterly Singapore Consumer Price Index (base year=1990) DOS

UN Quarterly unemployment rate of Singapore DOS

11This representation is used because it follows closely the

representation used in EVIEWs.12Since the VAR is under-identified, Choleski decomposition is

often used to orthogonalize the innovations. The results of thisapproach are not, however, invariant to the ordering of the variables in

the VAR. In a recent paper, Koop et al. (1996) have proposed an

alternative approach, the generalized impulse response analysis which

does not have this shortcoming. This is achieved by examining the

shock in one of the variables, and integrating the effect of other shocks

using an assumed or historically observed distribution of the errors.

However, we do not explicitly pursue this complication here.13In general, choosing a different ordering of the variables in the

vector yt produces different shocks and, thus the effects of the shock on

the system depend on the way the variables are arranged in the vector

yt. To account for this difficulty, Sims (1981) recommends attempting

various triangular orthoganalizations and checking whether the results

are robust to the ordering of the variables. Furthermore: When

results are sensitive to the ordering of the variables, one may take some

(footnote continued)

progress by using a prior hypothesis about the structure (Sims, 1981,

p. 288).

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where pj is the jth column of P: Therefore, theorthoganalized response at lag p is given by CpP where

P is a K K lower-triangular matrix with the SDs of u

along the main diagonal such that PP0 O:14 The row icolumn j element of CpP is the effect of a one SD

orthogonalized shock to yj;t on yi;tp; holding all other

shocks at all dates constant. The expression in theparentheses is the contribution of the jth orthogonalized

innovation to the mean square error of the n-period

forecast.

2.6. Empirical results and analysis

Table 4 reports the ADF tests and the PhilipsPerron

Tests for the stationary of each variable, over the 1978

quarter 1 to 2000 quarter 3 time period. For the level

series, both the ADF tests and the PhillipsPerron tests

(Phillips and Perron, 1988) do not reject the null

hypothesis of a unit root (nonstationary) at the 5percent, or even 1 percent, level. After first differencing,

each series rejects the null hypothesis of nonstationary

at the 1 percent level.

Evidence from both the ADF and the PhillipsPerron

tests thus suggest that all variables are integrated of the

order one, I1; implying that the series are stationary inthe first difference. Since all the series are nonstationary

at the levels and integrated of the same order one, this

suggests a possibility of the presence of cointegrating

relationship between oil prices and the Singapores

aggregate economic variables. The subsequent section

explores such a possibility.

2.7. Johansen cointegration analysis

The Johansen cointegration test (Johansen, 1988,

1991; Johansen and Juselius, 1990) is carried out to test

for cointegrating relationships between oil price and the

three Singapore macroeconomic variables. Prior to

performing the Johansen cointegration test, variables

are entered as levels into a VAR to determine the

optimal number of lags needed in the cointegration

analysis. Three criterions, the Akaike information

criterion (AIC) (Akaike, 1969), Schwarz criterion (SC)

and the likelihood ratio (LR) test are applied todetermine the optimal lag length needed.15

2.8. Optimal lag length selection

An arbitrary choice of a maximum of 8 lag intervals

(or 2 years) is chosen. Table 5 reports the AIC and SC

statistics from lag 1 to 8 in the VAR.

The statistics in bold indicates the optimal lag length

chosen by the AIC and SC criterion, respectively. Since

the results from the AIC and SC are different, the LR

test is applied to test for the hypothesis of lag 1 against

lag 3.16 The resulting LR test statistics of 98.578 reject

the null hypothesis of 1 lag, thus suggesting that 3 lags is

the optimal choice in the VAR specification.

2.9. Establishing the number of cointegrating vectors

The Johansens multivariate cointegration technique

is employed to the system of four integrated variables of

order one, as reported in the above section. A uniform

lag structure of 3 is used based on the procedure

determined in the above section. We use the test

assumption in Johansens test, which allows for a linear

deterministic trend in the data series, and an intercept in

the cointegrating equation.17 The results of the multi-

variate cointegration analysis are reported in Table 6.

