Macroeconomic Factors Affecting Petroleum Products Export: Evidence From The Frequentist and Bayesian Regression

  • Authors

    • Haris Noor Department of West Asian & North African Studies, Aligarh Muslim University, Aligarh, India
    • Aneesur Rahman Department of Economics, Aligarh Muslim University, Aligarh, India
    • Dr. Sana Noor Department of West Asian & North African Studies, Aligarh Muslim University, Aligarh, India
    • Mohd Kadir Ansari Department of Economics, Aligarh Muslim University, Aligarh, India
    • Mohd Fateh Department of Agricultural Eco. & Business Mngt, Aligarh Muslim University, Aligarh, India
    • Dr. Md. Firdos Ahmad Department of Economics, Aligarh Muslim University, Aligarh, India
    • Mahazabi Department of Applied Business Economics, Dayalbagh Educational Institute, Agra, India
    https://doi.org/10.14419/f5vx8x77

    Received date: July 15, 2025

    Accepted date: August 27, 2025

    Published date: September 6, 2025

  • ARDL, Bayesian Regression, OPEC, OLS, Petroleum Products

  • Abstract

    This study delves into the intricacies of India's export of petroleum products within the dynamic landscape of the Gulf Cooperation Council (GCC) countries by utilizing extensive export data. Besides this, some critical macroeconomic variables affecting India’s export of petroleum products, namely OPEC oil prices and India's economic size, which is epitomized by gross domestic product, are analyzed. The empirical analysis time frame is from 2000 to 2023. To examine linkages between the variables with their coefficient signs, this study uses autoregressive distributed lag bound testing to obtain short and long-run estimates with an error correction model and ordinary least squares. Moreover, non-frequentist Bayesian linear regression is used for validity estimations. The findings reveal that both frequentist and non-frequentist approaches justify that the OPEC oil prices and India's GDP significantly affect India’s export of petroleum products to GCC countries. Lastly, diagnostic tests are applied to check stability and robustness in both frequentist and non-frequentist Bayesian regression.

