Economic Analysis: Theory and Practice

Statistical and econometric assessment of risk

Vol. 17, Iss. 2, FEBRUARY 2018

Received: 14 November 2017

Received in revised form: 24 November 2017

Accepted: 15 December 2017

Available online: 1 March 2018


JEL Classification: 

Pages: 365–378

Lapteva E.A. Nizhny Novgorod State Agricultural Academy, Nizhny Novgorod, Russian Federation

ORCID id: not available

Bezaev I.I. Nizhny Novgorod State Agricultural Academy, Nizhny Novgorod, Russian Federation

ORCID id: not available

Importance Production activities are subject to various risks. They are mostly pronounced in the agricultural sector as it depends on natural and biological processes. Assessing and mitigating the risks are a necessary condition for effective management of businesses of different forms of ownership. The concept of risk is addressed in three ways: a threat, uncertainty and opportunity. The third aspect is most optimistic. It is mainly used when science and technology programs, innovative projects and investment policies are developed. The article considers the first two aspects.
Objectives The aim is to propose and justify a statistical and econometric method to assess entrepreneurial risk in agriculture.
Methods The study draws on works of leading foreign and domestic scientists in the field of economics and statistical methods of research and econometrics, annual reports of agricultural enterprises in the Nizhny Novgorod oblast. We also apply analysis and synthesis, induction and deduction, and methods of comparison and grouping.
Results The paper presents a classification of various types of risk in the agricultural sector of the economy. It specifies methods of risk assessment and risk profile.
Conclusions If the proposed methodology is used in management decision-making, it will enable a true picture of how the risks impact the final results of production and financial activities of agricultural organizations.

Keywords: agriculture, risk assessment, risk management, variance analysis, correlation-regression analysis

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

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November 2018