As shown in Table 6, the Johansens test results

suggest the existence of 1 cointegrating vectors present

at the 1 percent significance level.

2.10. Vector error correction (VECM) estimates

Based on the Johansens results, a VECM with 3-

quarter lags that is restricted with one cointegrating

vector is estimated.18 To make sure that the estimated

VECM is correct, the residual autocorrelation test is

performed. The results of the test indicate that the

residuals of the estimated VECM are approximately

uncorrelated, indicating that the estimated VECM isapproximately correctly specified.19 It is to be noted that

the coefficient estimates of the VECM are not of direct

interest in this empirical work. Instead, the concentra-

tion is on the impulse response and VDC analysis,

generated from the estimated VECM.

2.11. Impulse response analysis

Using the estimated VECM, an impulse response

analysis is performed to study the impact of an oil price

shock on the Singapore macroeconomy. The results of

14We use O here to differentiate the VDC from the impulse

response.15Refer to Enders (1995) for detailed illustration of the AIC, SC and

LR tests.

16The LR test statistics can be computed as follow: LR 2l1

l3; where l is the log likelihood statistic of the estimated VAR. It is tonote that to compute the LR test appropriately, VAR(1) and VAR(3)

must be estimated using the same sample period. The log likelihood

statistics for VAR(1) and VAR(3) in this case is 998.223 and 1047.512,

respectively. And the computed LR test statistics is 98.578. The test is

conducted by comparing the test statistics with the critical values from

the w2 table.17Two of the series (GDP and CPI) show a distinct upward trend.

Oil price and unemployment rate did not exhibit such a trend. Our

chosen test assumption represents the best compromise between these

factors.18The results of the estimated VECM are presented in Appendix A.19Results of the test are presented in Appendix B.

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the IRFs are subjected to the ordering of the variables.

To avoid this problem, a total of 24 possible order

combinations are tried. No significant differences in the

shape of the IRF are observed from all the various

possible order combinations.

Since it is known in the literature that oil price shocks

usually have an immediate direct impact on inflation,

and a lag effect on GDP and unemployment, the order

of LCPI, LGDP, UN are chosen.20 Fig. 1 presents the

impact on real GDP, CPI and unemployment rate over

a period of 30 quarters to a positive shock in real oil

prices that is equal to one SD innovation shock.

As shown in Fig. 1 Panel A, a positive one SD shock

to the real oil price leads to a slow down in Singapores

real GDP only after the fifth quarter. In fact, real GDP

shows a general declining pattern after that quarter,

suggesting that oil price shocks have a delayed negative

impact on real GDP. Such finding is consistent with

Table 4

Tests for stationary

Variable t-statisticsa,b Critical value at the 1

percent level

Classification I0; I1

Levels First difference

Augmented DickeyFuller test

Test assumptions: intercept

LOIL 1.263 [2] 7.709 [1] 3.50 I1

LGDP 0.516 [1] 7.361 [0] 3.50 I1

LCPI 2.963 [4] 5.432 [0] 3.50 I1

UN 2.203 [1] 4.577 [4] 3.50 I1

Test assumptions: intercept and trend

LOIL 3.135 [3] 7.668 [1] 3.50 I1

LGDP 2.816 [3] 7.331 [0] 3.50 I1

LCPI 3.879 [6] 5.953 [0] 3.50 I1

UN 2.745 [2] 4.547 [4] 3.50 I1

PhillipPerron testc

Test assumptions: intercept

LOIL 1.468 7.377 4.06 I1LGDP 0.484 7.498 4.06 I1

LCPI 3.078 5.399 4.06 I1

UN 2.216 7.445 4.06 I1

Test assumptions: intercept and trend

LOIL 2.427 7.335 4.06 I1

LGDP 1.972 7.472 4.06 I1

LCPI 2.327 5.947 4.06 I1

UN 2.256 7.406 4.06 I1

aAll t-statistics reported here are significance at the 1 percent level.bNumbers in squared brackets are the numbers of lagged differences p used in augmented estimated equation.cThe truncation lag for a total 91 observations is 3.