  • References

    1. Abbasi, K. R., Hussain, K., Radulescu, M., & Ozturk, I. (2022). Asymmetric impact of renewable and non-renewable energy on the industrial sec-tor in Pakistan: Fresh evidence from Bayesian and non-linear ARDL. Renewable Energy, 187, 944–957. https://doi.org/10.1016/j.renene.2022.02.012.
    2. Abdelsalam, M. A. M. (2023). Oil price fluctuations and economic growth: The case of MENA countries. Review of Economics and Political Sci-ence, 8(5), 353–379. https://doi.org/10.1108/REPS-12-2019-0162.
    3. Aidarova, A. B., Mukhamediyeva, G., Yessentayeva, A. A., Utemissova, G., Tastanbekova, K., Mustafayeva, B., & Myrzabekkyzy, K. (2024). Re-lationship between oil exports, renewable energy consumption, agriculture industry, and economic growth in selected OPEC countries: A panel ARDL analysis. International Journal of Energy Economics and Policy, 14(6), 344–352. https://doi.org/10.32479/ijeep.17201.
    4. Al Humssi, A., Petrovskaya, M., & Abueva, M. (2022). Modelling the Impact of World Oil Prices and the Mining and Quarrying Sector on the United Arab Emirates’ GDP. Mathematics, 11(1). https://doi.org/10.3390/math11010094.
    5. Alkahteeb, T. T., & Sultan, Z. A. (2019). Oil price and economic growth: The case of Indian economy. International Journal of Energy Economics and Policy, 9(3), 274–279. https://www.econjournals.com/index.php/ijeep/article/view/7602.
    6. Azhar, M. (2021). India’s Emergence as a Petroleum Products Exporter. The Journal of Energy and Development, 47(1/2), 75–100. https://www.jstor.org/stable/27188914.
    7. Baumeister, C., & Hamilton, J. D. (2021). Drawing conclusions about economic policy from Bayesian vector autoregressions. Journal of Economet-rics, 222(1), 271–293. https://ideas.repec.org/a/eee/jimfin/v109y2020ics0261560620302060.html.
    8. Bhattacharyya, S. C., & Blake, A. (2009). Domestic demand for petroleum products in MENA countries. Energy Policy, 37(4), 1552–1560. https://ideas.repec.org/a/eee/enepol/v37y2009i4p1552-1560.html.
    9. Block, J. H., & Wagner, M. (2014). The effect of family ownership on different dimensions of corporate social responsibility: Evidence from large US firms. Business Strategy and the Environment, 23(7), 475–492. https://onlinelibrary.wiley.com/doi/abs/10.1002/bse.1798.
    10. Briggs, W. M. (2023). A partial solution for the replication crisis in economics. Asian Journal of Economics and Banking, 7(2), 180–190. https://doi.org/10.1108/AJEB-03-2023-0027.
    11. Carriero, A., Clark, T. E., & Marcellino, M. (2022). Bayesian VARs: Specification choices and forecast accuracy. Journal of Applied Econometrics, 37(5), 901–920. https://ideas.repec.org/a/wly/japmet/v30y2015i1p46-73.html.
    12. Chan, J. C. C., & Eisenstat, E. (2022). Bayesian model averaging in oil market analysis. Energy Economics, 105.
    13. Dagar, V., & Malik, S. (2023). Nexus between macroeconomic uncertainty, oil prices, and exports: Evidence from quantile-on-quantile regression approach. Environmental Science and Pollution Research, 30(16), 48363–48374. https://link.springer.com/article/10.1007/s11356-023-25574-9.
    14. Del Negro, M., & Primiceri, G. E. (2022). Time-varying structural vector autoregressions and monetary policy. Review of Economic Studies, 89(2), 1002–1043. https://ideas.repec.org/a/oup/restud/v72y2005i3p821-852.html.
    15. Dion, M. L. (2022). Teaching ordinary least squares regression. In Teaching Graduate Political Methodology (pp. 134–142). Edward Elgar Publish-ing.
    16. Drachal, K., & Pawłowski, M. (2024). Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression. International Journal of Financial Studies, 12(2), 34. https://www.mdpi.com/2227-7072/12/2/34.
    17. Fraza, C. J., Dinga, R., Beckmann, C. F., & Marquand, A. F. (2021). Warped Bayesian linear regression for normative modelling of big data. Neu-roImage, 245. https://doi.org/10.1016/j.neuroimage.2021.118715.
