Zubarev, Andrey and Kirillova, Maria (2021): Эконометрическая оценка влияния шоков на рынке нефти на макроэкономические показатели Российской Федерации с помощью GVAR моделирования.
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Abstract
In this paper we use a global vector autoregression (GVAR) model to study the response of Russian macroeconomic indicators to external shocks. The model includes individual models for the world's largest economies and a model for the oil market. Our specification takes into account the peculiarities of the Russian economy and the persistence of variables in the oil market. We also obtained the impulse response functions to the oil supply shock in Saudi Arabia.
Item Type: | MPRA Paper |
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Original Title: | Эконометрическая оценка влияния шоков на рынке нефти на макроэкономические показатели Российской Федерации с помощью GVAR моделирования |
English Title: | The Impact of Oil Market Shocks on the Macroeconomic Indicators of the Russian Federation: GVAR Approach |
Language: | Russian |
Keywords: | global vector autoregression, GVAR, oil prices, GDP, oil production, impulse response function |
Subjects: | C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications F - International Economics > F4 - Macroeconomic Aspects of International Trade and Finance > F47 - Forecasting and Simulation: Models and Applications |
Item ID: | 110410 |
Depositing User: | Ms Maria Kirillova |
Date Deposited: | 06 Nov 2021 09:21 |
Last Modified: | 06 Nov 2021 09:21 |
References: | 1. Божечкова А., Трунин П. Анализ факторов динамики реального валютного курса // Издательский дом «Дело» РАНХиГС. 2016. 2. Дробышевский С., Идрисов Г., Каукин А., Павлов П., Синельников Мурылев С. Декомпозиция темпов роста российской экономики в 2007—2017 гг. и прогноз на 2018—2020 гг. // Вопросы экономики. 2018. №. 9. С. 5-31. 3. Казакова М. , Синельников-Мурылев С. Конъюнктура мирового рынка энергоносителей и темпы экономического роста в России // Экономическая политика. 2009. №. 5. С. 118-135. 4. Ломиворотов Р. Влияние внешних шоков и денежно-кредитной политики на экономику России // Вопросы экономики. 2014. № 11. С. 122–139. 5. Пестова А., Мамонов М. Оценка влияния различных шоков на динамику макроэкономических показателей в России и разработка условных прогнозов на основе BVAR-модели российской экономики //Экономическая политика. 2016. Т. 11. №. 4.С. 56-92. 6. Полбин А. В. Оценка траектории темпов трендового роста ВВП России в ARX-модели с ценами на нефть // Экономическая политика. 2020. Т.15. №.1.С.40-63. 7. Полбин А., Скроботов А. Тестирование наличия изломов в тренде структурной компоненты ВВП Российской Федерации // Экономический журнал Высшей школы экономики. 2016. Т. 20, №. 4. 8. Трунин П., Князев Д., Кудюкина Е. Анализ факторов динамики обменного курса рубля // Научные труды Института Гайдара. 2010. №. 144Р. 9. Bettendorf T. Investigating Global Imbalances: Empirical evidence from a GVAR approach // Economic Modelling. 2017. Vol. 64. P. 201-210. 10. Cashin P. et al The differential effects of oil demand and supply shocks on the global economy // Energy Economics. 2014. Vol. 44. P. 113-134. 11. Cesa-Bianchi A. Housing cycles and macroeconomic fluctuations: A global perspective // Journal of International Money and Finance. 2013. Vol. 37. P. 215-238. 12. Chudik A. et al. A counterfactual economic analysis of Covid-19 using a threshold augmented multi-country model // National Bureau of Economic Research. 2020.No.w27855. 13. Dees S. et al. Constructing Multi‐Country Rational Expectations Models //Oxford Bulletin of Economics and Statistics. 2014. Vol. 76. No. 6. P.812-840. 14. Dees S. et al Exploring the international linkages of the euro area: a global VAR analysis // Journal of applied econometrics. 2007. Vol. 22. No 1. P. 1-38. 