Table 5AIC and SC statistics from VAR (1) to VAR (8)

Lag intervals AIC SC

1 22.2064 21.65089

2 22.431 21.42436

3 22.6253 21.1614

4 22.5107 20.58333

5 22.5451 20.14784

6 22.4229 19.54916

7 22.328 18.97113

8 22.1476 18.30073

Table 6Johansens cointegrating vectors

Eigenvalue Likelihood

ratio

5 Percent

critical value

1 Percent

critical value

Hypothesized

no. of CE(s)

0.314205 58.46603 47.21 54.46 Nonea

0.191746 25.27445 29.68 35.65 At most 1

0.071616 6.541139 15.41 20.04 At most 2

2.18E-05 0.001918 3.76 6.65 At most 3

aRejection of the hypothesis at 1 percent significance level.

20For example, the EMF 7 Working Group Report ( EMF, 1987)

had reported that oil price shock would produce an immediate burst to

inflation. Impact of an oil price shock on GDP and unemployment

(footnote continued)

may follow certain lags. An economy may have kept some amount of

oil reserves for contingency reasons and thus is able to sustain the

impact of rising oil prices in the short run.

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those of Hamilton (1983) and Mork (1989), who find

decreases in real GDP (or GNP) after an oil price

shock.21

Panel B in Fig. 1 shows the impact of an oilprice shock on inflation in Singapore. As expected,

inflation measured by the CPI shot up immediately

following the oil price shock. The maximum impact is

reached at around the 12th quarter following the one SD

real oil price shock. Consistent with theory, an oil price

shock causes inflationary pressure on the Singapore

economy. Finally, the impact of the oil price shock on

unemployment is shown in Panel C of Fig. 1. Similar to

real GDP, there is a lag effect on unemployment.

Unemployment rate only starts to increase after the fifth

quarter and it exhibits an upward inclination pattern all

the way up to the 14th quarter. Thus, oil price shock

does affect unemployment rate in the Singapore

economy negatively.22 Such negative impact of oil prices

on unemployment has also been found. (Carruth et al.,

1998).23

As evident from the above findings, it seems to

suggest that Singapores macroeconomic performance

(measured by the selected three variables) is adverselyaffected by an oil price shock. However, as shown in

Fig. 1, the impact of the oil price shock on the economy

seems to be rather marginal. The reasons for such small

(insignificant) impacts are explored in later sections.

2.12. Variance decomposition (VDC) analysis

The VDC provides a tool of analysis to determine the

relative importance of real oil price shock in explaining

the volatility of the three macroeconomic variables. A

similar ordering as the impulse response analysis isapplied for the VDC. The results of the VDC over 30

quarters are presented graphically in Fig. 2.24

The results of the VDC suggest that an oil price shock

is not a major source of volatility for the macroeco-

nomic variables included in the VECM. As shown in

Fig. 2, oil price shock is a minimal source of disturbance

to GDP and unemployment rate over the examined

periods. The largest volatility to an oil price shock

happens to CPI. Even then, oil price shock is only able

to account up to a maximum of 17.5 percent in

-0.001

0.000

0.001

0.002

0.003

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Response of LGDP to LOIL

0.000

0.002

0.004

0.006

0.008

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Response of LCPI to LOIL

-0.0010

-0.0005

0.0000

0.0005

0.0010

0.0015

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Panel C

Panel A Panel B

Response of UN to LOIL

Fig. 1. Impact to one SD innovation shock in oil price.

21However, it is to be noted that their studies showed a much greater

impacts on real GDP/GNP as compared to the results presented here.22Increasing oil prices might indirectly raise the business costs,

which is expected to impact unemployment level negatively.23Using an efficiency-wage model, Carruth et al. (1998) found that

real oil price is able to explain efficiently the main post-war movements

in US unemployment level. 24Note that the scale applied for each of the graphs is different.

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explaining the variance attributed to CPI over the

examined periods.

