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Tiêu đề Relationships Between Natural Gas Production In Persian Gulf States And Natural Gas Consumption In The European Union
Tác giả Gaolu Zou, Dingsheng Feng, K. W. Chau
Trường học Chengdu University
Chuyên ngành Energy and Gas Market Analysis
Thể loại journal article
Năm xuất bản 2016
Thành phố Kitakyushu
Định dạng
Số trang 4
Dung lượng 171,46 KB

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Peer-review under responsibility of the organizing committee of CPESE 2016 doi: 10.1016/j.egypro.2016.10.206 Energy Procedia 100 2016 480 – 483 ScienceDirect 3rd International Confe

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1876-6102 © 2016 The Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license

( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

Peer-review under responsibility of the organizing committee of CPESE 2016

doi: 10.1016/j.egypro.2016.10.206

Energy Procedia 100 ( 2016 ) 480 – 483

ScienceDirect

3rd International Conference on Power and Energy Systems Engineering, CPESE 2016, 8-12

September 2016, Kitakyushu, Japan Relationships between Natural Gas Production in Persian Gulf States and Natural Gas Consumption in the European Union

Gaolu Zou a, *  , Dingsheng Feng b , K W Chau c

a School of Tourism and Economic Management, Chengdu University, Chengdu 610106, China b

Investment Company, Sichuan Normal University, Chengdu 610066, China c

The Ronald Coase Center for Property Rights Research, Faculty of Architecture, The University of Hong Kong, Hong Kong

Abstract

Qatar, Saudi Arabia, United Arab Emirates, Iraq and Kuwait hold abundant natural gas reserves This study examines the long-run and short-long-run relationships between natural gas production in the five Gulf states and consumption in the European Union (EU) The data consist of yearly time series covering the period from 1970 to 2012 We tested for cointegration and the Granger causality in a first-differenced VAR The tests did not indicate a long-run equilibrium or any short-run dynamics We suggest that the Gulf states have a huge potential to increase and establish a stable gas supply to the EU

© 2016 The Authors Published by Elsevier Ltd

Peer-review under responsibility of the organizing committee of CPESE 2016.

Keywords: consumption; Persian Gulf states; natural gas; production; reserves

1 Introduction

In 2012, proven natural gas reserves in Qatar accounted for 13.4% of the world’s total reserves (187.3 trillion cubic meters or tcm), ranking third after Iran (18.0%) and Russia (17.6%) [1] Proven gas reserves in Saudi Arabia, the United Arab Emirates, Iraq, and Kuwait constituted 4.4%, 3.3%, 1.9%, and 1.0% of the world’s total reserves, respectively Therefore, excluding Iran, these five Persian Gulf states held 24% of the world’s proven reserves With the deteriorating Ukraine crisis, the EU urgently expects to decrease its heavy reliance on Russian gas [2] It has been suggested that significant potential suppliers include Gulf states, e.g [3] This study investigates the long-run and short-run relationships between natural gas production in the five Persian Gulf states and consumption in the EU

in order to provide more evidence for the substitution of gas suppliers

2 Methodology

We estimate the Johansen trace statistics and cointegrating vector(s) [4] The Phillips-Ouliaris test can provide clues to the cointegration [5] The study tests for unit root using both the Augmented Dickey-Fuller (ADF) and



* Corresponding author Tel.: +86-28-84617900; fax: 020-28819702 ext.12191

E-mail address: zougaolu@vip.163.com

© 2016 The Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license

( http://creativecommons.org/licenses/by-nc-nd/4.0/ ).

Peer-review under responsibility of the organizing committee of CPESE 2016

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Phillips-Perron (PP) techniques Moreover, Perron structural break tests are conducted using the mixed innovational outlier (IO) model C [6] If series are cointegrated, an error-correction model (ECM) can be used to represent the long-run relationship [7] An Engle-type ECM in first differences is formulated as follows:

, 2

1 1

1

t p

j j j p

i i i

y O D'  E' I H

' ¦  ¦  z t1 (1) where zt1 is a cointegrating vector or error-correction (EC) term However, if the series are integrated of order one but not cointegrated, we estimate a traditional vector autoregressive (VAR) model in first differences by removing the EC term from Equation (1) In the ECM or VAR model, short-run and/or long-run effects in terms of elasticity and Granger causality can be determined [8] Wald-Ȥ2

statistics are estimated for the null hypothesis of no

Granger causality from x to y:

0 : ȕj

0

H (2)

3 Data

The data were gathered from BP [1] Natural gas production (PRODUCTION) represents the total natural gas

production of Qatar, Saudi Arabia, United Arab Emirates, Iraq, and Kuwait, while natural gas consumption

(CONSUMPTION) represents the total natural gas consumption in the EU The data were transformed into natural

logarithms before they were used to conduct empirical tests and were presented as yearly series covering the period from 1970 to 2012 Natural gas production and consumption seemed to trend together since the mid-1990s (Fig 1)

Fig 1 Natural gas production and consumption in logarithms

4 Empirical Results

Table 1 Unit root tests

Log variable Statistic (k, p-value) Statistic (k, p-value)

First difference -3.45(2, 0.06) -6.42(2, 0.00)

First difference -3.36(2, 0.07) -

Notes: For ADF tests, we selected the lag length k using SIC; for PP tests, the Newey-West method was used [9] However, the number of lags was set between two and eight on a general-to-specific principle [10] MacKinnon one-sided p-values were used [11]

For the variable PRODUCTION, ADF and PP tests consistently suggested one unit root For the variable

