Home Modelling Macroeconomic Shocks in the GCC: Is Monetary Unification Viable?
Article
Licensed
Unlicensed Requires Authentication

Modelling Macroeconomic Shocks in the GCC: Is Monetary Unification Viable?

  • Samir Al-Busaidi EMAIL logo
Published/Copyright: April 23, 2013

Abstract

This article investigates the viability of a common currency for the Gulf Cooperation Council (GCC) countries. A structural vector autoregressive modelling framework is employed to model and compare economically meaningful shocks affecting the GCC members from 1980 to 2004. The results show that output responses to oil price and monetary policy shocks are heterogeneous for the GCC members. Suggested policy implications are that the GCC economies are unlikely to require similar monetary policy adjustments, and the costs of monetary unification will increase as Bahrain and Oman’s oil resources are depleted.

JEL Classification: F15; F33

Appendix A: data description

VariableDefinitionUnitsMain sourceData availabilityData transformationsNotes
AnnualQuarterly
RGDPReal Gross Domestic ProductNational currency (billions)WEO1980–2004NA(see oil production below)UAE figures from 1999 onwards are from WDI and online central bank statistics. Nominal GDP figures are from IFS, supplemented by national statistical bulletins
M2Money SupplyNational currency (millions)IFS1975–20041973–2004Real M2 = (nominal m2/fx) /(cpi/100)
Exchange RateThe nominal exchange rate is the local rate per US dollar at the end of the period. For Kuwait and Bahrain the exchange rate is reported as dollar/local rate, so the reciprocal of the quoted rate is used for these two countries.Local currency per US dollar at end of periodIFS1970–20051973–2005
Foreign ReservesForeign reserves is the summation of the Foreign Exchange, Reserve Position in the IMF Fund, the U.S. dollar value of SDR holdings and the gold holding valued in US dollarsUS dollars (millions)IFS1973–20041973–2004
CPIConsumer Price indexIndex 2000 base yearIFS &WEO1980–20051973–2004 for Bahrain, Kuwait and Saudi, 1990–2004 for OmanUnavailable quarterly IMF data are interpolated using money supply fluctuations as done for GDP. Gaps in quarterly IFS data are complemented using annual WEO numbers and then interpolated, which includes all data for Qatar and the UAE and for Oman (1980–1990)
Interest RatesThe real interest rate used is the 3-month deposit rate, for the US the 3-month certificate of deposit rate is used%IFS &WDI1980–2004NAReal interest rate=nominal rate − CPI All quarterly series are interpolated from annual local rates by using quarterly US rates fluctuationsLarge gaps in SA and UAE are completed with US rates. All Qatar rates are prior to 1992 are fixed and do not represent market rates; therefore, US rates are used for (1980–1992) and where unavailable (2000–2002). Many of the post 2000 rates were obtained from monetary authority bulletins
Oil ProductionOil production indexIndex 2000 base yearIFS1973–20051973–2005Quarterly RGDP is interpolated using these quarterly valuesAll countries use crude petroleum except Bahrain that uses refined petroleum
Real world exportsUS dollars (billions)IFS1970–20051970–2005Where the export price index is provided by the IFS (2000 base year)
Oil PriceArab light crude oil pricesUS dollars per BBLOECD1960–20051985–2005Complemented by Brent oil prices where unavailable in yearly figures and by West Texas intermediate in quarterly data
US CPIIndex 2000 base yearIFS1970–20051970–2005
Trade weighted price index constructed from the CPIs of main import countries for each GCC country iIndex 2000 base yearWEO1980–2005NAAll quarterly series are interpolated using cubic spline method

Appendix B: unit root tests

The two unit root tests employed in this section are the augmented Dickey Fuller test (Dickey and Fuller 1979, 1981) and Phillips-Perron test Phillips and Perron (1988), which are referred to as ADF and PP tests, respectively hereafter.

Table B.1:

ADF test of variables in levels.