Although the impulse response analysis has shown

that a positive one SD shock to real oil prices couldadversely affect the macroeconomic variables, the VDC

has shown that the impact of an oil price shock is

marginal. Following the above empirical analysis, the

remaining sections in this paper explore some cursory

evidence to support our empirical findings.

2.13. Singapore experiences of previous oil price shocks

Since independence in 1965, Singapore has experi-

enced three oil price shocks. Fig. 3 presents a time-series

plot of Singapores macroeconomic performance from

1970 to 1999. Periods in which the oil price shock

occurred are highlighted in the graph.

As presented in Fig. 3, it seems that Singapores

macroeconomic performances as measured by the real

GDP growth, inflation level and unemployment rates

had been adversely affected whenever an oil price shock

occurs. During the first oil price shock in 1973/74,

inflation in Singapore rose to a double-digit range.

Although Singapore did not enter a recession following

the oil price shock, real GDP growth did fall subse-

quently. Unemployment rate also went up slightly after

the first oil shock as shown in Fig. 3. The impact of the

second oil price shock on the Singapore economy is

more moderate than the first oil price shock. Inflation

did rise but it did not enter a double-digit range. The

impacts on real GDP growth and unemployment rate

are not serious as compared to the first oil price shock.The impact of the third oil price shock is even less

severe. This could be attributed to transitory nature of

the third oil price shock (Tatom, 1993). As shown in

Fig. 3, inflation only rose moderately during the 1990 oil

price shock. Impacts on real GDP growth and

unemployment rate were also only marginal.

Singapore experiences of past oil price shocks suggest

that the impacts of oil price shocks had reduced

substantially. These experiences are consistent with the

empirical results obtained from the impulse response

and VDC analysis. In the next section, further evidence

is presented to show that the adverse impacts of oil

price shock on Singapore macroeconomy is expected to

have weakened.

2.14. Oil vulnerability measurements

This section examines two oil vulnerability measure-

ments, namely, Singapores oil intensity and Singapore

expenditure on oil consumption as a percentage of

GDP.

Oil intensity, defined as oil consumption per dollar of

GDP, is one of the measurements of the economys

vulnerability to oil disruptions (Kendell, 2000). Fig. 4

0

1

2

3

4

5

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

0

5

10

15

20

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

0

2

4

6

8

10

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Percent UN variance due to LOIL

Percent LGDP variance due to LOIL Percent LCPI variance due to LOIL

Fig. 2. Variance decompositions.

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Fig. 5,26 expenditure on oil consumption as a percentage

of GDP has also shown a general declining pattern since

1989. This provides further justification that Singapores

dependence on oil has fallen over time, thus implying a

weakening of oil pricemacroeconomy relationship in

Singapore.

The empirical results reported above have found that

oil price shock affects the Singapores macroeconomy

only marginally. This could be due to inclusion of more

recent data. As illustrated in the above section, both oil

intensity and expenditure on oil consumption as a

percentage of GDP have fallen over time, thus providing

evidence that oil price shock should not have great

adverse impact on Singapores macroeconomic perfor-mance. Experience from past oil shocks discussed in the

above section has also suggested the weakening of oil

pricemacroeconomy relationship in Singapore. These

evidences strengthen the validity of our econometric

analysis.

3. Conclusion

As Singapore continues to develop into a fully

industrialized country, it would have to secure supply

of energy from imports, which is essential for the

economy to grow. Subsequently, assuring the supply of

energy brings verification of the relationship between oil

price fluctuations and macroeconomy. Much of the

research on the oil price fluctuations and macroeconomy

has been concentrating on developed economies, and a

formal study on the impact of oil price changes on the

Singapore macroeconomy seems to be lacking. An

empirical modelling technique using Johansen cointe-

gration methodology is applied to examine the long-

term relationship between the oil price fluctuations and

the Singapore macroeconomy. A VECM is estimated

from the cointegration analysis. Impulse response

analysis and VDC are performed to quantify the

impacts of oil price shock on the Singapore macro-

economic variables.

