Back Matter
  • 1 https://isni.org/isni/0000000404811396, International Monetary Fund

Appendix Figure 1.
Appendix Figure 1.

GCC: Impulse Responses with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 2.
Appendix Figure 2.

Bahrain: Impulse Responses with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 3.
Appendix Figure 3.

Kuwait: Impulse Responses with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 4.
Appendix Figure 4.

Oman: Impulse Responses with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 5.
Appendix Figure 5.

Qatar: Impulse Responses with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 6.
Appendix Figure 6.

Saudi Arabia: Impulse Responses with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 7.
Appendix Figure 7.

U.A.E.: Impulse Responses with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 8.
Appendix Figure 8.

GCC: Variance Decomposition with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 9.
Appendix Figure 9.

Bahrain: Variance Decomposition with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 10.
Appendix Figure 10.

Kuwait: Variance Decomposition with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 11.
Appendix Figure 11.

Oman: Variance Decomposition with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 12.
Appendix Figure 12.

Qatar: Variance Decomposition with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 13.
Appendix Figure 13.

Saudi Arabia: Variance Decomposition with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations
Appendix Figure 14.
Appendix Figure 14.

U.A.E.: Variance Decomposition with Bootstrapped Confidence Intervals

Citation: IMF Working Papers 2012, 191; 10.5089/9781475505399.001.A999

1/ A precise description of these confidence intervals can be found in Efron and Tibshirani (1993) and Hall (1992). In order to compute bootstrap confidence intervals, we have set the number of drawings to 1,000.Source: Authors’ calculations

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*

The authors would like to thank Ahmed Al-Darwish, Elif Arbatli, Joshua Charap, Selim Elekdag, Davide Furceri, Felix Hammermann, Prachi Mishra, Kia Penso, Ananthakrishnan Prasad, Mohammad Rahmati, Gabriel Sensenbrenner, Jan Strasky, Michael Sturm, Anke Weber, and participants of the MCD Discussion Forum for their insightful comments and suggestions. Arthur Ribeiro da Silva and Renas Sidahmed provided excellent research assistance.

1

The GCC is comprised of six countries along the Arabian Gulf—Kuwait, Bahrain, Oman, Qatar, Saudi Arabia, and the United Arab Emirates—that have pegged their exchange rates to the U.S. dollar for more than three decades, except Kuwait with a peg to an undisclosed basket of currencies.

2

Although the GCC countries remain committed to monetary integration, Oman and the United Arab Emirates have opted out at this stage and the launch of the single currency has been postponed for an unspecified period.

3

Sims (2012) provides a concise assessment of the evolution of statistical modeling of how monetary policy affects economic activity.

4

In Denmark, the relative importance of traditional transmission mechanisms, such as the credit channel, declined before the introduction of the euro peg, while the bank lending channel in Hong-Kong has had a more significant impact on economic activity, particularly through the housing market.

5

Bank consolidation in the case of Latin American and Asian countries, or securitization in the case of European countries, can weaken the credit channel of monetary transmission by reducing the sensitivity to monetary policy shocks (Altunbas, Gambacorta, and Marqués-Ibáñez, 2007; Olivero, Li, and Jeon, 2011).

6

The concept of long-run monetary neutrality argues that an increase in money supply affects only nominal variables such as the price level in the short run and would be offset by an equal rise of prices and wages. However, as Blanchard (1990) argues, long-run neutrality of money is rather a matter of faith, based more on theoretical considerations than on empirical evidence. For example, supply-side effects (e.g., more flexible labor markets) may imply that negative supply shocks are absorbed with a smaller increase in inflationary pressures, given the limited extent of second-round effects.

7

Under a flexible exchange rate regime, currency depreciation is likely to lead to an increase in import prices, albeit imported inflation could be less of a problem than it is in countries with pegged exchange rate regimes. For example, inflationary pressures increased in the GCC countries, before the recent global financial crisis, as import prices surged with higher food prices and the declining value of the U. S. dollar.

8

Non-hydrocarbon GDP is the more relevant measure of credit expansion because the hydrocarbon sector has required a negligible amount of domestic financing in recent years of high oil prices.

9

The impact of rising consumer price inflation on the real effective exchange rate of Gulf currencies during this period was largely offset by the depreciation of the nominal effective exchange rate of the U.S. dollar, which resulted in a depreciation of the GCC currencies against their trading partners’ currencies, mainly in Asia.

