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Oil Price Fluctuations and Macroeconomic Performances in Asian and Oceanic Economies
Youngho Chang
Division of EconomicsNanyang Technological University
30th USAEE/IAEE North American Conference9 – 12 October 2011
Capital Hilton, Washington, D.C.
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Outline
• Introduction– Oil price fluctuations and the economy– Causality between oil prices and macroeconomic variables
• Objectives • Data• Test Results and Interpretations– Impulse response– Variance decomposition
• Conclusions
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Oil Price Fluctuations and the Economy
• Macroeconomic implications of oil price shocks identified since the 1970s• Research largely indicated a negative relationship, with oil price increases
preceding almost all recessions in the United States after World War II– Hamilton (1983, 1996 and 2004)– Gisser and Goodwin (1986)– Burbridge and Harrison (1984)
• Since then, other country studies have been conducted that further support this stand;– New Zealand (Gounder and Barleet, 2007) – Greece (Papapetrou, 2001)
• However, a declining oil-price and macroeconomic relationship has also been found– Mork et al. (1989, 1994) – Abeysinghe (2001)
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Summary of Literature Review Author Year Countries observed Macroeconomic Variables ConclusionsHamilton 1983, 1996, 2003 Significant negative relationship
Gisser and Goodwin 1986 Negative and relatively stable relationship
Burbridge and Harrison 1984 5 OECD countries SeveralSubstantial initial impact on macroeconomic indicators;
also a declining impact of oil price shocksJiménez-Rodríguez
and Sánchez2005 Several GDP Significant impact on macroeconomy
Gounder and Bartleet 2007 New Zealand GDP and inflation Direct relationship for GDP, indirect relationship with inflationPapapetrou 2001 Greece Significant negative casual relationship
Hooker 1996 USA Changing and unstable oil price-macroeconomic relationship Mork et al. 1990, 1994
Abeysinghe and Wilson 2000
Chang and Wong 2003 SingaporeGDP, inflation
and unemploymentMarginal impact on macroeconomic indicators
Blanchard and Gali 2007 Several GDP and inflation Changing impact of oil prices Barsky and Kilian 2001, 2004 Limited impact of oil price shocks
Kilian 2009 Little or no impact of oil price shocksFerderer 1996
Lardic and Mignon 2006 USA and EuropeCunado and Gracia 2005 6 Asian countries
Fuhrer 1995Hooker 2002
Barksy and Kilian 2004 Significant impact on inflationLeBlanc and Chinn 2004 Moderate impact on inflation
Lescaroux and Mignon 2008Chen 2009
Cunado and Gracia 2005 6 Asian countries Short run effect on inflationKumar 2005 India Significant impact on inflation
Loungani 1986 USABurbidge and Harrison 1984 5 OECD countries
Darby 1982 SeveralHamilton 1983
Gisser and Goodwin 1986Uri 1996
Dorgul 2010 TurkeyCarruth et al 1998 USA Lagged effect on unemployment
GDP
USA GDP
USA
Inflation
Unemployment
USA
Several
Several
USA
GDP
Several
Significant relationship
Long run relationship
Significant pass through effect to inflation
Declining impact on inflation
Declining oil price-macroeconomic relationship
Asymmetric relationship
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Causality between Oil Prices and Macroeconomic Variables
• Early studies have found the inverse relationship with oil price and particularly GDP (Hamilton, 1983)
• Most studies indicated causality running from oil price to real GDP or economic growth, especially for oil-importing countries– Lescaroux and Mignon (2008); Du, He and Wei (2010); Cunado and
Gracia (2005)• However, there are also studies which show no causality between the two
– Bartleet and Gounder (2007); Li, Ran and Voon (2010)• General results of causality running from oil price to inflation has been
found – Lescaroux and Mignon (2008); Du, He and Wei (2010); Cunado and
Garcia (2005); Jalles (2009)• For unemployment, most countries indicated a causality running from oil
price to unemployment – (Lescaroux and Mignon, 2008)
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Summary of Granger Causality Studies
Author Year Oil Price→GDP GDP→Oil Price GDP↔Oil Price No CausalityLescaroux and Mignon 2008 Saudi Arabia, UK, and to a less extent,
QatarDu, He and Wei 2010 China: Sample fom 2003-2008 shows causality
