Quarterly Publication

Document Type : Original Article

Authors

1 Instructor, Economics and Islamic Banking, Faculty of Economics, Kharazmi University, Tehran, Iran,

2 Assistant Professor, National Research Institute for Science Policy (NRISP), Tehran, Iran

3 Assistant Professor, University of Tehran, Tehran, Iran

4 Instructor, Allameh Tabataba’i University, Tehran, Iran

Abstract

This paper aims to show the asymmetric effect of oil shocks on Iran’s economy. It uses nonlinear time series models to investigate the asymmetric effect of oil shocks on resource allocation in Iran’s economy. The results show that adverse oil shocks have been more persistent during the last decades and severely negatively affect resource allocation in Iran’s economy. Different oil shocks have different implications for importing and exporting countries, and the rigidity of state fiscal systems in exporting countries causes adverse oil shocks to be more persistent. The oil economy’s response to positive and negative oil shocks depends on the structure of the economy. The government budget and trade balance have significant implications for the effects of oil shocks on oil-exporting economies. The government budget is highly dependent on oil revenues, so in the case of adverse oil shocks, the pass-through exchange rate will cause high inflation because of foreign exchange shortage and overshoot in the exchange rate. 

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Main Subjects

ADB. 2011. Asia 2050: Realizing the Asian Century. URL: https://www.adb.org/publications/asia-2050-realizing-asian-century [accessed 20.10.2021]
Aghashariatmadari, Z. 2021. The effects of COVID-19 pandemic on the air pollutants concentration during the lockdown in Tehran, Iran. Urban Climate. 38, 100882, doi: https://doi.org/10.1016/j.uclim.2021.100882
Andreoni, V. 2021. Estimating the European CO2 emissions change due to COVID-19 restrictions. Science of The Total Environment. 769, 145115, doi: https://doi.org/10.1016/j.scitotenv.2021.145115
Breitung, J., 2001. The local power of some unit root tests for panel data. In: Baltagi, B.H., Fomby, T.B., Hill, R.C. (Eds.), Nonstationary Panels, Panel Cointegration, and Dynamic Panels Advances in Econometrics Vol. 15. Emerald Group Publishing Limited, United Kingdom, pp. 161–177
Ehrlich, P.P., and Holdren, J.P. 1971.  Impact of population growth. Science. 171: 1212–1217.
Engel, R.F., Granger, C.W.J., 1987. Cointegration and error correction: representation, estimation, and testing. Econometrica 55: 251–276.
Fagbemi, F. 2021. COVID-19 and sustainable development goals (SDGs): An appraisal of the emanating effects in Nigeria. Research in Globalization. 3, 100047, doi: https://doi.org/10.1016/j.resglo.2021.100047
Fan, Y., Liu, L., Wu, G., and Wei, Y. 2006. Analyzing impact factors of CO2 emissions using the STIRPAT model. Environmental Impact Assessment Review. 26 (4): 377–395.
Gani, A. 2021. Fossil fuel energy and environmental performance in an extended STIRPAT model. Journal of Cleaner Production. 297, 126526, doi: https://doi.org/10.1016/j.jclepro.2021.126526
Gharemanloo, M., Lops, Y., Choi, Y., and Mousavinezhad, S. 2021. Impact of the COVID-19 outbreak on air pollution levels in East Asia. Science of The Total Environment. 754, 142226, doi: https://doi.org/10.1016/j.scitotenv.2020.142226
Hadri, K., 2000. Testing for stationarity in heterogeneous panel data. Econ. J. 3:148–161.
Hoang, A., Nizetic, S., Olcer, A., Ong, H., Chen, W., Chong, Ch., Thomas, S., Bandh, S., and Nguyen, X. 2021. Impacts of COVID-19 pandemic on the global energy system and the shift progress to renewable energy: Opportunities, challenges, and policy implications. Energy Policy, 154, 112322, doi: https://doi.org/10.1016/j.enpol.2021.112322
