Software implementation of~ensemble models for the analysis of~regional socio-economic development indicators
Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 22 (2022) no. 1, pp. 130-137.

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To predict indicators, modern approaches based on machine learning are increasingly being used, as a result, additional tools appear for quantitatively assessing the level of development of socio-economic systems. One of the relevant approaches in machine learning is the use of ensemble methods. The purpose of this study is to develop an approach for processing panel data using special regression models, in particular, the ensembles. An application is presented to implement and compare various regression models, including GPBoost, for panel data used in regional statistics. The application was tested on the example of assessing the innovative potential of Russian regions.
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G. Yu. Chernyshova; N. D. Rasskazkin. Software implementation of~ensemble models for the analysis of~regional socio-economic development indicators. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 22 (2022) no. 1, pp. 130-137. http://geodesic.mathdoc.fr/item/ISU_2022_22_1_a9/

[1] Aivazian S. A., Methods of Econometrics, INFRA-M, M., 2019, 512 pp. (in Russian)

[2] Greene W. H., Econometric Analysis, 5$^{\rm th}$ ed., Prentice Hall, Upper Saddle River, NJ, 2003, 1026 pp.

[3] Hajjem A., Bellavance F., Larocque D., “Mixed-effects random forest for clustered data”, Journal of Statistical Computation and Simulation, 84:6 (2014), 1313–1328 | DOI | MR | Zbl

[4] Ke G., Meng Q., Finley T., Wang T., Chen W., Ma W., Liu T. Y., “LightGBM: A highly efficient gradient boosting decision tree”, Advances in neural information processing system, 30 (2017), 3146–3154

[5] Firsova A., Chernyshova G., “Efficiency analysis of regional innovation development based on DEA Malmquist index”, Information, 11:6 (2020), 294 | DOI

[6] Veshneva I., Chernyshova G., “The scenario modeling of regional competitiveness risks based on the Chapman-Kolmogorov equations”, Journal of Physics: Conference Series (JPCS), 1784:1 (2021), 012008 | DOI

[7] Gurka M. J., Kelley G. A., Edwards L. J., “Fixed and random effects models”, Wiley Interdisciplinary Reviews: Computational Statistics, 4:2 (2011), 181–190 | DOI

[8] Breiman L., Friedman J. H., Stone C. J., Olshen R. A., Classication and Regression Trees, 1$^{\rm st}$ ed., CRC Press, New York, 1984, 368 pp. | DOI | MR

[9] Laird N. M., Ware J. H., “Random-effects models for longitudinal data”, Biometrics, 38 (1982), 963–974 | DOI | Zbl

[10] Pinheiro J., Bates D., Mixed-Effects Models in S and S-PLUS, Springer Science Business Media, 2006, 528 pp.

[11] Rasmussen C. E., Williams C. K. J., Gaussian Processes for Machine Learning, The MIT Press, 2006, 266 pp. | MR

[12] Sigrist F., Gaussian Process Boosting, 2020, arXiv: 2004.02653 | Zbl

[13] Baltagi B. H., Econometric Analysis of Panel Data, 6$^{\rm th}$ ed., John Wiley Sons, Chichester, 2021, 436 pp.

[14] Information Analysis System FIRA PRO, (accessed 15 September 2021) https://pro.fira.ru