BahrainKuwaitOmanQatarSaudiUAE
Case 17.250.234.091.840.811.58
(1)(0.74)(1)(0.98)(0.88)(0.97)
Case 20.86−0.30−0.401.320.150.99
(0.99)(0.91)(0.89)(0.99)(0.96)(0.99)
Case 3−1.59−2.87−3.58**−1.15−2.53−1.58
(0.76)(0.19)(0.05)(0.90)(0.31)(0.77)
Case 1−1.70*−0.64−1.26−1.811.94*−1.71
(0.08)(0.43)(0.19)(0.07)*(0.05)(0.08)*
Case 2−0.99−1.52−0.45−1.71−1.14−1.16
(0.74)(0.51)(0.88)(0.41)(0.68)(0.67)
Case 3−2.414.21**−1.55−2.56−2.32−2.43
(0.36)(0.02)(0.78)(0.30)*(0.41)(0.36)*
Case 14.063.324.754.862.061.40
(1)(0.99)(1)(1)*(0.99)(0.95)*
Case 2−0.581.55−0.44−1.03−1.740.27
(0.86)(1)(0.88)(0.72)(0.40)(0.97)*
Case 3−0.775.555.40−3.484.98−0.88
(0.86)(0.00)**(0.00)**(0.06)*(0.00)**(0.94)
Case 11.50(0.96)1.68(0.97)0.44(0.80)2.88(0.99)0.16(0.72)2.970.99
(0.96)(0.97)(0.80)(0.99)(0.72)(0.99)
Case 2−0.73−1.38−0.91−1.38−1.46−0.63
(0.82)(0.57)(0.77)(0.57)(0.54)(0.85)
Case 35.18−0.67−1.283.87−1.26−2.11
(0.00)**(0.96)(0.87)(0.03)**(0.87)(0.51)
Case 10.820.411.780.840.082.78
(0.88)(0.79)(0.98)(0.88)(0.69)(0.99)
Case 2−2.86−1.63−0.961.43−1.160.65
(0.06)*(0.45)(0.75)(0.68)(0.99)
Case 3−0.88−1.95−3.37−0.39−0.133.82
(0.94)(0.60)(0.08)*(0.98)(0.99)(0.03)**
Case 13.45−1.462.171.160.80−0.89
(0.00)**(0.13)(0.03)**(0.93)(0.87)(0.31)
Case 2−2.43−1.64−1.79−2.31−1.88−0.26
(0.15)(0.45)(0.38)(0.18)(0.33)(0.92)
Case 3−2.415.03−2.04−1.26−1.95−2.43
(0.37)(0.00)**(0.54)(0.87)(0.59)(0.36)

Notes: Table B.1 summarises the test statistics of the ADF tests where all variables are in logarithm form (except interest rates) and the values in parenthesis indicate p values. Case 1 indicates no deterministic terms, case 2 includes a constant and case 3 includes a constant and trend term. * indicates significance at 10% level, ** indicates significant at 5% level. Modified Akaike information criterion (MAIC) is used in lag selection. Bold values indicate rejection of a unit root at 5% level in both ADF and PP tests.

The results of the ADF tests on , , , , and in levels and first differences are shown in Tables B.1 and B.2, respectively.31 The results of both tests are incorporated in the tables by using bold values to indicate where the results of the ADF and PP tests coincide at the 5% level. For each variable, the unit root test is conducted with and without deterministic variables. In the ADF tests, the appropriate lag length is selected based on the modified Akaike information criterion (MAIC). In the PP tests, the adjustment for serial correlation is based on a Bartlett kernel function with the Newey and West (1994) procedure for bandwidth selection.

Overall, the results of the ADF and PP tests in Table B.1 are similar and indicate that most of the variables are non-stationary in levels. The exception is the interest rate in Saudi Arabia where the null is rejected. The results also indicate that M2 in Kuwait, Oman and Saudi Arabia, interest rates in Kuwait and the CPIs in Bahrain and Qatar are trend stationary.