15. Dees S. et al. Identification of new Keynesian Phillips curves from a global perspective // Journal of Money, Credit and Banking. 2009. Vol. 41No. 7.P. 1481-1502. 16. Fokin N., Polbin A. A Bivariate Forecasting Model For Russian GDP Under Structural Changes In Monetary Policy and Long-Term Growth // University Library of Munich. 2019. No. 95306. 17. Gauvin L. et al. Towards Recoupling? Assessing the Impact of a Chinese Hard Landing on Commodity Exporters: Results from Conditional Forecast in a GVAR Model // University Library of Munich.2013. No. 65457. 18. Jibril N. U., Halaç U. Oil Price Shocks and Macroeconomic Instability in Nigeria: Evidence from GVAR // International Journal of Contemporary Economics and Administrative Sciences. 2019. Vol. 9. No. 1. P. 94-118. 19. Kilian L. The impact of the fracking boom on Arab oil producers // The Energy Journal. 2017. Vol. 38. No. 6. 20. Kilian L. Oil price shocks: Causes and consequences // Annu. Rev. Resour. Econ. 2014. Vol. 6. No. 1. P. 133-154. 21. Koop G., Pesaran M., Potter S. Impulse response analysis in nonlinear multivariate models // Journal of econometrics. 1996. Vol. 74. No. 1. P. 119-147. 22. Krane J. A refined approach: Saudi Arabia moves beyond crude // Energy Policy. 2015. Vol. 82. P. 99-104. 23. Lardic S., Mignon V. The impact of oil prices on GDP in European countries: An empirical investigation based on asymmetric cointegration // Energy policy. 2006. Vol. 34. No. 18. P. 3910-3915. 24. Lardic S., Mignon V. Oil prices and economic activity: An asymmetric cointegration approach // Energy Economics. 2008. Vol. 30. No. 3. P. 847-855. 25. Lee C. Energy consumption and GDP in developing countries: a cointegrated panel analysis // Energy economics. 2005. Vol. 27.No. 3. P. 415-427. 26. Milani F. COVID-19 outbreak, social response, and early economic effects: a global VAR analysis of cross-country interdependencies // Journal of Population Economics. 2020. 27. Mohaddes K., Pesaran M. Country-specific oil supply shocks and the global economy: A counterfactual analysis // Energy Economics. 2016. Vol. 59. P. 382-399. 28. Mohaddes K., Raissi M. The US oil supply revolution and the global economy // Empirical Economics. 2019. Vol. 57. No. 5. P. 1515-1546. 29. Narayan P., Smyth R. Energy consumption and real GDP in G7 countries: new evidence from panel cointegration with structural breaks // Energy Economics. 2008. Vol. 30. No. 5. P. 2331-2341. 30. Neghad M., Hosseini R. Effects of Oil Shocks on the Unemployment: GVAR Approach // Romanian Economic Journal. 2017. Vol. 20. No. 65. 31. Neri S., Nobili A. The transmission of US monetary policy to the euro area // International Finance. 2010. Vol. 13. No. 1. P. 55-78. 32. Olayungbo D. The US–China trade dispute: spill-over effects for selected oil-exporting countries in Africa using GVAR analysis // Transnational Corporations Review. 2019. Vol. 11. No. 4. P. 310-322. 33. Pesaran M., Schuermann T., Weiner S. Modeling regional interdependencies using a global error-correcting macroeconometric model // Journal of Business & Economic Statistics. 2004. Vol. 22. No. 2. P. 129-162. 34. Pesaran M., Shin Y. Generalized impulse response analysis in linear multivariate models // Economics letters. 1998. Vol. 58. No. 1. P. 17-29. 35. Sims C. Macroeconomics and reality // Econometrica: journal of the Econometric Society. 1980. P. 1-48. 36. Smith V. L., Galesi A. https://sites.google.com/site/gvarmodelling/gvar-toolbox 37. Smith L. V., Tarui N., Yamagata T. Assessing the impact of COVID-19 on global fossil fuel consumption and CO2 emissions // ISER DP. 2020. No.1093. 38. Wei H., Lahiri R. The impact of commodity price shocks in the presence of a trading relationship: A GVAR analysis of the NAFTA // Energy Economics. 2019. Vol. 80. P. 553-569. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/110410 |