نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری اقتصاد نفت و گاز دانشکده اقتصاد، دانشگاه علامه طباطبائی

2 عضو هیأت علمی دانشکده اقتصاد دانشگاه علامه طباطبائی

3 دانشیار گروه اقتصاد نظری، دانشکده اقتصاد، دانشگاه علامه طباطبائی

4 دانشیار گروه اقتصاد انرژی، کشاورزی و محیط‌زیست، دانشکده اقتصاد، دانشگاه علامه طباطبائی

5 عضو هیئت علمی دانشکده اقتصاد، دانشگاه علامه طباطبائی

چکیده

در این پژوهش به تجزیه و تحلیل کمّی و مقایسه‌ای شبکه پویای تجارت بین‌الملل نفت خام ایران با استفاده از روش ارتباط شبکه دایبولد - ایلماز و همچنین تأثیر نامتقارن کوتاه‌مدت و بلندمدت افزایش و کاهش عوامل کلیدی پیشران و موانع گسترش جریان تجارت نفت خام از طریق رابطه جاذبه و با استفاده از الگوی رگرسیون خودبازگشت با وقفه‌های توزیعی تابلویی غیر‌خطی در دوره زمانی (2017-1980) پرداخته شده است. نتایج حاکی از پویایی سرریز تلاطم جریان تجارت نفت خام ایران در طول دوره زمانی مورد بررسی می‌باشد. به علاوه جریان تجارت نفت خام کشور ایران دارای تأثیرگذاری خالص (انتقال‌دهنده شوک) بر کشورهای منطقه خاورمیانه و تأثیرپذیری خالص (انتقال‌گیرنده شوک) به ترتیب از جریان تجارت نفت خام کشورهای مناطق آمریکا، اروپای‌شرقی- اوراسیا، آفریقا، اروپای غربی و آسیا پاسیفیک می‌باشد. بر این اساس تمرکز بر الگوی تجارت مجزای منطقه‌ای و اتخاذ سیاست‏های تبعیضی تجارت خارجی توسط ایران، احتمالاً نمی‌تواند مانع از کاهش تاب‌آوری اقتصاد ایران از تلاطم شبکه تجارت بین‌المللی نفت خام گردد. همچنین نتایج نشان‌دهنده رفتار نامتقارن جریان تجارت دوجانبه نفت خام ایران در برابر افزایش و کاهش متغیرهای تولید ناخالص داخلی کشورهای صادرکننده و واردکننده نفت خام و هزینه حمل و نقل بین‌المللی نفت خام در کوتاه‌مدت و بلندمدت می‌باشد که می‌تواند در شناسایی عوامل مؤثر بر انتقال تلاطم به منظور تنظیم و تعدیل سطح تجارت نفت خام مؤثر باشد. بنابراین به نظر می‌رسد با توجه به درجه بالای ادغام در شبکه تجارت بین‌الملل نفت خام ایران، اولویت رفتار همکاری تجاری بر رفتار رقابتی در تجارت نفت خام ایران و پاسخگویی مناسب به نوسانات و شوک بازار در طول زمان (مدیریت ریسک) در برنامه‌های اقتصادی کشور ضروری باشد.

کلیدواژه‌ها

عنوان مقاله [English]

A Dynamic Network Comparison Analysis of Iran’s Crude Oil International Trade

نویسندگان [English]

  • Masoud Shirazi 1
  • Abdolrasoul Ghasemi 2
  • Teymour Mohamadi 3
  • Ali Faridzad 4
  • Atefeh Taklif 5

1 Corresponding Auther, Ph.D. Candidate in Oil and Gas Economics, Allameh Tabataba'i University

2 Associate Professor, Department of Energy Economics, Faculty of Economics, Allameh Tabataba'i University

3 Associate Professor, Department of Theoretical Economics, Faculty of Economics, Allameh Tabataba'i University

4 Associate Professor of Energy Economics, Allameh Tabataba'i University, Faculty of Economics

5 Associate Professor, Department of Energy Economics, Faculty of Economics, Allameh Tabataba'i University

چکیده [English]