The same tests are also conducted on the variables when differenced and the results of these tests are summarised in Table B.2. For the differenced variables, the results of the ADF and PP tests are quite different; the ADF tests reject the null of a unit root far fewer times than the PP tests. If the ADF test results are correct, this would indicate that many of the variables are I(2) and would raise the difficulty of dealing with a mix of I(1) and I(2) variables. However, it has been documented that when comparing the PP and ADF tests, “for low frequency data, the PP test appears to be more powerful than the ADF test” (Maddala and Kim 1998, 130). Therefore, based on the PP test results, there is a strong evidence to reject the presence of I(2) variables.

Table B.2:

ADF test of variables in first differences.

BahrainKuwaitOmanQatarSaudiUAE
Case 1−0.88−4.53−2.59−0.31−1.76−0.64
(0.32)(0.00)**(0.01)**(0.56)(0.07)*(0.43)
Case 2−2.52−4.49−3.36−1.17−1.92−2.75
(0.12)(0.00)**(0.02)**(0.66)(0.32)(0.08)*
Case 1−3.82−6.89−3.03−4.95−4.33−3.62
(0.00)**(0.00)**(0.00)**(0.00)**(0.00)**(0.00)**
Case 2−4.01−7.17−3.13−5.40−4.82−3.82
(0.01)**(0.00)**(0.04)**(0.00)**(0.00)**(0.00)**
Case 1−0.57−1.94−2.79−0.60−2.09−0.27
(0.46)(0.05)*(0.01)**(0.45)(0.04)**(0.58)
Case 2−1.04−2.85−4.12−4.07−0.05−1.34
(0.72)(0.07)*(0.00)**(0.01)**(0.94)(0.60)
Case 1−4.93−1.13−1.51−1.96−2.88−1.62
(0.00)**(0.22)(0.12)(0.05)*(0.00)**(0.09)*
Case 2−4.79−2.98−1.84−3.66−2.80−3.63
(0.00)**(0.05)*(0.35)(0.01)**(0.07)*(0.01)**
Case 1−6.33−3.55−0.930.32−1.65−0.12
(0.00)**(0.00)**(0.30)(0.77)(0.09)*(0.63)
Case 2−6.20−5.33−6.08−0.34−1.58−4.06
(0.00)**(0.00)**(0.00)**(0.90)(0.47)(0.01)**
Case 1−1.29−6.051.61−3.07−2.22−3.26
(0.17)(0.00)**(0.09)*(0.00)**(0.02)**(0.00)**
Case 2−3.12−6.322.43−3.01−1.82−3.31
(0.04)**(0.00)**(0.14)(0.04)**(0.36)(0.03)**

Notes: See Table B.1. Bold values indicate the non-rejection of a unit root at the 5% level.

Appendix C: Long-run ARDL estimates

Table C.1:

ARDL regression results for the IS relationships.

ΔytBahrainKuwaitOmanQatarSaudiUAE
CoefSECoefSECoefSECoefSECoefSECoefSE
c−0.620.480.030.480.410.28−0.160.390.850.20−3.291.48
−0.560.13−0.610.12−0.120.05−0.340.09−0.470.11−0.170.10
0.280.100.190.12−2.490.36−0.220.151.460.63
−0.020.010.000.00−0.020.00−0.010.00−0.030.01
0.090.080.030.030.170.04−0.060.030.090.03
0.240.070.230.050.440.080.240.080.210.12
1.010.53−0.980.26−0.910.364.750.80
−0.010.000.010.00−0.010.01
0.060.02−0.100.03−0.050.02
0.700.491.640.191.210.15
0.150.110.770.19−0.440.110.240.14
1.110.28
0.010.000.010.00
−0.230.090.080.02−0.250.040.100.03−0.200.03
1.160.25−0.370.160.540.17
−0.770.19
0.000.00
−0.040.02
−0.170.090.040.02
−0.050.02−0.500.09−0.050.030.030.020.070.03
0.590.820.940.920.890.94
0.020.080.010.030.020.02

Notes: () is the adjusted squared multiple correlation coefficient, is the standard error of the regression. Excluded regressors indicated by “–” in the table)

Bahrain

Kuwait

Oman

Qatar

Saudi

UAE

Table C.2:

ARDL regression results for the LM relationships.