The empirical findings of this study suggest that oil

price shocks do adversely affect Singapores macro-

economic performance. This is consistent with what

economic theory suggests. However, the impacts

of an oil price shock on the examined variables areonly marginal. Such findings are consistent with the

findings reported by Monetary Authority of Singapore

(2000), Ministry of Trade and Industry (2000) and

Abseysinghe and Wilson (2000). This study further

examines Singapore experiences of past oil shocks.

Singapores oil intensity and expenditure on oil con-

sumption as a percentage of GDP have fallen over time,

which provide evidence that oil price shock should not

have great adverse impacts on Singapores macroeco-

nomic performance. All such analyses also support the

empirical results that the impact of oil price shock on

Singapore economy would be trivial. A similar conclu-

sion as Hooker (1996) could thus be drawn for

Singapore, i.e. a diminishing oil pricemacroeconomy

relationship.

However, there is room for refining the empirical

findings. The VECM methodology used in this study

might be overly simplified. A natural progression in this

direction would be to use a structural vector autoregres-

sion (VAR) methodology. Hoffman and Rasche (1997)

pointed out that the construction of IRF from VECM is

not as advanced as the structural VAR modelling. Thus

future research in this area could be pursued using a

structural VAR.

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Fig. 5. Expenditure on oil consumption (percent of nominal GDP), 19891999. Source: plotted using data from Singapore daily oil consumption

data from BP Amoco (2001) Statistical Review of World Energy 2000 and GDP at current market price obtained from the DOS website at http://

www.singstat.com.sg/.

26The plot is calculated using Dubai crude oil prices and Singapore

oil consumption data obtained from BP Amoco Statistical Review of

World Energy and GDP at current market price obtained from DOS

website. Note that the plot is only an estimation from the source of

information.

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Table 7

Cointegrating equation CointEq1

LGDP(-1) 1.000000

LCPI(-1) 3.212990

(0.30830)

(10.4216)

UN(-1) 13.08104

(4.15548)

(3.14790)

LOIL(-1) 0.309132

(0.07813)

(3.95650)

C 9.468666

Error correction: D(LGDP) D(LCPI) D(UN) D(LOIL)

CointEq1 0.003212 0.016242 0.011118 0.135428

(0.01120) (0.00523) (0.00248) (0.11003)(0.28672) (3.10725) (4.47445) (1.23078)

D(LGDP(-1)) 0.091229 0.026251 0.087639 2.841578

(0.11931) (0.05567) (0.02646) (1.17197)

(0.76466) (0.47151) (3.31156) (2.42462)

D(LGDP(-2)) 0.034003 0.039474 0.106701 2.498080

(0.12376) (0.05775) (0.02745) (1.21569)

(0.27475) (0.68353) (3.88687) (2.05487)

D(LCPI(-1)) 0.145771 0.310339 0.028541 0.266972

(0.23107) (0.10783) (0.05125) (2.26979)

(0.63086) (2.87815) (0.55685) (0.11762)

D(LCPI(-2)) 0.056818 0.004213 0.055830 3.989751(0.22885) (0.10679) (0.05076) (2.24799)

(0.24828) (0.03945) (1.09982) (1.77481)

D(UN(-1)) 1.192873 0.153668 0.015644 11.82410

(0.45254) (0.21118) (0.10038) (4.44535)

(2.63595) (0.72768) (0.15584) (2.65988)

D(UN(-2)) 0.216046 0.249721 0.003703 3.895650

(0.48716) (0.22733) (0.10806) (4.78547)

(0.44348) (1.09848) (0.03427) (0.81406)

D(LOIL(-1)) 0.016329 0.008879 0.004720 0.223964

(0.01124) (0.00524) (0.00249) (0.11041)

(1.45278) (1.69292) (1.89330) (2.02848)

D(LOIL(-2)) 0.004793 0.002932 0.005803 0.300173

(0.01077) (0.00503) (0.00239) (0.10582)