This research conducts a quantitative comparative analysis of the dynamic international crude oil trade network of Iran by using the network connectedness measures of Diebold and Yilmaz (2015) and also the asymmetric short-term and long-term impact of the increasing and decreasing key driving factors and obstacles in the crude oil trade development through the gravityrelation and by using the nonlinear panel auto-regressive distributed lag (ARDL) model during 1980–2017. Results indicated the dynamic spillover flow of the crude oil trade of Iran during the investigated period of time. Moreover, the crude oil trade flow of Iran is a net shock transmitter to Middle East and a net shock receiver from the crude oil trade flow in countries of America, Eastern Europe- Eurasia, Africa, Western Europe, and Asia Pacific, respectively. The focus on the divided regional trade scheme and adopting the biased foreign trade policies by Iran may not lead to the vulnerability reduction of its economy from crude oil trade flow volatilities. Findings also reveal the asymmetric behavior of the crude oil bilateral trade flow in response to the increasing and decreasing of gross domestic product (GDP) per capita variables in both crude oil exporting and importing countries and international crude oil transportation costs in the short-term and long-term period that it can be used in identifying the effective factors on the volatility transmission to adjust the crude oil trade flow. Therefore, concerning the high degree of the integration in the international crude oil trade network of Iran, it seems that it is necessary to prioritize cooperative over competitive behavior in the crude oil trade of Iran and respond appropriately to market shocks and volatilities during the time (risk management) in the economic plan of the country.

کلیدواژه‌ها [English]

