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Quantitative examination of the relation between unemployment and gross domestic product as exemplified by the russian federation

Authors: Vashlaev A.D., Kochkin I.A., Sadovskiy G.L.
Published in issue: #3(20)/2018
DOI: 10.18698/2541-8009-2018-3-280


Category: Economics and Production Organization

Keywords: unemployment, gross domestic product, model of regression, MATLAB, MathWorks, selective forecasting method, correlation, macroeconomic indices, socioeconomic development, demography
Published: 19.03.2018

The article analyzes the relation between unemployment and gross domestic product of the Russian Federation in the period from 2000 to 2016 using the linear model of regression constructed in the MathWorks, MATLAB Software R2017b (version 9.3) environment with the application of the tool StatisticsToolbox R2017b (version 11.2). We provide a rationale for the obtained indexes such as the employed population, the unemployment as a percentage of the working-age population and the number of unemployed persons in Russia. The authors undertake the review of the pace of Russia’s socioeconomic development in 2017 and also make a forecast of gross domestic product by means of the selective forecasting method.


References

[1] Gaydar E.T., ed. Ekonomika perekhodnogo perioda. Ocherki ekonomicheskoy politiki postkommunisticheskoy Rossii 1991-1997 [Interregnum economy. Economic policy sketches of post-communist Russia in 1991-1997]. Moscow, IEPP publ., 1998, 1114 p.

[2] Efimova L.A. Employment and unemployment in Russia. Regional’naya ekonomika: teoriya i praktika [Economic Analysis: Theory and Practice], 2015, no. 20(395), pp. 14–22.

[3] Zanyatost’ i bezrabotitsa [Employment and unemployment]. Available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/wages/labour_force/# (accessed 15 February 2018).

[4] Analyze and model data using statistics and machine learning. Available at: https://www.mathworks.com/products/statistics.html (accessed 15 February 2018).

[5] Metodika rascheta pokazatelya "Obshchaya chislennost’ bezrabotnykh, v protsentakh k ekonomicheski aktivnomu naseleniyu" [Calculation method of the “total number of the unemployed in percentage of active population” factor]. Available at: http://www.gks.ru/metod/met-pril/met-pr4.doc (accessed 15 February 2018).

[6] Blinov S. Prichiny padeniya i vozmozhnosti rosta [Causes of the fall and opportunities for growth]. Available at: http://expert.ru/2013/12/24/prichinyi-padeniya-i-vozmozhnosti-rosta/ (accessed 15 February 2018).

[7] Drogovoz P.A., Gorbachev A.S., Kutuzova A.A. An econometric assessment of the impact of non-oil factors on the formation of the federal budget of Russia. Audit i finansovyy analiz, 2017, no. 1, pp. 74–85.

[8] Tsvetkov V.A., Anosova L.A., Zoidov K.Kh., Bol’shakov A.V. Monitoring ekonomicheskogo razvitiya Rossii v period s 1991 po 2010 gg.: opyt tsiklicheskogo analiza makroekonomicheskoy dinamiki [Monitoring of the Russian economic development from 1991 to 2010: experience of periodic analysis of macroeconomic dynamics]. Annual Report on BRICS’ Social-Economic Development. Social Sciences Academic Press, 2011, 39 p.

[9] V demografii Rossii nemalo podvodnykh kamney [There are quite a number of milestones in Russian demography]. Available at: https://iq.hse.ru/news/177665348.html (accessed 15 February 2018).

[10] Karp D.B. Ekonometrika: osnovnye formuly s kommentariyami [Econometrics: main expressions with comments]. Vladivostok, DVGAEU publ., 2004, 50 p.

[11] Shanchenko N.I. Ekonometrika: laboratornyy praktikum [Econometrics: laboratory practicum]. Ul’yanovsk, UlSTU publ., 2011, 117 p.

[12] Hoffmann J.P. Linear regression analysis: assumptions and applications. Available at: https://www.researchgate.net/publication/228896673_Linear_Regression_Analysis_Assumptions_and_Applications (accessed 15 February 2018).

[13] Draper N.R., Smith H. Applied Regression Analysis. Wiley, 1998, 736 p. (Russ. ed.: Prikladnoy regressionnyy analiz. Kiev, Dialektika-Vil’yams publ., 2007, 912 p.)