Author 1:
Name & Surname: Linnik Yu. N.
Company: State University of Management (Moscow, Russia)
Work Position: Professor, Department of Oil and Gas Industry Economics and Management
Scientific Degree: Doctor of Engineering Sciences
Contacts: e-mail: yn_linnik@guu.ru

Author 2:
Name & Surname: Afanasev V. Ya.
Company: State University of Management (Moscow, Russia)
Work Position: Head of department, Professor
Scientific Degree: Doctor of Engineering Sciences

Author 3:
Name & Surname: Linnik V. Yu.
Company: State University of Management (Moscow, Russia)
Work Position: Assistant Professor
Scientific Degree: Doctor of Economical Sciences

Author 4:
Name & Surname: Tretyakova M. V.
Company: Ministry of Energy (Moscow, Russia)
Work Position: Leading Specialist

Having specified the considerable contribution made by the oil extraction industry in the revenue of the federal budget of Russia, the authors state the absence of a proper government-approved and authorized procedure for Urals Crude oil price forecast and describe the research aimed to develop adaptable econometric models of short-term oil price forecasting as an alternative for the internationally recognized forecasting tool — Consensus Forecast. The scope of the research included estimation of time series of daily prices of Urals and Brent Crude oils from 2002 through 2013 (output of 3656 units) using EViews7 software; relation of the prices of these oils; image-logit analysis and testing of a time series to reveal a trend and the autocorrelation dependence analysis (ARIMA model); and appraisal of the accuracy of the forecast produced by two econometric models — exponential trend and ARIMA model. In terms of oil prices, the autoregressive integrated moving average (ARIMA) model is recommended for short-term forecast (to three quarters), while the exponential trend method is better to be applied to longer term forecasting (to three years).

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