10

To offset the fallout from the global financial crisis, the GCC governments maintained—or even increased—spending levels, despite a sharp fall in hydrocarbon revenues, and also introduced exceptional financial measures to support domestic banks. Tracking the monetary easing cycle in the United States, the GCC central banks lowered interest rates, and eased liquidity through direct injections into the money market and through statutory changes, including reductions in reserve requirements and relaxation of prudential loan-to-deposit ratios.

11

The issue of monetary policy transmission under Islamic banking is beyond the scope of this paper, but this fast-growing segment of banking sectors may behave differently, compared to conventional banks, in transmitting monetary policy shocks.

12

Data for deposit and lending rates are not available for Saudi Arabia and the United Arab Emirates.

13

Prasad and Khamis (2011) provide a comprehensive description of macro-prudential measures used by the GCC central banks.

14

We test whether the volatility of real non-hydrocarbon output, consumer price inflation, short-term interest rates and real effective exchange rates is due to smaller/larger disturbances (the size of shock) and more/less frequent disturbances (the propagation of shock) to productivity, external demand, and changes in fiscal and monetary policy variables.

15

Christiano, Eichenbaum, and Evans (1999) provide a detailed survey on the use of SVAR models in estimating the effectiveness of monetary policy transmission.

16

For example, Blanchard and Quah (1989) estimate the output response during specific business cycle episodes to identify aggregate demand shocks, but do not identify the output response to foreign demand shocks.

17

Bootstrapping is a method for estimating confidence intervals when series differ from the asymptotic normal distribution; and it requires no knowledge of the actual distribution (e.g., it does not have to be normal).

18

It is worth noting that the recursive modeling with the Cholesky decomposition assumes a triangular matrix, while the structural identification of the matrix assumes any structure as long as there are enough restrictions.

19

By testing for weak exogeniety, we establish that these variables are weakly exogenous—i.e. Yt does not Granger cause Xt.

20

The price is crude oil is included as an exogenous variable, assuming that international prices are formed according to market conditions and that the GCC countries do not attempt to influence the pricing process.

21

ω21 is the impact of supply shock on consumer prices, ω31 is the impact of supply shock on bank lending, ω32 is the impact of demand shock on bank lending, and ω34 is the impact of interest rate shock on bank lending.

22

Three-month market interest rates can theoretically be viewed as part of monetary transmission in developed markets, but we judge that credibility and expectations are less of an issue in the GCC countries and, lacking time-series data on the policy rate, short-term interest rates are the best proxy for the monetary policy variable.

23

Given that interpolation may distort the results, we also utilize another interpolation method by fitting local quadratic polynomial. In addition, the stability of the coefficients is checked by applying the SVAR methodology to the interpolated annual GCC dataset, other than the synthetic aggregation.

24

The unit root tests are based on specifications with a constant term included, though alternative specifications that include both a constant and a deterministic trend also produce similar results.

25

We study the impact of ω21 (supply shock on prices) in addition to the monetary policy shocks (ω32 is the impact of demand shock on bank credit, and ω34 is the impact of interest rate shock on bank credit).

26

Given that the p-values are significantly higher than 5 percent, the null hypothesis of stability—that is, the benchmark model has no structural break—cannot be rejected.

Lost in Transmission? The Effectiveness of Monetary Policy Transmission Channels in the GCC Countries
Author: Mr. Serhan Cevik and Ms. Katerina Teksoz
  • View in gallery

    GCC: Impulse Responses with Bootstrapped Confidence Intervals

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    Bahrain: Impulse Responses with Bootstrapped Confidence Intervals

  • View in gallery

    Kuwait: Impulse Responses with Bootstrapped Confidence Intervals

  • View in gallery

    Oman: Impulse Responses with Bootstrapped Confidence Intervals

  • View in gallery

    Qatar: Impulse Responses with Bootstrapped Confidence Intervals

  • View in gallery

    Saudi Arabia: Impulse Responses with Bootstrapped Confidence Intervals

  • View in gallery

    U.A.E.: Impulse Responses with Bootstrapped Confidence Intervals

  • View in gallery

    GCC: Variance Decomposition with Bootstrapped Confidence Intervals

  • View in gallery

    Bahrain: Variance Decomposition with Bootstrapped Confidence Intervals

  • View in gallery

    Kuwait: Variance Decomposition with Bootstrapped Confidence Intervals

  • View in gallery

    Oman: Variance Decomposition with Bootstrapped Confidence Intervals

  • View in gallery

    Qatar: Variance Decomposition with Bootstrapped Confidence Intervals

  • View in gallery

    Saudi Arabia: Variance Decomposition with Bootstrapped Confidence Intervals

  • View in gallery

    U.A.E.: Variance Decomposition with Bootstrapped Confidence Intervals