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ИД «Финансы и кредит»






Economic Analysis: Theory and Practice

Using econometric models to make management decisions at oil and gas enterprises

Vol. 21, Iss. 4, APRIL 2022

Received: 27 March 2022

Received in revised form: 6 April 2022

Accepted: 17 April 2022

Available online: 28 April 2022


JEL Classification: C53, F47

Pages: 765–779


Tat'yana Yu. KUDRYAVTSEVA Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation


Evgeniya A. KOZLOVA Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russian Federation

ORCID id: not available

Subject. At present, there is a need to increase the flexibility of exporting companies and gas traders to ensure the stability and continuity of supplies in the European gas market. The specialized literature does not fully present scientifically based methods and algorithms, according to which a management decision can be made on the problem.
Objectives. Our aim is to develop a methodology enabling to qualitatively and quantitatively substantiate the feasibility of building a certain portfolio of capacities, and to evaluate the effects of its implementation.
Methods. The study draws on general scientific research methods.
Results. We developed and tested a model that allows modeling and forecasting of high-frequency time series, using VARMA and ARIMA econometric models, to make accurate and reasonable short- and medium-term decisions, and to generate a growth strategy for enterprises operating in the oil and gas industry.
Conclusions. Our findings correspond to the qualitative and quantitative assessment of the International Energy Agency, thus confirming that the model is relevant for use in the oil and gas industry. The findings can be applied in educational process in the field of ‘macroeconomics’, ‘econometrics’, in the implementation of operational and strategic planning at enterprises. They also can be useful for research and development, including testing at enterprises of other sectors and industries.

Keywords: VARMA model, demand modeling, demand forecasting, high-frequency data, management decision


  1. Kozlova E.A., Kudryavtseva T.Yu. [Ways of capacity portfolio structuring of energy company in order of market flexibility provision]. Estestvenno-gumanitarnye issledovaniya = Natural Humanitarian Studies, 2020, no. 30, pp. 64–69. URL: Link? (In Russ.)
  2. Dufour G.-M., Pelletier D. Practical methods for modelling weak VARMA processes: Identification, estimation and specification with a macroeconomic application. April 2011. URL: Link
  3. Granger C.W.J. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 1969, vol. 37, no. 3, pp. 424–438. URL: Link
  4. Granger C.W.L., Newbold P. Spurious regression in economics. Journal of Econometrics, 1974, vol. 2, no. 2, pp. 111–120. URL: Link90034-7
  5. James C., Koreisha S., Partch M. A VARMA Analysis of the Causal Relations Among Stock Returns, Real Output, and Nominal Interest Rates. The Journal of Finance, 1985, vol. 40, iss. 5, pp. 1375–1384. URL: Link
  6. Jenkins G.M., Alavi A.S. Some Aspects of Modelling and Forecasting Multivariate Time Series. Journal of Time Series Analysis, 1981, vol. 2, iss. 1, pp. 1–47. URL: Link
  7. Johansen S. Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 1988, vol. 12, iss. 2-3, pp. 231–254. URL: Link90041-3
  8. Lütkepohl H. Forecasting with VARMA models. Handbook of Economic Forecasting, 2006, vol. 1, pp. 287–325. URL: Link01006-2
  9. Simionescu M. The Use of VARMA Models in Forecasting Macroeconomic Indicators. Economics & Sociology, 2013, vol. 6, no. 2, pp. 94–102. URL: Link
  10. Tsay R.S., Tiao G.C. Use of Canonical Analysis in Time Series Model Identification. Biometrika, 1985, vol. 72, no. 2, pp. 299–315. URL: Link

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ISSN 2311-8725 (Online)
ISSN 2073-039X (Print)

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Vol. 21, Iss. 4
April 2022