Δ(m−p)tBahrainKuwaitOmanQatarSaudiUAE
CoefSECoefSECoefSECoefSECoefSECoefSE
c−1.060.514.832.292.680.487.811.39−8.931.670.010.70
0.130.06−0.450.22−0.470.09−1.020.18−0.100.08−0.080.10
0.730.120.560.110.390.120.210.09
−0.410.170.080.070.180.10
0.020.01−0.030.01−0.050.01
0.330.110.300.13
−0.010.01−0.020.01−0.010.01-
0.100.07
−0.620.160.270.16
−0.890.18−0.170.14−0.190.130.240.19
0.260.10−0.050.04
−0.030.01-0.030.01
0.360.150.420.16
−0.520.17
−0.010.01
0.160.07
0.160.150.660.150.270.15
−0.900.27−0.900.13−0.320.12
−0.290.140.160.16
−0.260.18
−0.240.05−0.180.08
0.160.02
−0.430.04
6.101.39
0.640.590.870.670.860.75
0.040.030.030.040.020.03

Notes: See Table C.1.

Bahrain

Kuwait

Oman

Qatar

Saudi

UAE

Table C.3:

ARDL regression results for the PPP relationships.

ΔptBahrainKuwaitOmanQatarSaudiUAE
CoefSECoefSECoefSECoefSECoefSECoefSE
c2.440.500.000.31−0.300.330.310.163.901.270.430.10
−0.680.14−0.470.220.230.15−0.380.17−0.630.18−0.680.16
0.160.040.410.18−0.190.080.320.150.100.030.590.14
−0.250.14
1.830.53−0.950.72
0.910.190.410.13−0.430.170.460.240.590.210.660.17
−1.040.25−0.810.57
0.460.13
0.250.14−0.230.15−0.350.110.570.230.360.19
0.920.28−0.380.33−0.700.20
0.350.10
−0.300.13
−0.640.25−0.700.55
−0.020.010.020.010.050.01−0.020.01
0.040.010.040.020.040.01
0.100.02
−1.080.45
0.800.900.900.150.590.62
0.010.010.010.020.010.01

Notes: See Table C.1.

Bahrain

Kuwait32

Oman

Qatar

Saudi

UAE

Table C.4

ARDL regression results for the IRP relationships

ΔRi,tBahrainKuwaitOmanQatarSaudiUAE
CoefSECoefSECoefSECoefSECoefSECoefSE
c1.020.50−7.365.584.170.952.300.970.300.19NANA
−0.860.28−0.370.10−1.210.23−1.250.35−0.510.25NANA
0.580.190.150.060.730.150.970.330.480.24NANA
−7.074.64
0.760.090.260.090.630.100.950.230.990.04NANA
12.703.71
0.230.120.350.180.780.19NANA
−0.280.14NANA
13.494.14
0.300.16−0.360.22NANA
0.130.080.350.22NANA
0.310.180.730.19NANA
−0.180.12−0.250.10NANA
11.444.28
0.550.54NANA
0.840.730.780.630.98NANA
0.480.430.540.910.22NANA

Notes: See Table C.1.