(0.44496) (0.58319) (2.42847) (2.83663)

C 0.015477 0.002768 0.004006 0.028082

(0.00367) (0.00171) (0.00081) (0.03603)

(4.21930) (1.61726) (4.92337) (0.77937)

R-squared 0.178742 0.444923 0.397005 0.326558

Adj. R-squared 0.083982 0.380876 0.327428 0.248853

Sum sq. resids 0.014895 0.003244 0.000733 1.437308

SE equation 0.013819 0.006449 0.003065 0.135746

F-statistic 1.886250 6.946781 5.706024 4.202548

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The explanatory variables applied here might not

accurately represent the full array of macroeconomic

variables. This is mainly due to the shortage of data that

is not easily obtainable in Singapore. The inclusion of

additional macroeconomic variables may generate

different kinds of results. And also unlike similar studies

done in other economies, the number of observations

used in the study is relatively small. Recent studies on

the impacts of oil shocks have employed more than 150

observations (for example, Lee et al., 1995; Hooker,

1996). Thus, the estimation in this study may suffer from

the degree of freedom problem. This study only explores

the unidirectional impact of oil shock. Asymmetric

effects, which are widely explored in more recent

research, are not attempted here.27

This study has suggested that oil shocks might not

notably affect Singapores macroeconomic performance.

Singapore has reported economic growth of about 10

percent and inflation rate of about 1 percent in the year

2000 despite the 2000 oil price surge. This provides

further evidence that the impacts of oil price shock on

Singapores macroeconomic performance is marginal.

The declining trend of oil intensity suggests that oil

pricemacroeconomy relationship in Singapore might

Table 7 (continued)

Cointegrating equation CointEq1

Log likelihood 257.2314 324.3038 389.7499 56.17427

Akaike AIC 5.618894 7.143268 8.630681 1.049415

Schwarz SC 5.337379 6.861752 8.349165 0.767900

Mean dependent 0.018367 0.005787 8.56E-05 8.69E-05

SD dependent 0.014439 0.008195 0.003738 0.156627

Determinant residual covariance 7.17E-16

Log likelihood 1034.875

Akaike Information Criteria 22.51988

Schwarz criteria 21.28122

Table 8

Correlogram of the residuals of the estimated VECM

Autocorrelation Partial correlation AC PAD Q-stat Prob

1 0.004 0.004 0.0014 0.9702 0.052 0.052 0.2403 0.887

3 0.000 0.000 0.2403 0.971

4 0.074 0.077 0.7241 0.948

5 0.116 0.116 1.9409 0.857

6 0.076 0.087 2.4758 0.871

7 0.207 0.198 6.4482 0.488

8 0.059 0.077 6.7727 0.561

9 0.005 0.009 6.7750 0.661

10 0.192 0.173 10.343 0.411

11 0.013 0.024 10.359 0.498

12 0.021 0.023 10.402 0.581

13 0.015 0.012 10.425 0.659

14 0.076 0.060 11.019 0.685

15 0.032 0.035 11.128 0.743

16 0.026 0.062 11.202 0.79717 0.117 0.057 12.672 0.758

18 0.100 0.082 13.767 0.744

19 0.039 0.069 13.930 0.788

20 0.226 0.279 19.627 0.481

21 0.014 0.022 19.650 0.544

22 0.014 0.006 19.672 0.604

23 0.078 0.056 20.390 0.618

24 0.033 0.074 20.522 0.667

27Examples include Mork (1989) and Mork et al. (1994).

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weaken even further in the future. Following the

weakening of such relationship, it would be of interest

to examine the degree of oil efficiency as an energy

source in Singapore in future studies. It would also be

relevant to examine the relationship between oil prices

and the terms of trade (TOT). Trade statistics reviewed

in the first section has shown that Singapore hasconsiderable oil trading transactions. Any changes in

oil prices may have an impact on the TOT considerably.

Appendix A

Vector error correction estimates are given in Table 7.

Appendix B

Diagnostic checks for the estimated VECM are given

in Table 8.

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