    18. Georgiou, G. P. (2024). Bayesian Models Are More Sensitive than Frequentist Models in Identifying Differences in Small Datasets Comprising Phonetic Data. Stats, 7(4). https://doi.org/10.3390/stats7040087.
    19. Giannone, D., Lenza, M., & Primiceri, G. E. (2021). Priors for the long run. Econometrica, 89(1), 151–194. https://ideas.repec.org/p/ecb/ecbwps/20182132.html.
    20. Gupta, P., & Goyal, A. (2015a). Impact of oil price fluctuations on Indian economy. OPEC Energy Review, 39(2), 141–161. https://onlinelibrary.wiley.com/doi/abs/10.1111/opec.12046.
    21. Gupta, P., & Goyal, A. (2015b). Impact of oil price fluctuations on Indian economy. OPEC Energy Review, 39(2), 141–161. https://onlinelibrary.wiley.com/doi/abs/10.1111/opec.12046.
    22. Hastie, T., & Tibshirani, R. (2022). Bayesian shrinkage priors in regression. Statistical Science, 37(2), 197–214.
    23. Iqbal, M., Arif, A. S., Jadoon, A. K., & Rana, A. D. (2023). The impact of debt service and inflation on economic growth in Pakistan: Evidence from ARDL model and approximate Bayesian analysis. Pakistan Journal of Commerce and Social Sciences (PJCSS), 17(2), 263–287. https://www.econstor.eu/handle/10419/278052.
    24. Jiao, Z., Sharma, R., Kautish, P., & Hussain, H. I. (2021). Unveiling the asymmetric impact of exports, oil prices, technological innovations, and income inequality on carbon emissions in India. Resources Policy, 74. https://ideas.repec.org/a/eee/jrpoli/v74y2021ics0301420721004177.html.
    25. Kalia, A. (2024a). CEO power and stock price crash risk in India: The moderating effect of insider trades. Asian Journal of Economics and Banking, ahead-of-print. https://doi.org/10.1108/AJEB-10-2023-0095.
    26. Kalia, A. (2024b). CEO power and stock price crash risk in India: The moderating effect of insider trades. Asian Journal of Economics and Bank-ing, ahead-of-print. https://doi.org/10.1108/AJEB-10-2023-0095.
    27. Karlsson, S. (2021). Forecasting with Bayesian methods. In G. Elliott & A. Timmermann (Eds.), Handbook of economic forecasting (Vol. 2B, pp. 791–897). Elsevier. https://ideas.repec.org/h/eee/ecofch/2-791.html.
    28. Kathiravan, C., Selvam, M., Maniam, B., Dana, L. P., & Babu, M. (2023). The Effects of Crude Oil Price Surprises on National Income: Evidence from India. Energies, 16(3). https://doi.org/10.3390/en16031148.
    29. Koop, G., & Korobilis, D. (2021). Large time-varying parameter VARs with stochastic volatility. Journal of Econometrics, 222(1), 36–54. https://ideas.repec.org/a/eee/econom/v177y2013i2p185-198.html.
    30. Kotsakis, C. (2023). Ordinary Least Squares. In Encyclopedia of Mathematical Geosciences (pp. 1032–1038). Springer. https://doi.org/10.1007/978-3-030-85040-1_237.
    31. Kulkarni, M., Tikyani, J., & Singh, K. K. (2023). Impact of COVID-19 on Import and Export of Petroleum Products and Crude Oil in India. 21–31. https://www.springerprofessional.de/en/impact-of-covid-19-on-import-and-export-of-petroleum-products-an/24060730.
    32. Latief, N. F., Chauhan, R., Thakar, I., Maseleno, A., & Wahyudi, A. (2020). Impact, Cause and Trend: India’s Trade of Crude and Petroleum Products. International Journal of Advanced Science and Technology, 29(06), 1823–1831. https://repository.iain-manado.ac.id/405/.
    33. Lee, C.-Y., & Huh, S.-Y. (2017). Forecasting long-term crude oil prices using a Bayesian model with informative priors. Sustainability, 9(2), 190. https://doi.org/10.3390/su9020190.
    34. Liu, H., & Chen, Y. (2023). Bayesian structural equation modeling of renewable energy, oil prices, and GDP in emerging economies. Renewable and Sustainable Energy Reviews, 174.