Bahrain

Kuwait

Oman

Qatar33

Saudi34

Appendix D: impulse responses of GCC prices to a US interest rate shock

References

Abu-Bader, S., and A. S. Abu-Qarn. (2008). “On the Optimality of a GCC Monetary Union: Structural VAR, Common Trends, and Common Cycles Evidence.” The World Economy 31 (5):612–630.10.1111/j.1467-9701.2008.01096.xSearch in Google Scholar

Al-Raisi, A. H., S. Pattanaik, and A. Y. Al-Raisi. (2007). “Transmission Mechanism of Monetary Policy under the Fixed Exchange Rate Regime of Oman Central Bank.” Central Bank of Oman Occasional Paper, 1.Search in Google Scholar

Alhajji, A. F., and D. Huettner. (2000). “OPEC and World Crude Oil Markets from 1973 to 1994: Cartel, Oligopoly, or Competitive?” Energy Journal 21 (3):31–60.10.5547/ISSN0195-6574-EJ-Vol21-No3-2Search in Google Scholar

Bacha, O. I. (2008). “A Common Currency Area for MENA Countries? A VAR Analysis of Viability.” International Journal of Emerging Markets 3 (2):197–215.10.1108/17468800810862641Search in Google Scholar

Benkwitz, A., H. Lutkepohl, and J. Wolters. (2001). “Comparison of Bootstrap Confidence Intervals for Impulse Responses of German Monetary Systems.” Macroeconomic Dynamics 5 (1):81–100.10.1017/S1365100501018041Search in Google Scholar

Berument, H., and N. B. Ceylan. (2008). “US Monetary Policy Surprises and Foreign Interest Rates: Evidence from a Set of MENA Countries.” Review of Middle East Economics and Finance 4 (2):117–133.10.2202/1475-3693.1065Search in Google Scholar

Berument, H., N. B. Ceylan, and N. Dogan. (2008). “The Impact of Oil Price Shocks on the Economic Growth of Selected MENA Countries.” Bilkent University <mimeo>.Search in Google Scholar

Dar, H. A., and J. R. Presley. (2001). “The Gulf Co-Operation Council: A Slow Path to Integration?” World Economy 24 (9):1161–78.10.1111/1467-9701.00405Search in Google Scholar

Darrat, A. F., and F. S. Al-Shamsi. (2005). “On the Path of Integration in the Gulf Region: Are the Gulf Economies Sufficiently Compatible?” Applied Economics 37:1055–62.10.1080/00036840500109027Search in Google Scholar

De Santis, R. A. (2003). “Crude Oil Price Fluctuations and Saudi Arabia’s Behaviour.” Energy Economics 25 (2):155–73.10.1016/S0140-9883(02)00106-8Search in Google Scholar

Diboolu, S., and E. Aleisa. (2004). “Oil Prices, Terms of Trade Shocks, and Macroeconomic Fluctuations in Saudi Arabia.” Contemporary Economic Policy 22 (1):50–62.10.1093/cep/byh005Search in Google Scholar

Dickey, D. A., and W. A. Fuller. (1979). “Distribution of the Estimators for Autoregressive Time Series with a Unit Root.” Journal of the American Statistical Association 74 (366):427–31.10.2307/2286348Search in Google Scholar

Dickey, D. A., and W. A. Fuller. (1981). “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root.” Econometrica 49 (4):1057–72.10.2307/1912517Search in Google Scholar

Eltony, M. N., and M. Al-Awadi. (2001). “Oil Price Fluctuations and Their Impact on the Macroeconomic Variables of Kuwait: A Case Study Using a VAR Model.” International Journal of Energy Research 25 (11):939–59.10.1002/er.731Search in Google Scholar

Fasano, U., A. Schaechter, R. Bhattacharya, I. A. Gelaiqah, B. Guerami, B., Hussein, S., et al. (2003). “Monetary Union among Member Countries of the Gulf Cooperation Council.” Occasional Paper, 223.10.5089/9781589062191.084Search in Google Scholar

Federal Reserve Bank of San Francisco. (2004). “U.S. Monetary Policy: An Introduction.” Retrieved from http://www.frbsf.org/publications/federalreserve/monetary/MonetaryPolicy.pdf.Search in Google Scholar

Fielding, D., K. Lee, and K. Shields. (2011). “Does One Size Fit All? Modelling Macroeconomic Linkages in the West African Economic and Monetary Union.” Economic Change and Restructuring (Special Issue in honour of Wojciech Charemza, forthcoming).Search in Google Scholar