at 1% and 5% level of significance. Hanabusa 2009 Japan: In mean, with a 9-month lag, and in
variance with a lag of 1-month. In mean, with a 6-month lag. In variance,
with a lag of 1 month.Prasad, Narayan and
Narayan 2007 Fiji Islands: In the long run, at 5% significance
level.Jalles 2009 France
Li, Ran and Voon 2010 Hong KongBartleet and Gounder 2007 New Zealand for
Economic Growth
Author Year Oil Price→CPI CPI→Oil Price CPI↔Oil Price No CausalityLescaroux and Mignon 2008 Oil prices have a large influence on CPI for the
United Arab Emirates, United Kingdom, Mexico and Libya
Du, He and Wei 2010 China: Similar to the results for the causality in the GDP section, there are no significance in the
1995-2001 period sample but there are causality running from oil price to CPI at the 1% and 5%
level, but not the other way round.Cunado and Grancia 2005 Japan; Singapore; Thailand
Jalles 2009 France
Author Year Oil Price→Unemployment Unemployment→Oil Price Unemployment↔Oil Price
No CausalityLescaroux and Mignon 2008 Great influence of oil prices on the
unemployment rate in the United States, Luxembourg, France, Canada andVenezuela
Four oil importing countries (China, Greece, Spain and the United States)
Li, Ran and Voon 2010 Hong Kong
GDP
CPI
UNEMPLOYMENT
Note: → denotes the direction of Granger-Causality while ↔denotes bi-directional Granger-Causality
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Objectives
• To explore the impact of oil price fluctuations on macroeconomic variables for economies in ASEAN, the Asia-Oceanic Region and South Asia
• To investigate the varied patterns of the impact by different categories of economies in the region– Oil-exporting economies– Small-open economies– Large countries with rapid economic growth
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Vector Autoregression Model (VAR)
• Investigate the relationship between oil price and the macroeconomic variables – When they are not cointegrated
• Equation:
o y is an n-vector of endogenous variables o Bk is an (n × n) matrix of regression coefficients to be estimated. o The error term, ut, is assumed to be independent and identically
distributed with a zero mean and constant variance. o Selection of the appropriate lag length, p, is important. 4 is chosen
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Data
• Variables – GDP, CPI and unemployment rate
• Scope – 17 countries (Asia-Pacific and ASEAN region)
• Sources– CEIC data manager – International Financial Statistics (IFS) CD-ROM 2010– Specific government sources and websites
• Type of Oil – Dubai crude “Arab Gulf Dubai” measured in FOB $US/BBL
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Unit Root Tests for Stationarity
• Phillip-Perron (PP) unit root tests are conducted• Null hypothesis – Series are non-stationary– If the p-value is less that 0.1 (10% level of significance), the
null hypothesis of non-stationarity is rejected
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Unit Root Tests for Stationarity: GDP
• GDP Time-Series– All series show non-stationarity except for the Philippines
and Vietnam
• Oil Price Series Corresponding to the GDP– All except for the Philippines
*Brunei and Vietnam were not be examined due to different orders of integration
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Unit Root Tests for Stationarity: CPI
• CPI Time-Series – Australia, Brunei, China, Japan, the Philippines and South
Korea • Null hypothesis of non-stationarity is rejected• All other countries are non-stationary
• Oil Price Series Corresponding to the CPI– The Philippines: Null hypothesis for is rejected – Other countries: Rejected for the first differences– Australia, Brunei, China, Japan and South Korea
• Not studied due to different orders of integration
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Unit Root Tests for Stationarity: Unemployment
• Unemployment Rate Series – More varied results due to smaller sample sizes– Brunei, Cambodia, Malaysia and Thailand
• Null hypothesis of non-stationarity is rejected – The remaining countries (except for China and Vietnam)
• Rejected for the first differences
• Oil Price Series Corresponding to the Unemployment Rate– Non-stationarity is rejected only for Indonesia– The remaining series except Cambodia
• Rejected for first differences– Analysis omitted for 7 countries due to different order of integration