    35. Mehrara, M., Seijani, S., & Karsalari, A. R. (2017). Determinants of high-tech export in developing countries based on Bayesian model averaging. Zbornik Radova Ekonomskog Fakulteta u Rijeci: Časopis Za Ekonomsku Teoriju i Praksu, 35(1), 199–215. https://ideas.repec.org/a/rfe/zbefri/v35y2017i1p199-215.html.
    36. Michaelides, P. G. (2025). Ordinary Least Squares. In 21 Equations that Shaped the World Economy: Understanding the Theory Behind the Equa-tions (pp. 29–44). Springer. https://doi.org/10.1007/978-3-031-76140-9.
    37. Montant, G. (2024). The Effectiveness of OPEC and Opec+ from 2009 to 2023: An Empirical Appraisal. https://dx.doi.org/10.2139/ssrn.4700404.
    38. Negi, P. (2015). Impact of oil price on economic growth: A study of BRIC nations. Indian Journal of Accounting, 47(1), 144–155. https://www.scirp.org/reference/referencespapers?referenceid=2694516.
    39. Nguyen, H. T., Trung, N. D., & Thach, N. N. (2019). Beyond traditional probabilistic methods in econometrics. 3–21. http://dx.doi.org/10.1007/978-3-030-04200-4_1.
    40. Noor, H., & Noor, S. (2024). A Gravity Model Approach for Determining Foreign Direct Investment Flows between India and the Gulf Coopera-tion Council (GCC) Countries. Saudi J Econ Fin, 8(7), 220-229. https://saudijournals.com/articles/10418/.
    41. Obadi, S. M., & Korcek, M. (2024). The relationship between geopolitical events and the crude oil prices: An application of ARDL model. Interna-tional Journal of Energy Economics and Policy, 14(5), 85–97. https://doi.org/10.32479/ijeep.16487.
    42. Olayungbo, D. O., Zhuparova, A., & Al-Faryan, M. A. S. (2023). Oil supply and oil price determination among OPEC and non-OPEC countries: Bayesian Granger network analysis. Economic Change and Restructuring, 56(6), 4603–4628. http://dx.doi.org/10.1007/s10644-023-09565-x.
    43. Pandey, R., Chand, D. B., & Tolani, H. (2023). Bayesian Multiple Linear Regression Model for Gross Domestic Product in Bhutan. Advances and Applications in Statistics, 87(2), 161–190. http://dx.doi.org/10.17654/0972361723033.
    44. Pandey, R., Chand, D. B., & Tolani, H. (2024). Bayesian multiple linear regression model for GDP in India. International Journal of System Assur-ance Engineering and Management, 15(6), 2170–2187. https://doi.org/10.1007/s13198-023-02233-3.
    45. Parikh, J., Purohit, P., & Maitra, P. (2007). Demand projections of petroleum products and natural gas in India. Energy, 32(10), 1825–1837. https://doi.org/10.1016/j.energy.2007.02.008.
    46. Periwal, A. (2023). The Impact of Crude Oil Price Fluctuations on Indian Economy. International Journal for Research in Applied Science and En-gineering Technology, 11(4), 3173–3202. https://doi.org/10.22214/ijraset.2023.50851.
    47. Pesaran, M. H., & Shin, Y. (1995). An autoregressive distributed lag modelling approach to cointegration analysis (Vol. 9514). Department of Ap-plied Economics, University of Cambridge Cambridge, UK. https://ideas.repec.org/p/cam/camdae/9514.html.
    48. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://www.jstor.org/stable/2678547.
    49. Petrović, A., & Vukadinović, P. (2022). Bayesian quantile regression for crude oil price volatility. Applied Economics Letters, 29(20).
    50. Rahiman, H. U., & Kodikal, R. (2019). Ups and downs in the Indian economy: The impact of crude oil prices. Int. J. Sci. Technol. Res, 8(8), 846–857.
    51. Rajan, A. M. S. (2012). Globalization and Its Impact on India’s Petroleum Exports. Journal Impact Factor, 3(3), 17–24. https://iaeme.com/Home/article_id/IJM_03_03_003.
    52. Rao, R. D., & Parikh, J. K. (1996). Forecast and analysis of demand for petroleum products in India. Energy Policy, 24(6), 583–592. https://ideas.repec.org/a/eee/enepol/v24y1996i6p583-592.html.
    53. Rafiqul, I., & Alam, S. (2022). Bayesian hierarchical modeling of CO₂ emissions and energy use in Asia. Energy Policy, 165, http://dx.doi.org/10.1177/21582440241268739.