Garratt, A., K. Lee, M. H. Pesaran, and Y. Shin. (2003). “A Long Run Structural Macroeconometric Model of the UK.” The Economic Journal, 113 (487), 412–455.10.1111/1468-0297.00131Search in Google Scholar

Garratt, A., K. Lee, M. H. Pesaran, and Y. Shin. (2006). Global and National Macroeconometric Modelling: A Long-Run Structural Approach. Oxford: Oxford University Press.10.1093/0199296855.001.0001Search in Google Scholar

Hasan, M., and H. Alogeel. (2008). “Understanding the Inflationary Process in the GCC Region: The Case of Saudi Arabia and Kuwait.” IMF Working Paper, 193.10.2139/ssrn.1266526Search in Google Scholar

Hassanain, K. (2004). “Purchasing Power Parity: Further Evidence and Implications.” Review of Middle East Economics and Finance 2 (1):63–77.10.1080/14753680410001685687Search in Google Scholar

Husain, A. M., K. Tazhibayeva, and A. Ter-Martirosyan. (2008). “Fiscal Policy and Economic Cycles in Oil-Exporting Countries.” IMF Working Paper, 253.Search in Google Scholar

Jadresic, E. (2002). “On a Common Currency for the GCC Countries.” IMF Policy Discussion Paper, 02/12.10.5089/9781451969481.003Search in Google Scholar

Johansen, S., and K. Juselius. (1990). “Maximum Likelihood Estimation and Inference on Cointegration – with Applications to the Demand for Money.” Oxford Bulletin of Economics & Statistics 52 (2):169–210.10.1111/j.1468-0084.1990.mp52002003.xSearch in Google Scholar

Johansen, S., and K. Juselius. (1992). “Testing Structural Hypotheses in a Multivariate Cointegration Analysis of the PPP and the UIP for UK.” Journal of Econometrics 53 (1–3):211–44.Search in Google Scholar

Kandil, M. (1991). “Structural Differences between Developing and Developed Countries: Some Evidence and Implications.” Economic Notes 20 (2):254–78.Search in Google Scholar

Kenen, P. B. (1969). “The Theory of Optimum Currency Areas: An Eclectic View.” In Monetary Problems of the International Economy, edited by R. A. Mundell and A. K. Swoboda, 41–60. Chicago: University of Chicago Press.Search in Google Scholar

Louis, R. J., F. Balli, and M. Osman. (2008). “Monetary Union among Arab Gulf Cooperation Council (AGCC) Countries: Does the Symmetry of Shocks Extend to the Non-Oil Sector?” <mimeo>.Search in Google Scholar

Maddala, G. S., and I. Kim. (1998). Unit Roots, Cointegration, and Structural Change. Cambridge: Cambridge University Press.Search in Google Scholar

McKinnon, R. I. (1963). “Optimum Currency Areas.” The American Economic Review 53 (4):717–25.Search in Google Scholar

Mundell, R. A. (1961). “A Theory of Optimum Currency Areas.” The American Economic Review 51 (4):657–65.Search in Google Scholar

Narayan, P. K., and B. C. Prasad. (2005). “The Validity of Purchasing Power Parity Hypothesis for Eleven Middle Eastern Countries.” Review of Middle East Economics and Finance 3 (2):135–49.10.1080/14753680500166466Search in Google Scholar

Newey, W. K., and K. D. West. (1994). “Automatic Lag Selection in Covariance Matrix Estimation.” The Review of Economic Studies 61 (4):631–53.10.2307/2297912Search in Google Scholar

Pesaran, M. H., and Y. Shin. (1998). “An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis.” In Econometrics and Economic Theory in the 20th Century, edited by S. Strøm. Cambridge: Cambridge University Press, 371–413.10.1017/CCOL0521633230.011Search in Google Scholar

Pesaran, M. H., Y. Shin, and R. J. Smith. (2001). “Bounds Testing Approaches to the Analysis of Level Relationships.” Journal of Applied Econometrics 16 (3):289–326.10.1002/jae.616Search in Google Scholar

