Stochastic model for forecasting the dynamics of gross regional product and regional production resources

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Abstract

The article presents a stochastic model for forecasting dynamics of gross regional product (GRP), developed using statistical data from Samara Region for the period 1998–2023. The model enables assessment of investment impact on regional economic development. To describe GRP dynamics, we propose a stochastic differential balance equation that relates GRP indicators to regional production resource (RPR) volumes. Within the study, we have: (1) estimated RPR volumes, (2) constructed theoretical trajectories of GRP and RPR dynamics, and (3) derived mathematical expectation curves for their growth. Numerical analysis demonstrates the model’s high consistency with empirical data.

About the authors

Leonid A. Saraev

Samara National Research University

Author for correspondence.
Email: saraev_leo@mail.ru
ORCID iD: 0000-0003-3625-5921
Scopus Author ID: 57219452875
https://www.mathnet.ru/rus/person41652

Dr. Phys. & Math. Sci., Professor; Professor; Dept. of Mathematics and Business Informatics

Russian Federation, 443086, Samara, Moskovskoye shosse, 34

Anastasiya V. Yuklasova

Samara National Research University

Email: yuklasova.anasta@mail.ru
ORCID iD: 0009-0007-9684-8864
https://www.mathnet.ru/eng/person230061

Cand. Econ. Sci., Associate Professor; Associate Professor; Dept. of State and Municipal Administration

Russian Federation, 443086, Samara, Moskovskoye shosse, 34

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Supplementary files

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2. Figure 1. Empirical trajectory of gross regional product $V$ growth (according to Table 1) and predicted trajectory of regional production resource $Q$ growth (according to Table 2)

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3. Figure 2. Stochastic growth trajectories of gross regional product $V(t)$ and regional production resource $Q(t)$ obtained through: numerical simulation using algorithm (12), and calculations via formula (22); points represent statistical data from Tables 1 and 2

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4. Figure 3. Mathematical expectations $\langle V \rangle$ and $\langle Q \rangle$ obtained by numerical solution of the Cauchy problem (18), (10) using (22); points represent statistical data from Tables 1 and 2

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