DYNAMICS OF INDIVIDUAL BANKRUPTCIES IN RUSSIA: TIME SERIES ANALYSIS

In recent years, the number of individual bankruptcies in Russia has been steadily increasing. This trend puts pressure on the banking system and signals a recession in the domestic economy. This study sheds light on the previously insufficiently studied dynamics of individual bankruptcies in Russia, describes the temporal determinants and nature of bankruptcies, and also offers banks a model for predicting future trends. In this article, the author aims to create an econometric model that describes and predicts the number of bankruptcies of individuals in Russia using time series analysis tools. The methodology includes models such as the nonlinear least squares, ARIMA, and ETS. After evaluating various models, the best one was selected and analyzed. In this study, the author also shows the continuing trend towards an increase in the number of bankruptcies of individuals in Russia.

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Publication Details

Journal
SOFT MEASUREMENTS AND COMPUTING
Published
2025-01-01
DOI
https://doi.org/10.36871/2618-9976.2025.06.008
Primary Topic
Economic and Technological Developments in Russia
Type
article
Field-Weighted Citation Impact
0.00
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article

DYNAMICS OF INDIVIDUAL BANKRUPTCIES IN RUSSIA: TIME SERIES ANALYSIS

Matvey D. Shuvarikov
SOFT MEASUREMENTS AND COMPUTING
Economic and Technological Developments in Russia
article

DYNAMICS OF INDIVIDUAL BANKRUPTCIES IN RUSSIA: TIME SERIES ANALYSIS

Matvey D. Shuvarikov
article en

Abstract

In recent years, the number of individual bankruptcies in Russia has been steadily increasing. This trend puts pressure on the banking system and signals a recession in the domestic economy. This study sheds light on the previously insufficiently studied dynamics of individual bankruptcies in Russia, describes the temporal determinants and nature of bankruptcies, and also offers banks a model for predicting future trends. In this article, the author aims to create an econometric model that describes and predicts the number of bankruptcies of individuals in Russia using time series analysis tools. The methodology includes models such as the nonlinear least squares, ARIMA, and ETS. After evaluating various models, the best one was selected and analyzed. In this study, the author also shows the continuing trend towards an increase in the number of bankruptcies of individuals in Russia.

SOFT MEASUREMENTS AND COMPUTINGVol. 6(91)
Openalex Percentile: Top 64%
Economic and Technological Developments in Russia
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