Phillips, P. C. B., and P. Perron. (1988). “Testing for a Unit Root in Time Series Regression.” Biometrika 75 (2):335–46.10.1093/biomet/75.2.335Search in Google Scholar

Pock, A. V., H. Schulte-Croonenberg, and D. Buchta. (2007). Bank Consolidation in the GCC: Myth or Mania? Dubai: A.T. Kearney. Retrieved from www.atkearney.com.Search in Google Scholar

Rutledge, E. (2009). Monetary Union in the Gulf: Prospects for a Single Currency in the Arabian Peninsula. New York: Routledge.Search in Google Scholar

Sarno, L., M. P. Taylor, and J. A. Frankel. (2002). The Economics of Exchange Rates. Cambridge: Cambridge University Press.Search in Google Scholar

Sriram, S. S. (2001). “A Survey of Recent Empirical Money Demand Studies.” IMF Staff Papers 47 (3):334–365.Search in Google Scholar

Sturm, M., and N. Siegfried. (2005). “Regional Monetary Integration in the Member States of the Gulf Cooperation Council.” ECB Occasional Paper, 31.10.2139/ssrn.752091Search in Google Scholar

  1. 1

    Rutledge (2009), for example, argues that because all of the GCC countries are dependent on oil their commonly undiversified production structures make them suitable for currency unification.

  2. 2

    On the basis of Chow’s break point test and Chow’s predictive failure test, there is a strong evidence of a break in the GDP growth series for all except two of the economies (Kuwait and Saudi Arabia) in 2004. Therefore, since the economies are being modelled as a system, the sample period is truncated at 2004.

  3. 3

    RGDP is used instead of non-oil RGDP because oil represents the largest source of economic activity in both the private and public sectors of the region, this is in line with most empirical papers analysing the GCC such as Darrat and Al-Shamsi (2005), Bacha (2008) and Abu-Bader and Abu-Qarn (2008).

  4. 4

    The world trade index is used as a proxy for foreign income, see data appendix.

  5. 5

    A third of the region’s GDP directly stems from oil and gas exports and “oil income contributes around 80% to government revenues” Sturm and Siegfried (2005, 17).

  6. 6

    In Islam “riba” is prohibited – “riba” can be translated as usury or an exorbitant high amount of rate of interest. As a result, there is a segment of the population that do not participate in the conventional banking system because it is based on interest receipts and payments. The muted response to interest rates in the GCC adds to the difficulty of implementing open market operations and explains why more direct tools are used to conduct monetary policy. Further discussions on the conduct of monetary policy and the tools used in the GCC are provided by Fasano et al. (2003) and Al-Raisi, Pattanaik, and Al-Raisi (2007).

  7. 7

    Further details on the endogeneity of oil price for the Saudi Arabian economy are discussed in Section 4.3.

  8. 8

    The absence of some quarterly data series motivates a two-stage estimation procedure which makes use of the information in both the annual and quarterly series; this procedure is elaborated in Section 4.2.

  9. 9

    Clearly, it is possible that the empirical results might be sensitive to the method of interpolation chosen. This particular interpolation approach was selected on the basis of consideration of economic criteria over statistical criteria on deriving some of the quarterly series.

  10. 10

    Two missing values for Oman’s oil index in 1993 and four missing values for Kuwait during the Gulf War in 1990 and 1991 are linearly interpolated.

  11. 11

    Three missing values for the money supply for Saudi Arabia in 1983, UAE in 1986 and Kuwait during the war are linearly interpolated.

  12. 12

    The average coefficient of variation of prices is 0.14, which is lower than the coefficient of variation of 0.49 for money in the GCC.

  13. 13

    Since the dynamics of are not central to the questions addressed in this paper, the interpolation of the fluctuations in the series have an insignificant impact on the results.

  14. 14

    The treatment of and as I(1) is in line with other GCC studies that have analysed their unit root properties, such as Hasan and Alogeel (2008), Abu-Bader and Abu-Qarn (2008) and Bacha (2008).