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Australia Brunei Cambodia China India Indonesia Japan Laos Malaysia
lnGDP I(1) I(2) I(1) I(1) I(1) I(1) I(1) I(1) I(1)
lnOil_GDP I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1)
lnCPI I(0) I(0) I(1) I(0) I(1) I(1) I(0) I(1) I(1)
lnOil_CPI I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1) I(1)
UN I(1) I(0) I(0) I(2) - I(1) I(1) - I(0)
lnOil_UN I(1) I(1) I(2) I(1) - I(0) I(1) - I(1)
MyanmarNew
Zealand Philippines SingaporeSouth Korea Taiwan Thailand Vietnam
lnGDP I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(0)
lnOil_GDP I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1)
lnCPI I(1) I(1) I(0) I(1) I(0) I(1) I(1) I(1)
lnOil_CPI I(1) I(1) I(0) I(1) I(1) I(1) I(1) I(1)
UN - I(1) I(1) I(1) I(1) I(1) I(0) I(2)
lnOil_UN - I(1) I(1) I(1) I(1) I(1) I(1) I(1)
33 (shaded) out of 49 pairs of variables proceeded with the cointegration test
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Cointegration Test
• Engle-Granger cointegration test• The critical value calculated according to the equation is -1.61
by MacKinnon (2010)• If the absolute value of the statistic is greater than |-1.61|– Null hypothesis is rejected– Proceed with Vector Error Correction model (VECM)
• If the absolute value of the statistic is less than |-1.61|– No cointegration between the two variables – Variance auto-regression (VAR) model adopted
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Cointegration Test
• GDP and Oil Price – Australia, India, Japan, South Korea and Thailand
• No cointegration
• CPI and Oil Price– India, Indonesia, Laos, Taiwan and Thailand
• No cointegration
– Malaysia, Myanmar, New Zealand, Singapore and Vietnam • Cointegration detected
• Unemployment Rate and Oil Price– Australia, Japan, New Zealand, the Philippines, Singapore,
South Korea and Taiwan • Cointegration
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Cointegration Test
• Observations of cointegration– GDP and oil price series: 9 countries– CPI and oil price series: 6 countries– Unemployment rate and oil price series: 7
countries• Mainly in developed Asian countries • Australia, Japan, New Zealand, Singapore, South Korea
and Taiwan • Developing nations studied have no cointegrating
relationship
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Australia Brunei Cambodia China India Indonesia Japan Laos Malaysia
lnGDP on lnOil_GDP -0.467643 - -4.342324 -2.433306 0.198985 -2.712333 -1.277553 -3.550048 -3.117576
lnCPI on lnOil_CPI - - -2.369161 - 0.333649 -0.179834 - -1.233173 -2.657299
UN on lnOil_UN -2.217284 - - - - - -2.160321 - -
Myanmar New Zealand Philippines Singapore South Korea Taiwan Thailand Vietnam
lnGDP on lnOil_GDP -3.381828 -2.701918 - -2.014756 -1.106708 -1.728377 -1.382402 -
lnCPI on lnOil_CPI -1.746361 -3.012051 - -1.819914 - -1.171726 0.103821 -1.626163
UN on lnOil_UN - -1.921504 -2.238887 -1.793251 -2.758843 -2.864638 - -
• Shaded boxes indicate cointegration• “-“ represents no cointegration between the variables (not integrated of the same order)
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Vector Error Correction Model (VECM)
• For two cointegrated variables, the VECM describes the data-generating process
• The Error Correction Term (ECT) shows how fast the relationship between the two variables converges towards its long-run equilibrium
• Impulse-Response Analysis• Variance Decomposition
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Impulse Response Functions
• Impact of a one standard deviation shock to the real oil price on three variables– GDP– CPI– Unemployment Rate
• Study of the 8-year impact• Depicted through graphical means
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Impulse Response Analysis for GDP
Varied impact of an oil price shock on GDP• Singapore and Taiwan
– Small-open economies– Delayed negative impact– Consistent with findings of previous
studies
• Malaysia and Indonesia – Net oil exporters in the past– Long-run positive impact on GDP due
to critical nature of oil, short run inelastic demand
– Strong support from some studies
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Impulse Response Analysis for GDP
Varied impact of an oil price shock on GDP• China
– Large economy with strong growth– Positive GDP impact from oil shock– Most energy needs met by coal, not oil– Robust economic growth in the past
