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Oil price uncertainty and real output growth: new evidence from selected oil-importing countries in the Middle East

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Abstract

This paper provides an empirical evidence on the influence of oil price uncertainty on the real economic activity in Jordan and Turkey during the period 1986:01–2014:12. To measure the effect of uncertainty, the paper combines a bivariate structural VAR with a GARCH-in-mean process that allows oil volatility to affect the growth of industrial production. Our results indicate that oil market uncertainty has a negative influence on the industrial output of Jordan and Turkey. For instance, the increase in one standard error of oil price uncertainty is found to be associated with a decline of 0.81 and 1.01% in the industrial production of Jordan and Turkey, respectively. Moreover, consistent with the recent empirical evidence, we find that output growth increases/decreases after a negative/positive oil price shock. These results imply that sound energy policies that mitigate the effect of oil market uncertainty may help in stabilizing output in both countries.

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Notes

  1. The theoretical foundations of the theories of investment under uncertainty and real options at the firm level are developed by Henry (1974), Bernanke (1983), Brennan and Schwartz (1985), Majd and Pindyck (1987), Brennan (1990), Gibson and Schwartz (1990), Triantis and Hodder (1990), and Aguerrevere (2009).

  2. It is expected for economic activity in large countries to influence the oil market. Therefore, oil is endogenous and this should be reflected by the model that studies output–oil relationship.

  3. The same model is used to draw inference on the relationship between oil price uncertainty and the level of economic activity in Canada by Rahman and Serletis (2012) and in South Africa by Aye et al. (2014).

  4. Our analysis is restricted only to these two countries because sufficient data on industrial production index are unavailable for most of MENA countries.

  5. Examples are the works of Mork (1994), Mory (1993), Hamilton (1996, 2003), Brown and Yücel (2002) and Mehrara (2008).

  6. Recently, Kilian and Vigfusson (2011a) and Kilian (2014) provided a comprehensive theoretical discussion on the rationale behind asymmetric responses of real output to oil price shocks.

  7. Turkey is more sensitive to oil price uncertainty shocks compared to Jordan because of its higher oil intensity in terms of the amount of oil consumption needed to produce a unit of its GDP.

  8. If oil volatility has a negative effect, then the impulse response of output is asymmetric. This point has been raised to us thankfully by one of the referees.

  9. The discussion and the notations contained in this section are largely derived from Elder and Serletis (2010, 2011).

  10. The theoretical literature, however, does not provide any definitive guideline for choosing appropriate level of lag length in a VAR model. However, Hamilton and Herrera (1983, 1996, 2004), Hooker (1996), and Jiménez-Rodríguez and Sánchez (2005) argue that a lag length of 4 quarter (12 months) is sufficient to capture the dynamic impacts of oil price shocks on real activity. Consequently, we use the long lag of 12 as is common with prior empirical literature (i.e., Herrera et al. 2011, 2015; Kilian and Vigfusson 2011a, b), although our results are robust to alternatives lag length.

  11. Based on the Schwartz Information Criterion, we choose a lag length of \(q=r=1\) in Eq. (2).

  12. For more details of this approach (see Elder 2003, 2004).

  13. Note that the impulse response functions in this model are able to capture potentially asymmetric responses to oil price shocks (Elder and Serletis 2009, 2010).

  14. Authors who used the industrial production include: Bernanke et al. (1997), Lee and Ni (2002), Hamilton and Herrera (2004), Rahman and Serletis (2011), Serletis and Istiak (2013), Baumeister and Peersman (2013), Aye et al. (2014), Donayre and Wilmot (2015), and Herrera et al. (2015) among others.

  15. The sample is selected on the basis of the availability of data of the industrial production index.

  16. In some previous literature, the scale of economic output is measured by real gross domestic production (GDP) on quarterly basis (see, i.e., Rahman and Serletis 2012; Kilian and Vigfusson 2014). However, time series for quarterly GDP data is only available from 1999 and 2003 for Jordan and Turkey, respectively. Therefore, in this paper we use the industrial production index to proxy real GDP.

