Home  Journals  Economic Analysis: Theory and Practice  3(510) - 2021 March

Identification of seasonal fluctuations in the nonlinear dynamics of the number of registered crimes

Received: 4 December 2020

Received in revised form: 14 January 2021

Accepted: 12 February 2021

Available online: 30 March 2021

Subject Heading: MATHEMATICAL METHODS AND MODELS

JEL Classification: C22, C51

Pages: 554–576

https://doi.org/10.24891/ea.20.3.554

Subject. The article considers trends in different types of crimes committed in the Russian Federation from 2012 to 2019.
Objectives. The purpose is to determine trends and the presence or absence of annual seasonality in the analyzed dynamics.
Methods. The study draws on parametric modeling of trend-seasonal dynamics, using our own procedures, a set of models and methods for their identification by means of generalized ARMA models, the STL (Seasonal Transformation using LOESS) method, the Yeo-Johnson method based on standard libraries and applications of the R programming language.
Results. The paper offers two methods to model seasonality: a "rough" assessment of its presence and a "fine" assessment, with obtaining quantitative estimates of model parameters and estimates of qualitative characteristics of modeling. We determine optimal smoothing settings to solve the problem of trend-seasonal modeling of crime dynamics, analyze the dynamics of eleven types of registered crimes, and identify the parameters of seasonal component for each of them.
Conclusions. In nine out of eleven types of considered crimes, there is a pronounced annual seasonality, which is advisable to take into account, when organizing and planning the law enforcement activities.

Keywords: crime, trend-seasonal decomposition, seasonal, fluctuation

References:

1. Demidov V.V., Semenychev E.V. Modelirovanie sezonnykh kolebanii v ekonomike [Modeling of seasonal fluctuations in the economy]. Samara, Samara Academy of State and Municipal Administration Publ., 2012, 82 p.
2. Semenychev V.K., Semenychev E.V. Parametricheskaya identifikatsiya ryadov dinamiki: struktury, modeli, evolyutsiya: monografiya [Parametric identification of dynamics series: Structures, models, evolution: a monograph]. Samara, Samara Scientific Center of RAS Publ., 2011, 364 p.
3. Semenychev E.V., Demidov V.V. [Frequency Decomposition Method for Seasonal Economic Dynamics Modeling]. Vestnik Samarskogo munitsipal'nogo instituta upravleniya = Bulletin of Samara Municipal Institute of Management, 2014, no. 1, pp. 79–83. (In Russ.)
4. Svetun'kov I.S., Svetun'kov S.G. Metody sotsial'no-ekonomicheskogo prognozirovaniya. T. 2 [Methods of socio-economic forecasting. Vol. 2]. Moscow, Yurait Publ., 2020, 447 p.
5. Cleveland R.B., Cleveland W.S., McRae J.E., Terpenning I. STL: A Seasonal-Trend Decomposition Procedure Based on Loess. Journal of Official Statistics, 1990, vol. 6, iss. 1, pp. 3–73. URL: Link
6. Cleveland W.S., Grosse E., Shyu W.M. Local Regression Models. In: J. Chambers, T. Hastie (Eds), Statistical Models in S. Pacific Grove, Calif., Wadsworth and Brooks/Cole Advanced Books & Software, 1992, pp. 309–376.
7. Cleveland W.S. Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 1979, vol. 74, iss. 368, pp.829–836. URL: Link
8. Box G.E.P., Cox D.R. An Analysis of Transformations. Journal of the Royal Statistical Society. Series B (Methodological), 1964, vol. 26, iss. 2, pp. 211–252. URL: Link
9. Yeo I.-K., Johnson R. A New Family of Power Transformations to Improve Normality or Symmetry. Biometrika, 2000, vol. 87, iss. 4, pp. 954–959. URL: Link

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