    54. Roy, A. (2023). Nexus between economic growth, external debt, oil price, and remittances in India: New insight from novel DARDL simulations. Resources Policy, 83, https://doi.org/10.1016/j.resourpol.2023.103742.
    55. Salem, L. B., Nouira, R., Saafi, S., & Rault, C. (2024). How do oil prices affect the GDP and its components? New evidence from a time-varying threshold model. Energy Policy, 190, https://ideas.repec.org/a/eee/enepol/v190y2024ics0301421524001824.html.
    56. Sarkar, B., & Mathew, J. (2018). Causes of Indian Basket Crude Oil Price Fluctuations and Its Impact on Indian Economy. SDMIMD Journal of Management, 9(2), 9–22. https://doi.org/10.18311/sdmimd/2018/21683.
    57. Shalu, K., Singh, K., Kaur, S., & Sikka, P. (2023). Petroleum Export Performance in India: Analysis of Growth and Structural Dynamics. https://www.soed.in/article/889.
    58. Sharma, P., & Shrivastava, A. K. (2024). Economic activities and oil price shocks in Indian outlook: Direction of causality and testing cointegration. Global Business Review, 25(3), 771–790. https://doi.org/10.1177/0972150921990491.
    59. Soundarapandiyan, K., & Ganesh, M. (2017). An Analytical view of crude oil prices and its impact on Indian Economy. IOSR Journal of Business and Management (IOSR-JBM), 23–28.
    60. Thach, N. N. (2020). How to explain when the ES is lower than one? A Bayesian nonlinear mixed-effects approach. Journal of Risk and Financial Management, 13(2), 21. https://doi.org/10.3390/jrfm13020021.
    61. Thach, N. N. (2023). Applying Monte Carlo simulations to a small data analysis of a case of economic growth in COVID-19 times. Sage Open, 13(2), https://doi.org/10.1177/21582440231181540.
    62. Thach, N. N. (2025). Which, renewable or non-renewable energy, is more vital for the economic growth of OECD countries? A Bayesian hierar-chical analysis. Heliyon. https://doi.org/10.1016/j.heliyon.2025.e42259.
    63. Thach, N. N., Kreinovich, V., Ha, D. T., & Trung, N. D. (2022). Financial econometrics: Bayesian analysis, quantum uncertainty, and related topics (Vol. 427). Springer Nature. http://dx.doi.org/10.1007/978-3-030-98689-6.
    64. Tiwari, A. K. (2015). On the dynamics of Indian GDP, crude oil production and imports. OPEC Energy Review, 39(2), 162–183. https://doi.org/10.1111/opec.12047.
    65. Tuan, T. A., Kreinovich, V., & Nguyen, T. N. (2019). Decision making under interval uncertainty: Beyond Hurwicz Pessimism-optimism criterion. 176–184. http://dx.doi.org/10.1007/978-3-030-04200-4_14.
    66. Van Doorn, J., Van Den Bergh, D., Böhm, U., Dablander, F., Derks, K., Draws, T., Etz, A., Evans, N. J., Gronau, Q. F., & Haaf, J. M. (2021). The JASP guidelines for conducting and reporting a Bayesian analysis. Psychonomic Bulletin & Review, 28, 813–826. https://doi.org/10.3758/s13423-020-01798-5.
    67. WDI_ DataBank.html. (2025).
    68. Wang, Z., & Zhang, L. (2021). A Bayesian approach to analyzing global energy trade networks. Energy Economics, 95, 105137.
    69. Wooditch, A., Johnson, N. J., Solymosi, R., Medina Ariza, J., & Langton, S. (2021). Ordinary least squares regression. In A Beginner’s guide to statistics for criminology and criminal justice using R (pp. 245–268). Springer. http://dx.doi.org/10.1007/978-3-030-50625-4.
    70. Zavaleta, A., Walls, W., & Rusco, F. W. (2015). Refining for export and the convergence of petroleum product prices. Energy Economics, 47, 206–214. https://ideas.repec.org/a/eee/eneeco/v47y2015icp206-214.html.

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    Noor, H., Rahman , A. ., Noor , D. S. ., Ansari , M. K. ., Fateh , M. ., Ahmad , D. M. F. ., & Mahazabi. (2025). Macroeconomic Factors Affecting Petroleum Products Export: Evidence From The Frequentist and Bayesian Regression. International Journal of Accounting and Economics Studies, 12(5), 248-263. https://doi.org/10.14419/f5vx8x77