Im, K.-S., Pesaran, H., Shin, Y., 2003. Testing for unit roots in heterogeneous panels. J. Econ. 115: 53–74
Kao, C., 1999. Spurious regression and residual‐based tests for cointegration in panel data. J. Econ. 90: 1–44
Madkour, K. 2021. Monitoring the impacts of COVID-19 pandemic on climate change and the environment on Egypt using Sentinel-5P Images and the Carbon footprint methodology. The Egyptian Journal of Remote Sensing and Space Science. In Press. doi: https://doi.org/10.1016/j.ejrs.2021.07.003
Marazziti, D., Cianconi, P., Mucci, F., Foresi, L., Chiarantini, I., and Vecchia, A. 2021. Climate change, environment pollution, COVID-19 pandemic, and mental health. Science of The Total Environment. 773, 145182, doi: https://doi.org/10.1016/j.scitotenv.2021.145182
Nundy, S., Ghosh, A., Mesloub, A., Albaqawy, Gh., and Alnaim, M. 2021. Impact of COVID-19 pandemic on socio-economic, energy-environment and transport sector globally and sustainable development goal (SDG). Journal of Cleaner Production. 312, 127705, doi: https://doi.org/10.1016/j.jclepro.2021.127705
Pedroni, P., 1999. Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxf. Bull. Econ. Stat. 61: 653–670.
Pedroni, P., 2004. Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econ. Theory 20: 597–625
Pesaran, M., 2007. A simple panel unit root test in the presence of cross-section dependence. J. Appl. Econ. 22: 265–312.
Sikarwar, V., Reichert, A., Jeremias, M., and Manovic, V. 2021.  COVID-19 pandemic and global carbon dioxide emissions: A first assessment. Science of The Total Environment. 794, 148770, doi: https://doi.org/10.1016/j.scitotenv.2021.148770
Smith, L.V., Tarui, N., and Yamagata, T. 2021. Assessing the impact of COVID-19 on global fossil fuel consumption and CO2 emissions. Energy Economics. 97, 105170, doi: https://doi.org/10.1016/j.eneco.2021.105170
Travaglio, M., Yu, Y., Popovic, R., Selley, L., Leal, N., and Martins, L. 2021. Links between air pollution and COVID-19 in England. Environmental Pollution. 268 (A), 115859, doi: https://doi.org/10.1016/j.envpol.2020.115859
Wang, P., Wu, W., Zhu, B., and Wei, Y. 2013. Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China. Applied Energy. 106: 65–71.
Wang, Q., and Wang, Sh. 2020. Preventing carbon emission retaliatory rebound post-COVID-19 requires expanding free trade and improving energy efficiency. Science of The Total Environment. 746, 141158, doi: https://doi.org/10.1016/j.scitotenv.2020.141158
Wang, R., Xiong, Y., Xing, X., Yang, R., Li, J., Wang, Y., Cao, J., Balkanski, Y., Penuelas, J., Ciais, Ph., Hauglustaine, D., Sardens, J., Chen, J., Ma, J., Xu, T., Kan, H., Zhang, Y., Oda, T., and Tao, Sh. 2020. Daily CO2 Emission Reduction Indicates the Control of Activities to Contain COVID-19 in China. The Innovation. 1 (3), 100062.
Westerlund, J., 2007. Testing for error correction in panel data. Oxf. Bull. Econ. Stat. 69: 0305–9049
World Bank. 2021. The Global Economy: on Track for Strong but Uneven Growth as COVID-19 Still Weighs. URL: https://www.worldbank.org/en/news/feature/2021/06/08/the-global-economy-on-track-for-strong-but-uneven-growth-as-covid-19-still-weighs [accessed on 06.07.2021]
Zhang, S., and Zhao, T. 2019. Identifying major influencing factors of CO2 emissions in China: Regional disparities analysis based on STIRPAT model from 1996 to 2015. Atmospheric Environment. 207: 136–147.
Zhang, X., Li, Zh., and Wang, J. 2021. Impact of COVID-19 pandemic on energy consumption and carbon dioxide emissions in China’s transportation sector. Case Studies in Thermal Engineering. 26, 101091, doi: https://doi.org/10.1016/j.csite.2021.101091