Analysis and forecasting of gross industrial value added on the example of the Nizhny Novgorod region

M. Yu. Malkina (), O. V. Kapitanova () and A. V. Semenov ()

Russian Journal of Industrial Economics, 2025, vol. 18, issue 4

Abstract: Under the influence of global shocks and macroeconomic instability it is essential to develop more advanced approaches to forecasting the gross regional product (GRP) and its components both at the country and region levels. Forecasting of GRP involves selection and justification of the key factors determining its dynamics. In the study, the formation of gross value added (GVA) of the industry was analyzed on the example of a fairly developed Russian industrial region – the Nizhny Novgorod region. To this end, the authors built a two-level GVA econometric model of industry of the Nizhny Novgorod region, which showed that the value is statistically significantly affected by such factors as the average per capita monetary income of the population and the average annual official dollar exchange rate. It has been stated that the dynamics of the average per capita monetary income of the population, in its turn, depend on the average price of Urals crude oil, gratuitous receipts to the consolidated budget of the region and the average annual number of employed people. The choice of factors is determined by the statistical procedure that allows revealing the relationships using time series cointegration. On the basis of the created two-level model for the GVA of the industry of the region and the models for exogenous factors, the authors make forecasts for all the involved indicators for the period up to 2026. The results of the study can be useful for the regional authorities in creating scenarios of development of the industry and determining the effectiveness of the control factors, which will make it possible to make sound management decisions in industrial policy and strategic planning.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ach:journl:y:2025:id:1493

DOI: 10.17073/2072-1633-2025-4-1493

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