FACTOR ANALYSIS OF THE VOLATILITY OF AGRICULTURAL PRODUCTION IN THE RUSSIAN FEDERATION AS AN INDICATOR OF THE ECONOMIC CRISIS
Abstract and keywords
Abstract (English):
Relevance. The great potential for the development of the agricultural industry in the Russian Federation necessitates the study of factors influencing it. In the current crisis conditions of economic development, this direction will help identify the causes of the development of agricultural crises, make timely management decisions related to minimizing the consequences of their negative impact and rapid economic recovery. The purpose of the study is to analyze the factors of agricultural activity that can reflect the impact of instability and economic crises on the development of the agricultural sector. Methods. The methods of normalized sliding standard deviation, a model of multiple linear regression, a forecasting method based on the ARIMA model, and the Granger causality test were used. Scientific novelty. For the first time in Russian practice, the volatility of agricultural indicators was assessed, the links between these indicators and shocks in financial markets were revealed. Results. A statistical relationship between the volume of agricultural production and instability factors has been modeled, showing that 87.5 % of the variation in agricultural production can be explained by variations in variables – water, labor. The causality test showed that instability in the agricultural sector is the reason for the volatility of the RTS index, the index of industrial production. The growth of agricultural production is predicted for all three forecasting scenarios. The most likely (optimal) scenario showed an increase in production volumes to 9806,9 billion rubles by the end of 2023, to 10 707,1 billion rubles by the end of 2024 and to 11 605,1 billion rubles by the end of 2025. It is concluded that financial shocks cause an increase in volatility in agricultural markets.

Keywords:
agriculture, economic crisis, crisis indicators, instability factors, volatility, forecast, causality test, statistical relationship
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