BSU Bulletin. Mathematics, Informatics
Bibliographic description:
, CRIME MODELING AND FORECASTING THE NUMBER OF CRIMES IN THE CONSTITUENT ENTITIES OF THE RUSSIAN FEDERATION // BSU Bulletin. Mathematics, Informatics. - 2020. №2. . - С. 36-51.
Title:
CRIME MODELING AND FORECASTING THE NUMBER OF CRIMES IN THE CONSTITUENT ENTITIES OF THE RUSSIAN FEDERATION
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The article presents the work-out of a comprehensive econometric research of crime situation in the Russian Federation based on regression analysis. As an
object of research we have used empirical data from the Federal State Statistics Service
for 2018 to identify the factors that have a significant impact on the number of heavy
and especially grave crimes committed in Russia. When establishing correlation between variables, we have built a "fan" of six econometric models of multiple regression. To select the best model, we have carried out Box-Cox and Zarembka tests, which
made it possible to extract a linear regression model.
A complete econometric study of the problem under consideration also included the
analysis of multicollinearity of factors and the study of the heteroscedasticity of the
residuals of the linear regression model. Verification of the heterogeneity of observations in the model, which in the course of the study turned out to be the best of all the
models considered, was carried out using the White, Breusch-Pagan, Goldfeld-Quandt,
Park, and Glazer tests. For all performed tests the hypothesis of homoscedasticity of the
residues was rejected.
Since, as a result of the study preference was given to a linear regression model, it
was on the basis of this model that point and interval forecasts were built. Quantitative
relationships of the studied variables have been established
object of research we have used empirical data from the Federal State Statistics Service
for 2018 to identify the factors that have a significant impact on the number of heavy
and especially grave crimes committed in Russia. When establishing correlation between variables, we have built a "fan" of six econometric models of multiple regression. To select the best model, we have carried out Box-Cox and Zarembka tests, which
made it possible to extract a linear regression model.
A complete econometric study of the problem under consideration also included the
analysis of multicollinearity of factors and the study of the heteroscedasticity of the
residuals of the linear regression model. Verification of the heterogeneity of observations in the model, which in the course of the study turned out to be the best of all the
models considered, was carried out using the White, Breusch-Pagan, Goldfeld-Quandt,
Park, and Glazer tests. For all performed tests the hypothesis of homoscedasticity of the
residues was rejected.
Since, as a result of the study preference was given to a linear regression model, it
was on the basis of this model that point and interval forecasts were built. Quantitative
relationships of the studied variables have been established
Keywords:
econometric research; multicollinearity of factors; heteroscedasticity of residuals; regression model; elasticity coefficients; forecasting.
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10. Molokov V. V., Rudakova E. N. Issledovanie statisticheskikh vzaimosvyazei pokazatelei prestupnosti kak faktora kriminalizatsii regiona [Investigation of Statistical Evidence of Crime Indicators as a Factor in the Criminalization of the Region]. Vestnik Sibirskogo yuridicheskogo instituta MVD Rossii. 2018. No. 1(30). Pp. 61–68.
11. Mustafina S. Yu. Vliyanie sotsialno-ekonomicheskikh faktorov na uroven prestupnosti: statisticheskoe issledovanie [Impact of Socio-Economic Factors on the Crime Rate: A Statistical Study]. Ekonomika innovatsionnogo razvitiya: teoriya i praktika. 2018. No. 3(19). Pp. 41–46.
12. Mkhitaryan V. S. Ekonometrika [Econometrics]. Moscow: Prospekt Publ., 2008. 380 p.
13. Rogacheva O. A. Migratsionnye protsessy v regionakh Sibiri i Dalnego Vostoka [Migration Processes in the Regions of Siberia and the Far East]. Global and Regional Research. 2019. V. 1, no. 3. Pp. 256–263.
14. Trofimov S. E. Ekonometricheskoe modelirovanie dinamicheskogo vremennogo ryada tseny na neft [Econometric Modeling of the Dynamic Time Series of Oil Prices]. Izvestiya Irkutskoi gosudarstvennoi ekonomicheskoi akademii. 2015. V. 25, no. 6. Pp. 990–998.