  17. Very similar results were obtained when using Texas Intermediate crude oil as a benchmark for oil prices.

  18. Over our sample period, we find that the correlation coefficient between WTI and Brent oil prices is 0.9864.

  19. Very similar results were obtained when denominating the oil price in the country-specific consumer price index, which was obtained from the IFS. We also got similar results when the nominal price of oil was used.

  20. We estimated conditional volatility of real oil price using AR(1)–GARCH(1,1) model.

  21. For more detailed information about oil price history, see Kilian (2009) and Hamilton (2013).

  22. We also test the lag length using the AIC, FPE, and HQC information criteria. All suggest that 12 lags are sufficient to summarize the dynamics of the system.

  23. This conclusion is consistent with the findings of Elder and Serletis (2010, 2011) and Aye et al. (2014).

  24. For a robustness check, we re-estimated the model for a sample that excludes the recent global financial crises. Results have not changed. Still the influence of uncertainty is negative and highly significant. The coefficients with the t-statistics were \(-\) 0.03 (\(-\) 3.44) and \(-\) 0.03 (\(-\) 3.69) for Jordan and Turkey, respectively.

  25. Oil intensity is measured as the oil consumption needed to produce one unit of GDP. Energy diversification is defined as diversification of energy sources. Following the empirical literature (e.g., Löschel et al. 2010), we use the Herfindahl–Hirschman (HH) index to measure diversification energy sources. This index is equal to the sum of the squares of the fractional share of each source (standardized in energy units). The more energy diversification of energy sources, the lower is the value of the index. We use six energy sources in calculating the HH index: crude oil, natural gas, coal, hydropower, nuclear power, and other forms of renewable energy (geothermal, solar, wind, wood, and waste electricity generation). The data on share in total energy consumption of each energy source were retrieved from the US Energy Information Administration (http://www.eia.gov/opendata/).

  26. Note that energy diversification is more helpful in mitigating the negative influence on output when prices and price volatilities of different energy sources are weakly correlated.

  27. The budget deficit due to the oil subsidy scheme has been always covered by foreign aid from Saudi Arabia and other oil-rich Arab countries.

  28. Currently, Jordan is adopting the pegged exchange rate regime. The Jordanian Dinar is pegged to the US dollar. Turkey is adopting a floating exchange rate regime.

  29. Note that similar results are obtained when we use 6- and 24-month forecast horizon. These results are only available from the authors upon request.

  30. Herrera et al. (2015) also argue that the standard lag selection criteria such as the Bayesian Information Criterion (BIC) do not work well for nonlinear models and may yield misleading results in small samples.

  31. That is, we restrict the coefficient on oil price uncertainty to zero, which effectively eliminates the multivariate GARCH-in-mean term from Eq. (6).

  32. Note that getting the fitted volatilities from the structural VAR with multivariate GARCH also produce similar results.

  33. Note that given the weak dependence of structural breaks, we expect tiny values in the off-diagonal locations of the volatility matrices of the BEKK and hence similar results. A drawback of this model is that it complicates the interpretation of the structural shocks. This point has been raised to us thankfully by one of the referees.

  34. Equations (9) and (10) are jointly estimated using quasi-maximum likelihood procedure.

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Acknowledgements

The authors would like to thank the editor and two anonymous referees of Empirical Economics for their valuable and helpful comments. The authors are responsible for any remaining errors.

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Authors and Affiliations

  1. Department of Economics and Finance, College of Business and Economics, United Arab Emirates University, P.O. Box 15551, Al-Ain, UAE

    Aktham I. Maghyereh & Osama D. Sweidan

  2. Westminster Business School, University of Westminster, Marylebone Road, London, NW1 5LS, UK

    Basil Awartani

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  1. Aktham I. Maghyereh
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Correspondence to Aktham I. Maghyereh.

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Maghyereh, A.I., Awartani, B. & Sweidan, O.D. Oil price uncertainty and real output growth: new evidence from selected oil-importing countries in the Middle East. Empir Econ 56, 1601–1621 (2019). https://doi.org/10.1007/s00181-017-1402-7

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