CONSUMPTION, the ADF test suggested one unit root, but the PP test suggested no unit roots Moreover, we did

not detect a break date in the data (Table 2) Hence, we considered these two variables as I(1) series based on the

recommendation in [12]

Table 2 Structural break tests

Į

b

Notes: We chose lag length between two and nine on a general-to-specific basis [10] The t-statistic in absolute value was above or equal to 1.8

b

Tˆ was the possible break date detected The break date was selected between 1984 and 2000 One-sided test critical values for the sample size of

70 were -6.32, -5.59, and -5.29 at the 1%, 5%, and 10% significance levels, respectively [6]

1 2 3 4 5 6 7

1970 1975 1980 1985 1990 1995 2000 2005 2010

LOG PRODUCTION LOG CONSUMPTION

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We used the Akaike information criterion (AIC) to select the optimal model for the Johansen trace tests Fig 1

indicates that the data contain the intercept and trend Hence, we focused on the examination of Model 4 based on

[13] When lags were two and four, we obtained lower AIC; however, when lags were four, the cointegrating

equation was autocorrelated Hence, we selected Model 4 with a lag length of two for the trace test Moreover, the

Johansen-type ECM satisfied the criterion of multivariate normality The tests did not detect a cointegrating vector

(Table 3) Furthermore, the residual-based tests suggested no cointegration (Table 4) Hence, we estimated a

first-differenced VAR, in which the tests did not detect any short-run Granger causality between PRODUCTION and

CONSUMPTION (Table 5)

Table 3 The Johansen multivariate cointegration trace tests

Notes: *5% asymptotical critical value in [14] **p-value in [15] ***Cheung-Lai finite-sample critical value [16].

Table 4 Phillips-Ouliaris residual-based cointegration tests

Notes: Null hypothesis was that series were not cointegrated *p-value in [11]

Table 5 Estimates of first-differenced VAR and Granger causality tests

Dependent:

CONSUMPTION

Time Estimate

(t-statistic)

Multivariate normality

(p-value)*

Q-statistic (p-value,

lags)**

Granger causality

(p-value)

2.66 (0.26)

2.53 (0.28)

Constant 0.01 (1.22)

Notes: Data were converted to logarithmic form Appropriate lags depends on data [10] Tests made AIC as small as possible VAR satisfied

multivariate normality and removed autocorrelations *Jarque-Bera statistic based on Cholesky factorization matrix **Portmanteau

autocorrelation adjusted Q-statistic R-squared: 0.41 Adj R-squared: 0.34 F-statistic: 6.18 Akaike AIC: -3.64

5 Concluding remarks

The five Persian Gulf states (Qatar, Saudi Arabia, United Arab Emirates, Iraq, and Kuwait) hold about a quarter

of the world’s proven natural gas reserves The EU has shown an increasing reliance on external gas supplies, such

as those from the Gulf states Hence, this study investigates the long-run and short-run relationships between natural

gas production in the five Persian Gulf states and natural gas consumption in the EU However, the cointegration

tests did not indicate a long-run equilibrium, and the granger causality tests did not detect any short-run dynamics

Therefore, the gas production showed neither long-run nor short-run effects on the EU’s gas consumption

We argue that gas imports in the EU from the Gulf states were small In particular, in 2012, Europe and Eurasia

constituted only 29.5% of Qatar’s liquefied natural gas exports [1], which may undermine the potential long-run and

short-run effects An enormous potential exists for the Gulf states to increase their gas supply to the EU

References

[1] BP Statistical review of world energy 2013 http://www.bp.com/statisticalreview

[2] Söderbergh B, Jakobsson K, Aleklett K European energy security: An analysis of future russian natural gas production and exports Energ

Policy 2010; 38: 7827-7843

[3] Bilgin M Geopolitics of european natural gas demand: Supplies from russia, caspian and the middle east Energ Policy 2009; 37: 4482-4492

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[4] Johansen S, Juselius K Maximum likelihood estimation and inference on cointegration with applications to the demand for money Oxford

Bull Econ Statist 1990; 52: 169-210

[5] Phillips PCB, Ouliaris S Asymptotic properties of residual based tests for cointegration Econometrica 1990; 58: 165-193

[6] Perron P Further evidence on breaking trend functions in macroeconomic variables J Econometrics 1997; 80: 355-385

[7] Engle RF, Granger CWJ Cointegration and error correction: Representation, estimation and testing Econometrica 1987; 55: 251-276

[8] Granger CWJ Investigating causal relations by econometric models and cross-spectral methods Econometrica 1969; 37: 424-438

[9] Newey WK, West KD A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix Econometrica

1987; 55: 703-708

[10] Ng S, Perron P Lag length selection and the construction of unit root tests with good size and power Econometrica 2001; 69: 1519-1554

[11] MacKinnon JG Numerical distribution functions for unit root and cointegration tests J Appl Econometrics 1996; 11: 601-618

[12] Hendry DF, Juselius K Explaining cointegration analysis: Part i Energy J 2000; 21: 1-42

[13] Hendry DF, Juselius K Explaining cointegration analysis: Part ii Energy J 2001; 22: 75-120

[14] Osterwald-Lenum M A note with quantiles of the asymptotic distribution of the maximum likelihood cointegration rank test statistics

Oxford Bull Econ Statist 1992; 54: 461-472

[15] MacKinnon JG, Haug AA, Michelis L Numerical distribution functions of likelihood ratio tests for cointegration J Appl Econometrics

1999; 14: 563-577

[16] Cheung Y-W, Lai KS Finite-sample sizes of johansen's likelihood ratio tests for cointegration Oxford Bull Econ Statist 1993; 55: 313-328

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