Subject. The article provides the results of meso-dynamics analysis of main twelve industries, based on monthly data for 82 Russian regions, from January 2005 till December 2020. Objectives. The purpose of the study is to address the problem of balanced and stable spatial development of Russia’s regions and Russia, which requires modeling of adequate tools and forecasting nonlinear mesodynamics. Methods. The study follows the econophysics methodology. Results. We consider additive and multiplicative interactions of regular time series components between each other and the residuals, thus expanding the scope of tools application for the variety of considered industries and their models. Using the common and new trend models, we analyze structural changes, introduce the topological measure of proximity to the neighborhood of residuals with heavy-tailed distribution, which is estimated by median values of trends and cycles for regular components. The traditional time series decomposition (by trend, cycle, seasonality, and residual) is supplemented by our unique complex of wavelet transformations, which forms the models of cycles, using auto regression. We obtained representative and time-synchronized analytical estimates of regular components of industries’ dynamics for meso- and macro-indicators of the Russian economy that have higher accuracy than the known results for the accuracy of modeling and forecasting. Conclusions. The offered methodology and tools enable a more adequate analysis of non-linear dynamics of regions’ middle-term development. They help shift to growth point identification, create the atlas of economic industrial cycles, analyze stages of bifurcations and scenario predictive planning.
Keywords: meso-dynamics, econophysics, neighborhood of residuals with heavy-tailed distribution, wavelet transformation
References:
Semenychev V.K., Khmeleva G.A., Korobetskaya A.A. [Proposed methodology and tools of econophysics to analyze mesodynamics of economic sectors in Russian regions]. Ekonomicheskii analiz: teoriya i praktika = Economic Analysis: Theory and Practice, 2020, vol. 19, iss. 7, pp. 1192–1217. (In Russ.) URL: Link
Khmeleva G.A., Semenychev V.K., Korobetskaya A.A. et al. Rossiiskie regiony v usloviyakh sanktsii: vozmozhnosti operezhayushchego razvitiya ekonomiki na osnove innovatsii [Russian regions under conditions of sanctions: The possibility of priority development of the economy based on innovation]. Samara, Samara State University of Economics Publ., 2019, 446 p.
Kalyadin V.L. [Distributions with infinite dispersion and insufficiency of the classical statistics]. Radioelektronika i informatika = Radioelectronics and Informatics, 2002, no. 2, pp. 4–11. URL: Link (In Russ.)
Andrews D.F., Bickel P.J., Hampel F.R. et al. Robust Estimates of Location: Survey and Advances. Princeton, N.J., Princeton University Press, 1972, 374 p.
Muller D.W., Sawitzki G. Excess Mass Estimates and Tests for Multimodality. Journal of the American Statistical Association, 1991, vol. 86, iss. 415, pp. 738–746. URL: Link
Friedman J., Fisher N. Bump Hunting in High-Dimensional Data. Statistics and Computing, 1999, vol. 9, pp. 123–143. URL: Link
Hall P., Minnotte M.C., Zhang C. Bump Hunting with Non-Gaussian Kernels. Annals of Statistics, 2004, vol. 32, pp. 2124–2141. URL: Link
Andrews D.F., Bickel P.J., Hampel F.R. et al. Robust Estimates of Location: Survey and Advances. Princeton, N.J., Princeton University Press, 1972, 374 p.
Yeo I.-K., Johnson R.A. A new family of power transformations to improve normality or symmetry. Biometrika, 2000, vol. 87, iss. 4, pp. 954–959. URL: Link
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
Breusch T.S., Pagan A.R. A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica, 1979, vol. 47, iss. 5, pp. 1287–1294. URL: Link47:5<1287:ASTFHA>2.0.CO;2-9
Bezruchko B.P., Smirnov D.A. Matematicheskoe modelirovanie i khaoticheskie vremennye ryady [Mathematical modeling and chaotic time series]. Saratov, Kolledzh Publ., 2005, 320 p.
Semenychev V.K., Kurkin E.I., Semenychev E.V., Danilova A.A. Multimodel forecasting of non-renewable resources production. Energy, 2017, vol. 130, pp. 448–460. URL: Link
Bai J., Perron P. Computation and Analysis of Multiple Structural Change Models. Journal of Applied Econometrics, 2003, vol. 18, iss. 1, pp. 1–22. URL: Link
Zeileis A., Kleiber C., Krämer W., Hornik K. Testing and Dating of Structural Changes in Practice. Computational Statistics and Data Analysis, 2003, vol. 44, pp. 109–123. URL: Link
Yang Xiang, Gubian S., Suomela B., Hoeng J. Generalized Simulated Annealing for Efficient Global Optimization: The GenSA Package. The R Journal, 2013, vol. 5, iss. 1. URL: Link
Igel C., Hüsken M. Empirical evaluation of the improved Rprop learning algorithms. Neurocomputing, 2003, vol. 50, pp. 105–123. URL: Link00700-7
Riedmiller M. Advanced supervised learning in multilayer perceptrons – From backpropagation to adaptive learning algorithms. Computer Standards and Interfaces, 1994, vol. 16, iss. 3, pp. 265–278. URL: Link90017-5
Schnabel R.B., Koonatz J.E., Weiss B.E. A modular system of algorithms for unconstrained minimization. ACM Transactions on Mathematical Software, 1985, vol. 11, iss. 4, pp. 419–440. URL: Link
Slutskii E.E. [The summation of random causes as the source of cyclic processes]. Voprosy kon"yunktury, 1927, vol. 3, iss. 1, pp. 34‒64. (In Russ.)
Percival D.B., Walden A.T. Wavelet Methods for Time Series Analysis. Cambridge, Cambridge University Press, 2000. URL: Link
Hyndman R.J., Khandakar Y. Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 2008, vol. 27, iss. 3. URL: Link
Semenychev V.K. Identifikatsiya ekonomicheskoi dinamiki na osnove modelei avtoregressii [Identification of economic dynamics based on autoregression models]. Samara, Samara Scientific Center of Russian Academy of Sciences Publ., 2004, 243 p.