despite increases in oil prices– Contradictory conclusions from some
studies
• New-Zealand– Another small open economy– Immediate negative impact followed by
positive trend– Positive and delayed effect from trading
partners, China and Australia
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Impulse Response Analysis for CPI
• Malaysia, New Zealand, Singapore Vietnam– Instantaneous increase after oil price
shock– But the inflationary increase is small– Improved Central Bank credibility in
fighting inflation– Even smaller impact for oil exporting
nations
• Cambodia– Lagged inflationary impact– Transmission through trading
partners such as Vietnam and Thailand
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Impulse Response Analysis for Unemployment• Australia, Japan, New Zealand, Singapore,
South Korea– Lagged positive impact of an oil price
shock on the unemployment rate– Increase in unemployment rate after
four or five years; support from past study
– However, scale of increase is nominal• Taiwan
– No lag; immediate uptick– Flexible labor market
• Australia– Delayed positive impact, but subsiding
effect on unemployment rate– Commodity-linked economy; benefits
from commodity price increase• Long-lasting impact on unemployment rate
(3 years)
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Variance Decomposition
• Impact of real oil price fluctuations on the long-run volatility of three variables– GDP– CPI– Unemployment Rate
• Study of the 8-year impact• Depicted through graphical means
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Variance Decomposition for GDP• Most economies including Cambodia, China, Indonesia,
Malaysia, Myanmar, Singapore – An oil price shock is a considerable source of GDP volatility– Impact not uniform over time
• Increasing impact for China and Indonesia • Decreasing impact for Cambodia and Singapore
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Variance Decomposition for CPI and Unemployment
• Substantial source of disturbance to CPI volatility over all examined countries– Oil price shocks account for over 10%
of CPI variance with an increasing impact over time
– Limited studies for comparison– New Zealand and Singapore CPI
volatility through oil has been studied previously
• Little research has examined the importance of an oil price shock on unemployment rate– Varied results across economies– Substantial impact on New Zealand,
Philippines and Taiwan, but negligible for Australia, Japan and Singapore.
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Granger-Causality
Cambodia Cambodia IndiaChina India Philippines
Indonesia IndonesiaLaos Laos
Malaysia MalaysiaMyanmar Myanmar
India ThailandThailand Vietnam
Australia New Zealand AustraliaNew Zealand Singapore Japan
Singapore Taiwan New ZealandTaiwan SingaporeJapan South Korea
South Korea Taiwan
UnemploymentRate
Emerging and Developing Economies
Advanced Economies
GDP CPI
In bold, Granger-Causality runs from oil price to the considered variable at 10% significance level.
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Conclusions
• Countries are classified according to their macroeconomic characteristics to form three broad categories
1. Asian countries that export oil and are in a position to gain an advantage from a positive oil price shock
2. Small open economies for which trade plays a big role in their economic activity
3. Large, rapidly growing economies
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Conclusions1. Asian Oil-exporting economies
– Includes Malaysia and Indonesia– Increase in the oil price causes their GDP to increase– Large percentage of the volatility in GDP is contributed by oil price variance– Signifies that oil price plays a substantial role in influencing their GDP
2. Small open economies– Includes Singapore, New Zealand and Taiwan– Negatively impacted by an oil price shock in the short-run but improves in
the long-run– Indirect positive effect through major trading partners causes resurgence in
their economic activity
3. Large and fast growing economies – Includes China and India– Negligible impact of an oil price shock on GDP– Small reliance on oil as a source of energy
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Thank you for your attention!
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