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

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: С3, С5, R15

Pages: 2339–2355

https://doi.org/10.24891/ea.18.12.2339

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

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

References:

  1. Ivanov P.A., Sakhapova G.R. [Financial instability in the region: Assessment methods and elimination tools]. Ekonomicheskie i sotsial'nye peremeny: fakty, tendentsii, prognoz = Economic and Social Changes: Facts, Trends, Forecast, 2014, no. 6, pp. 183–198. URL: Link (In Russ.)
  2. Ivanova Z.Sh., Makhosheva S.A. [Modeling of regional stability and regional development]. Izvestiya Kabardino-Balkarskogo nauchnogo tsentra RAN, 2014, no. 5, pp. 118–125. (In Russ.)
  3. Zoidov K.H., Yankauskas K.S., Pirogov N.L. [Simulation of the system of fiscal relations in Russia in the framework of the development and expansion of the EAEU in conditions of instability. Part II]. Nauchnoe obozrenie. Seriya 1: Ekonomika i pravo = Scientific Review. Series 1. Economics and Law, 2016, no. 6, pp. 81–100. (In Russ.)
  4. Malkina M.Yu. [Instability of Financial Return of Regional Economies and Its Determinants]. Prostranstvennaya ekonomika = Spatial Economics, 2018, no. 3, pp. 88–114. (In Russ.) URL: Link
  5. Fedorova E.A., Lukasevich I.Ya. [Forecasting financial crises with the help of economic indicators in the CIS countries]. Problemy prognozirovaniya = Problems of Forecasting, 2012, no. 2, pp. 112–122. URL: Link (In Russ.)
  6. Granitsa Yu.V. [Practical use of certain tools to assess the financial instability of the region's economy]. Regional'naya ekonomika: teoriya i praktika = Regional Economics: Theory and Practice, 2019, vol. 17, iss. 8, pp. 1540–1557. (In Russ.) URL: Link
  7. Granitsa Yu.V. [Application of the Koyck method to assess the impact of volatility of economic indicators on the financial instability of the region]. Rossiya: Tendentsii i perspektivy razvitiya. Ezhegodnik = Russia: Trends and Prospects. Yearbook, 2019, iss. 14, part 2, 968 p. (In Russ.)
  8. Sadykov R.M., Migunova Yu.V., Gavrikova A.V., Ishmuratova D.F. [Social development of the region in a volatile economic environment: The key aspects]. Regional'naya ekonomika: teoriya i praktika = Regional Economics: Theory and Practice, 2017, vol. 15, iss. 10, pp. 1906–1919. (In Russ.) URL: Link
  9. Gafarova E.A., Lakman I.A. [Econometric Modelling of Region's Municipalities Development with Account to Their Inhomogeneity (case study: Republic of Bashkortostan)]. Voprosy Statistiki, 2017, no. 4, pp. 54–63. URL: Link (In Russ.)
  10. Orlova I.V. [The identification and elimination of information redundancy metric data in packages R and GRETL]. Myagkie izmereniya i vychisleniya = Soft Measurement and Computing, 2018, no. 11, pp. 94–100. (In Russ.)
  11. Orlova I.V. [Approach to the solution of the multicollinearity problem at the analysis of the influence of the factors on the resulting variable in models of regression]. Fundamental'nye issledovaniya = Fundamental Research, 2018, no. 3, pp. 58–53. (In Russ.) URL: Link
  12. Orlova I.V. [The approach to solving the problem of multicollinearity by using the transformation of variables]. Fundamental'nye issledovaniya = Fundamental Research, 2019, no. 5, pp. 78–84. URL: Link (In Russ.)
  13. Moiseev N.A. [Comparative analysis of efficiency methods of elimination of multicollinearity]. Uchet i statistika = Accounting and Statistics, 2017, no. 2, pp. 62–73. URL: Link (In Russ.)
  14. Il'yasov B.G., Makarova E.A., Zakieva E.Sh., Gizdatullina E.S. [Analysing the Data on Incomes in the Regional Context by the Principal Component Method]. Ekonomika regiona = Economy of Region, 2019, vol. 15, iss. 2, pp. 601–617. (In Russ.) URL: Link
  15. Davydov A.R., Tregubova Yu.S. [Multidimensional Statistical Analysis of Sustainable Development of the Russian Federation Regions]. Nauka i biznes: puti razvitiya = Science and Business: Ways of Development, 2015, no. 8, pp. 92–96. (In Russ.)
  16. Mokeev V.V., Solomakho K.L. [On the use of a principal component method for the analysis of an enterprise activity]. Vestnik Yuzhno-Ural'skogo gosudarstvennogo universiteta. Ser.: Ekonomika i menedzhment = Bulletin of the South Ural State University. Series: Economics and Management, 2013, vol. 7, no. 3, pp. 41–46. URL: Link (In Russ.)
  17. Malkina M.Yu. [Dynamics and Determinants of Intra and Inter-Regional Income Differentiation of the Population of the Russian Federation]. Prostranstvennaya ekonomika = Spatial Economics, 2014, no. 3, pp. 44–66. (In Russ.) URL: Link
  18. Arzhenovskii S.V., Bakhteev A.V., Slobodyan A.S. [Logit models to assess the risk of fraudulent misstatements in financial statements of Russian banks]. Mezhdunarodnyi bukhgalterskii uchet = International Accounting, 2019, vol. 22, iss. 1, pp. 24–37. (In Russ.) URL: Link

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