  • Crude Oil Trade Behavior
  • Diebold-Yilmaz Dynamic Network Connectedness Measures
  • Gravity Equation
  • Nonlinear Panel Autoregressive Distributed Lag Model
Alam, M.I. & Quazy, R.M. (2003), “Determinant of Capital Flight: an Econometric Case Study of Bangladesh”, Review of Applied Economics, vol. 17, pp. 85-103.
Anderson, J. E. (1979), “A Theoretical Foundation for the Gravity Equation.”, Am. Econ Rev, no.69 (1), pp.106-116.
An, H. Z. and Zhong, W. Q and Chen, Y. R. and Li, H. J. and Gao, X.Y. (2017), “Features and Evolution of International Crude Oil Trade Relationships: A Trading-Based Network Analysis.”, Energy, no.74,pp. 254-259.
An, Q. and Wang, L. and Qu, D. and Zhang, H. (2018), “Dependency Network of International Oil Trade before and after Oil Price Drop.” Energy, no.165, pp.1021-1033.
Babri, S. and Jørnsten, K. and Viertel, M. (2015), “Application of Gravity Models with a Fixed Component in the International Trade Flow of Coal, Iron Ore and Crude Oil”, Marit Econ Logist,pp.1-18.
Banerji, A. and Dolado, J. and Galbraith, J. W.  and Hendry, D. F. (1993), “Cointegration, Error Correction, and the Econometric Analysis of Non-Stationary Data”, Oxford University Press.
Blackburne, E. F. and Frank, M.W. (2007), “Estimation of non- Stationary Heterogeneous Panels”, Stata J., no.7 (2),pp. 197-208.
Bougheas, S. and Demetriades, P. and  Morgenroth, E. (1999), “Infrastructure, Transport Costs and Trade.”, Int. Econ. , no.47 (1), pp.169-189.
Diebold, F. X. and Yilmaz, K. (2009), “Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets.”, Econ. J., no.119, pp.158-171.
Diebold, F. X. and Yilmaz, K. (2015), “Measuring the Dynamics of Global Business Cycle Connectedness.Unobserved Components and Time Series Econometrics: Festschrift in honor of Andrew Harvey's 65th year.”, Oxford University Press.
Diebold, F. X. and Yılmaz, K. (2014), “On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms.” Journal of Econometrics, Causality, Prediction, and Specification Analysis: Recent Advances and Future Directions, no.182, pp.119-134.         http://dx.doi.org/10.1016/j.jeconom.2014.04.012.
Dong, G. and Du, R. and Tian, L. and Wang, Y. and Liu, Y. and Wang, M. and  Fang, G. (2016). “A Complex Network Perspective on Features and Evolution of World Crude Oil Trade.”, Energy Procedia, no.104, pp.221-226.
Econometric Methods and Applications, DOI 10 1007/978-1-4899-8008-3-9, 281-314.
Guan, Q. and An, H. Z. and Hao, X. Q. and Jia, X. L. (2016), “The Impact of Countrie’s Roles on the International Photovoltaic Trade Pattern: the Complex Networks Analysis.”, Sustain-Basel, no.8.
Guan, Q. and An, H. Z. and Gao, X. Y. and Huang, S. P. and Li, H. J. (2016), “Estimating Potential Trade Links in the International Crude Oil Trade: A Link Prediction Approach.”, Energy, no.102, pp.406-415.
Jammazi, R and Lahiani, A. and Nguyen, D. (2015), “A Wavelet-Based Nonlinear ARDL Model for Assessing the Exchange Rate Pass-Through to Crude Oil Prices.”, Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 34(C), pp. 173-187.
Ji, Q. and Zhang, H. Y. and Xi, W. W. and Zhang, Q. (2018), “Exploring the Driving Factors of Global LNG Trade Flow using Gravity Modeling.”, Journal of Cleaner Production, no.172,pp. 508-515.
Kagohashi, K. and Tsurumi, T. and Managi, S. (2015), “The Effects of International Trade on Water Use.”, PLOS ONE, no.10(7): e0132133. http://dx.doi.org/10.1371/journal.
Katrakilidis, C. & Trachnas, E. (2012), “What Drives Housing Price Dynamics in Greece: New Evidence from Asymmetric ARDL Cointegration.”, Economic Modelling, no.29, pp.1064-1069.
Koop, G. and Pesaran, M. H. and Potter, S. M. (1966), “Impulse Response Analysis in Nonlinear Multivariate Models.”, J. Econ., no 74, pp. 119-147.
Lue, L. and  Zhou, T. (2010), “Link Prediction in Weighted Networks: the Role of Weak Ties”, Europhys Lett, no.89.
Managi, S. and Hibiki, A. and Tsurumi, T. (2009), “Does Trade Openness Improve Environmental Quality?”, J Environ Econ Manage, no.58(3), pp.346-363.
Managi, Sh. & Kitamura, T. (2017), “Diving Force and Resistance: Network Feature in Oil Trade.”, Applied Energy, no.208, pp.361-375.
Narayan, P. K. & Narayan, S. (2004), “Estimating Income and Price Elasticity’s of Imports for Fiji in a Cointegration Framework”, Economic Modeling, vol. 22, pp. 423-438.
Niu, J. (2017), “A Study of the Influencing Factors of the Export Trade of Beijing's Cultural Creativity Industry.”, Am. J. Ind. Bus. Manag.no. 7 (1), pp.69-77.
Novy, D. (2013), “Gravity Redux: Measuring International Trade Costs with Panel Data.”, Econ. Inq., no. 51 (1), pp.101-121.
Persson, M. & Wilhelmsson, F. (2016), “EU Trade Preferences and Export Diversification.”, World Econ, no.39, pp.16-53.
Pesaran, M. H. and Shin, Y. and Smith, R. J. (2001), “Bounds Testing Approaches to the Analysis of Level Relationships.”, Journal of Applied Econometrics, no.16,pp. 289-326.
Pesaran, H. H.  and Shin, Y. (1998), “Generalized Impulse Response Analysis in Linear Multivariate Models.”, Ecol. Lett. , no.58, pp.17-29.
Shin, Y. and Yu, B. and Greenwood-Nimmo, M. (2014). “Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework”. Festschrift in Honor of Peter Schmidt,pp.281-314.
Shin, Y. and Yu, B. M. and Greenwood-Nimmo (2011), “Modeling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework”, Mimeo.
Silva, J. S. and Tenreyro, S. (2006), “The Log of Gravity.”, Rev Econ Stat, no.88(4),pp. 641-658.
Tinbergen, J. (1962), “Shaping the World Economy; Suggestions for an International Economic Policy.”, New York: Twentieth Century Fund.
Tsurumi, T. and Managi, S. and Hibiki, A. (2015), “Do Environmental Regulations Increase Bilateral Trade Flow?”, B.E. J Econ Anal Policy, no.15(4),pp. 1549-1577.
Yazdani, M. & Pirpour, H. (2018), “Evaluating the Effect of Intra-Industry Trade on the Bilateral Trade Productivity for Petroleum Products of Iran.”, Energy Economics. ENEECO-03933.