  15. 15

    If quarterly data were available for all of the series then there would be no advantage in using the annual data set and both the long and short-run coefficients could be estimated with quarterly data.

  16. 16

    It is also found that these relationships are compatible with the quarterly data when the long-run coefficients are imposed on the VECM. However, the LM relationship for Bahrain does not hold when incorporated in the VECM with quarterly data and is therefore not included in the final estimation.

  17. 17

    With limited observations, long-run variables with a t-stat greater than unity are considered important in the relationships.

  18. 18

    Kandil (1991) estimates the interest rate sensitivity of investment demand.

  19. 19

    Based on the contribution of the mining sector to GDP which includes oil and gas (GCC Statistical bulletin, 2005, http://library.gcc-sg.org).

  20. 20

    The unit root tests reported in the Appendix indicate that real exchange rates were non-stationary, which is an indication that PPP does not hold (Sarno, Taylor, and Frankel 2002). However, the long-run PPP tests conducted in this section are more sophisticated than the unit root tests since the world price coefficient is not restricted to one and dummy variables are included in the ARDL regressions to account for shifts in the mean. Wald tests were conducted on the estimated coefficients to see if they were significantly different to one. For the PPP relationships, the null hypothesis that the world price variable had a unit coefficient was rejected for all countries except Kuwait and Oman at the 5% level. In the IRP relationships, all GCC countries except for Saudi Arabia had US interest rate coefficients that were significantly different from one. These results coincide with the summarized long-run relationships at the bottom Tables C.3 and C.4 in the Appendix.

  21. 21

    This statistic refers to the average traded goods and services as a percentage of GDP from 1990–2002, World Bank (WDI 2007).

  22. 22

    As in the ARDL specification search, estimates with a t-statistic of one or more are retained within the model. The coefficients of the VECM are available on request.

  23. 23

    The re-estimated long-run relationships are based on the same variables and dummies that were used in Section 4.3. Core terms that were deemed insignificant in the ARDL regressions were not included in these simulations.

  24. 24

    Increasing the number of simulations from 1,000 to 2,000 made little difference to the confidence intervals.

  25. 25

    Based on the contribution of the mining sector to GDP (GCC Statistical bulletin, 2005, http://library.gcc-sg.org).

  26. 26

    The authors consider Kuwait, Oman, Saudi Arabia and the UAE as the GCC. Bahrain and Qatar are not included in the study.

  27. 27

    The responses of GCC prices and the money aggregates to an oil price shock were also investigated but the responses were not significant for all GCC countries.

  28. 28

    Due to the lack of data on domestic interest rates in the UAE, the pass-through effects could not be examined.

  29. 29

    However, the speed of the pass-through is quicker in their study.

  30. 30

    The responses of local prices to a US monetary shock are summarized in the Appendix. In many open economies, a hike in US interest rates can be inflationary abroad. In the GCC, such effects are insignificant as seen in the Appendix.

  31. 31

    Since the nominal exchange rate is fixed for these countries, the real exchange rate is tested for a unit root.

  32. 32

    For Kuwait, is included because the exchange rate fluctuates slightly for most of the period. For Saudi, is a contemporaneous dummy to adjust for the depreciation in 1986. Although fluctuates in Saudi from 1980 to 1986, for most of the sample is fixed. D86_shift was not used for Saudi because it fails to dummy out the early movements in . Shift dummy variables were not required in the IS relationships because not was used.

  33. 33

    For Qatar, the sub-sample period 1992–2004 is used because prior to 1992 US interest rates are used to proxy local rates.

  34. 34

    For Saudi Arabia, the exchange rate was insignificant in the IRP relationship.

Published Online: 2013-04-23

©2013 by Walter de Gruyter Berlin / Boston

Downloaded on 1.10.2025 from https://www.degruyterbrill.com/document/doi/10.1515/rmeef-2013-0023/html
Scroll to top button