2. Baldynova E. V., Malyutina S. A. Sravnitelnyi analiz srednemesyachnoi realnoi zarabotnoi platy rabotnikov organizatsii v razreze vidov ekonomicheskoi deyatelnosti po Irkutskoi oblasti [Comparative Analysis of the Average Monthly Real Wages of Corporate Employees in Terms of Types of Economic Activity in Irkutsk Oblast]. Izvestiya Baikalskogo gosudarstvennogo universiteta. 2018. V. 28, no. 3. Pp. 409–418.
3. Valentinov V. A. Ekonometrika [Econometrics]. Moscow: Dashkov i K° Publ., 2009. 436 p.
4. Volchenko L. Yu., Mamonova N. V., Zavyalova E. O. Modelirovanie vliyaniya deyatelnosti tamozhennykh organov na sotsialno-ekonomicheskoe razvitie i investitsionnuyu aktivnost regionov [Modeling the Impact of Customs Authorities’ Work on Socio-Economic Development and Investment Activity in the Regions]. Innovatsionnoe razvitie ekonomiki. 2017. No. 6(42). Pp. 16–26.
5. Draper N. R., Smith H. Applied Regression Analysis. 3 rd ed. USA: Wiley, 1998. 698 p.
6. Eliseeva I. I. Ekonometrika [Econometrics]. Moscow: Yurait Publ., 2012. 449 p.
7. Kucherova S. V. Ispolzovanie analiza vremennykh ryadov pri issledovanii urovnya prestupnosti [Application of Time Series Analysis in the Study of Crime Rate]. Fundamentalnye issledovaniya. Ekonomicheskie nauki. 2015. No. 11. Pp. 1206–1209.
8. Latov Yu. V. Ekonomicheskie determinanty prestupnosti v zarubezhnykh stranakh (obzor kriminometricheskikh issledovanii) [Economic Determinants of Crime in Foreign Countries (review of criminometric studies)]. Journal of Institutional Studies. 2011. V. 3, No. 1. Pp. 133–149.
9. Mamonova N. V., Gavrilova E. A. Analiz narusheniya garantii nezavisimosti advokatov sotrudnikami pravookhranitelnykh organov pri zashchite lichnosti v ugolovnom sudoproizvodstve [Analysis of Violations of the Guarantees of Independence of Lawyers by Law Enforcement Officials in the Protection of Personality in Criminal Proceedings]. Advokatskaya praktika. 2019. No. 2. Pp. 45–51.
10. Molokov V. V., Rudakova E. N. Issledovanie statisticheskikh vzaimosvyazei pokazatelei prestupnosti kak faktora kriminalizatsii regiona [Investigation of Statistical Evidence of Crime Indicators as a Factor in the Criminalization of the Region]. Vestnik Sibirskogo yuridicheskogo instituta MVD Rossii. 2018. No. 1(30). Pp. 61–68.
11. Mustafina S. Yu. Vliyanie sotsialno-ekonomicheskikh faktorov na uroven prestupnosti: statisticheskoe issledovanie [Impact of Socio-Economic Factors on the Crime Rate: A Statistical Study]. Ekonomika innovatsionnogo razvitiya: teoriya i praktika. 2018. No. 3(19). Pp. 41–46.
12. Mkhitaryan V. S. Ekonometrika [Econometrics]. Moscow: Prospekt Publ., 2008. 380 p.
13. Rogacheva O. A. Migratsionnye protsessy v regionakh Sibiri i Dalnego Vostoka [Migration Processes in the Regions of Siberia and the Far East]. Global and Regional Research. 2019. V. 1, no. 3. Pp. 256–263.
14. Trofimov S. E. Ekonometricheskoe modelirovanie dinamicheskogo vremennogo ryada tseny na neft [Econometric Modeling of the Dynamic Time Series of Oil Prices]. Izvestiya Irkutskoi gosudarstvennoi ekonomicheskoi akademii. 2015. V. 25, no. 6. Pp. 990–998.
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