Economic Analysis: Theory and Practice

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Methods to identify and quantify the relationship between regional economic indicators

Vol. 18, Iss. 12, DECEMBER 2019

Received: 7 October 2019

Received in revised form: 15 October 2019

Accepted: 31 October 2019

Available online: 25 December 2019


JEL Classification: С3, С5, R15

Pages: 2339–2355

Granitsa Yu.V. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation

ORCID id: not available

Subject The article analyzes methods for identifying and quantifying the relationships between economic indicators to predict the financial instability of regional structures.
Objectives The purpose of the study is to investigate economic and statistical tools, which are adequate for the analysis of interrelations between regional economic indicators.
Methods I employ statistical, calculation-constructive and economic-mathematical methods, and corresponding methods of data analysis.
Results Estimating the interrelations of absolute values of economic indicators with the help of the panel data analysis model with random effects gave grounds to identify significant regressors for assessing the volatility of per capita income. Fixed investments have a reverse effect on the volatility of per capita income. Comparable dependence is obtained in the linear model, where the growth rates of economic indicators are determined as regressors. The estimation of interrelations of factors, using the logit model showed that the most significant direct impact on the process of recession is characterized by per capita income, the share of influence of the standard regressor value is 46 percent. The standard indicator of the volume of deposits with the share of influence of 25% also show inverse dependence.
Conclusions Economic indicators of regional statistics clustered by Federal district should be evaluated, using the panel data analysis models with random effects. The preferred way to eliminate multicollinearity is the method of principal components. If compared with the Belsley method, it enables to build models with a full set of original economic determinants.

Keywords: financial instability, volatility, Belsley method, ridge regression, principal component analysis


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