Econometric estimation of the impact of oil prices shock on the Russian economy in VECM model

Polbin, Andrey

doi:10.32609/0042-8736-2017-10-27-49

Cited by 10 publications

(8 citation statements)

References 31 publications

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“…Multipliers are calculated for oil prices for the variables of interest under the model with a common long-run growth rate, and are presented in Table 3. The 0.09 estimate for long-run oil price elasticity of GDP is similar to the 0.1 observed in domestic papers by Polbin and Skrobotov (2016) and Polbin (2017b). Papers by Rautava (2004), Beck et al (2007), and Kuboniwa (2014) produce an oil price elasticity of GDP of 0.2, which is significantly higher than our estimate.…”

Section: Estimation Resultssupporting

confidence: 85%

Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model

Fokin¹,

Polbin²

2019

RJMF

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This paper examines an application of the VAR-LASSO model to Russia's key macroeconomic indicators: GDP, household consumption, fixed asset investment, exports, imports, and the rouble real exchange rate, along with oil prices (as an exogenous variable). The slowdown in the Russian economy following the 2008-2009 crisis is modelled as a structural break in the unconditional mean of growth rates of the time series under examination. The model is estimated with the assumption of a common growth rate for GDP, consumption, investment, exports and imports (any discrepancies in actual growth rates are due to changing oil prices and other shocks), which provides a solid foundation for balanced medium-term forecasts using an econometric specification that factors in this constraint. The model exhibits fairly good predictive power when pseudo real-time forecasts are benchmarked against the forecast by the Ministry of Economic Development and the forecast given by the BVAR model in Pestova and Mamonov (2016b), as well as against the best (based on the BIC criterion) VAR(1) model and the classical ARIMA model. The estimated model is used to study functions for impulse responses to oil price shocks and to build scenario-driven forecasts for 2019-2024.

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Section: Estimation Resultssupporting

confidence: 85%

Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model

Fokin¹,

Polbin²

2019

RJMF

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“…Increased export income leads to increased consumption, investments, and GDP (Malakhovskaya and Minabutdinov, 2013). The results of a study by Polbin (2017) demonstrate that, contrary to the traditional opinion, growth of oil prices leads to growth of gross production only in the short term (2-2.5 years), while in the mid term, this correlation becomes negative.…”

Section: Literature Review On the Correlation Between The Commodity Amentioning

confidence: 80%

“…We also assessed the sensitivity of Russia's financial cycle parameters during a period of relatively homogeneous monetary policy (Polbin, 2017;Pestova, 2017): before the adoption of the inflation targeting (IT) regime in November 2014 (Figure 2). Assessment of empirical data in the January 2004-November 2014 period indicates a higher oil price elasticity for most lending indicators (the key credit risk indicators out of those in Table 2 are assessed), including the foreign currency predominance ratios, as compared to the elasticity of the same indicators over the entire assessment horizon (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017).…”

Section: Russiamentioning

confidence: 99%

Commodity and Financial Cycles in Resource-based Economies

Tiunova¹

2019

RJMF

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This research analyses the influence of commodity prices on financial cycle parameters in commodity-exporting countries-Australia, Brazil, Canada, Columbia, Russia, and Chile-over the past two decades. One of the key issues discussed herein is the degree to which the extensive implementation of macroprudential policies can reduce the dependence of a country on global commodity cycles. Methodologically, this research is based on the Bayesian Structure Vector Autoregressive Model, structurally identified by means of variable recursive ranking and the Cholesky decomposition of the error covariance matrix. Changes in commodity prices are shown to provoke a stronger response from such financial cycle parameters as the sovereign risk premium and currency exchange rate in resource-based emerging market economies (Brazil, Columbia, Chile, and Russia) than in advanced economies (Canada and Australia). In Brazil, Columbia, Chile, and Russia, increases in commodity prices result in acceleration of the overall lending and external debt growth rate, while in Russia and Brazil they also trigger growth in the share of FX loans. In Australia and Canada, lending parameters react negatively to positive commodity price shocks. In the developing countries that apply macroprudential policy extensively (Columbia and Chile), lending dynamics are less dependent on changes in the global terms of trade. To reduce the impact of the commodity cycle on the financial cycle, the economic policy authorities of emerging market countries should develop national financial markets and introduce macroprudental policy tools more extensively.

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“…The scientific search's main objective was to justify state oil purchase as the main factor in improving the Russian oil exchange market liquidity at the present stage of its development. The paper of (Polbin, 2017) estimates terms of trade shock influence on the Russian output, gross investment, and consumption using VECM model with exogenous variables. Empirical results demonstrate that a permanent oil price increase led to a short-run economic boom followed by a negative contribution to economic growth.…”

Section: Introductionmentioning

confidence: 99%

Brent and Urals Oil Price Control Mechanisms

Solovieva

Chernyaev

Владимировна

2021

IJEEP

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The article considers the main mechanisms for controlling the formation of oil prices, including Brent and Urals brands. Based on the analysis of trends in the oil market and their dynamics over the past decades, the authors identify factors affecting pricing in the oil market, different oil market price control systems, and their dynamics. Our results show that there is a substantial price gap in the world oil market between the Brent reference grade and the Urals crude grade, while the formation of the Urals brand price is opaque. Comparing the key characteristics of different systems of price control in the oil market, we revealed that under the influence of external and internal factors, the system of control over pricing in the oil market underwent significant changes throughout its existence. The authors conclude that at present, there are real prerequisites for the institutional correction of the global oil market in terms of strengthening the administrative levers of developed countries control over exchange and OTC pricing mechanisms.

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Econometric estimation of the impact of oil prices shock on the Russian economy in VECM model

Cited by 10 publications

References 31 publications

Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model

Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model

Commodity and Financial Cycles in Resource-based Economies

Brent and Urals Oil Price Control Mechanisms

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