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Taking into account heterogeneity of Russian economy’s branches under modelling of their dynamics

Priority areas of development: economics
2016
Department: Laboratory for Macro-Structural Modeling of the Russian Economy
The project has been carried out as part of the HSE Program of Fundamental Studies.

Objects of research are Russian economy’s specific sectors.

The aim of the research is development of new models in the framework of the system of models elaborated by the staff of the laboratory for analysis and forecasting of Russian economy and its separate sectors.  These new models should correspond to changing economic environment of Russia. The models should be ready for both fundamental and applied researches running by researchers, students and PhD-students of NRU HSE.

Methodology of research combines econometric, general equilibrium, machine learning and analytical approaches to modeling of contemporary economic processes in specific branches of Russian economy.

There were made some significant improvements during the current year. Both some sectorial models of Russian economy and sectorial indicators have been modified. Declining of oil prices and recession of Russian economy forced to take into account new economic reality and to check robustness of the previously developed models under new circumstances. There were developed some solutions to arisen problems including. Among them: development of the model of the Russian banking system; modification of the OLS approach for improvement of forecast power of a dynamic model; elaboration of Liquidity-Risk indicator for banking system; development of the aggregating consumer model; revealing main factors influencing on direct investments dynamics; development of the Bank Financial Stability model; risk evaluation at Russian financial market; modeling of joint influence of foreign direct investments and output in Russian food industry on regional level, modelling of dynamics of interregional migration in Russia based on gravitational model with space lags.

The main scientific results are the following.

There was developed a modified OLS procedure for long run forecasting. The classical problem of minimization of sum of one-step forecast errors squares is modified by the problem of minimization of sum of many-step forecast errors squares. The Monte-Carlo simulation showed that the modified procedure is preferable in presence of non-linear component. The same conclusion is preliminary valid for real economic time-series.

There was elaborated a new Liquidity-Risk indicator for evaluation of liquidity sufficiency in Russian Bank System. The proposed indicator based on formal division of bank accounts on liquid and non-liquid ones. There was revealed that the indicator was a leading one for crises of both 2008 and 2014/ The indicator was less than 10% of the historical minimum, that is it indicates in half a year before both Lehman Brothers crisis and Russian ruble fall.

There was developed the model a bank financial stability in context of Bank of Russia license withdraw practice. The methodology is based on LASSO-regularization and cross-cvalidation. The model predicts a license withdraw for a specific Russian bank. The model can be applied for monitoring og Russian bank system.

This year research continues examination of "fat tails puzzle" at Russian financial market. We implemented to Russian financial indices the method of Yuri Gabovich (G-bounds approach) based on the rate of convergence in CLT to the normal distribution. It was shown that the G-bounds evaluate risk at financial markets more carefully than models based on Var and GARCH approaches.

The analysis of FDI in Russian food firms revealed misbalances of regional foreign capital inflow and investment demand. The developed model showed the inertia of FDI in Russian food industry. It ranked also Russian regions with respect of FDI demand. Top-5 Russian regions FDI demand are Moscow, Moscow oblast, Vladimir oblast, Sankt-Petersberg,  posiKrasnodar kray.

The gravitation model of interregional migration in Russia with exogenous factors space lags was developed. The model showed that neighbor regions’ factors are important, both in-flow and out-flow regions. The scheme of influence is different for different explained variables. Any case it turned out that the region-neighbors are important for inner migrants into a specific region.

The foreign partners were

Maurel Mathilde, Centre d'économie de la Sorbonne
Maison des Sciences Economiques,http://centredeconomiesorbonne.univ-paris1.fr/bandeau-haut/membres/m-chercheurs/

Sasha Sardavar, Wienna University of Economics and Business,http://www.wu.ac.at/wgi/institut/team/forscher/sardadvar

Publications:


Пильник Н. П., Корищенко К. Н. Оценка вклада основных факторов в динамику потребительских цен // Глобальные рынки и финансовый инжиниринг. 2016. Т. 3. № 1. С. 67-82. doi
Sardadvar S., Vakulenko E. Interregional migration within Russia and east-west divide: evidence from spatial panel regressions // Review of Urban and Regional Development Studies. 2016. Vol. 28. No. 2. P. 123-141. doi
Vakulenko E. Econometric analysis of factors of internal migration in Russia / Пер. с рус. // Regional Research of Russia. 2016. Vol. 6. No. 4. P. 344-356. doi
Ачкасов Ю. К., Пильник Н. П. Оптимальный механизм аудита налога на прибыль и эндогенный выбор между рынком труда и самозанятостью // В кн.: XVI Апрельская международная научная конференция по проблемам развития экономики и общества: в 4 кн. / Отв. ред.: Е. Г. Ясин. Кн. 1. М. : Издательский дом НИУ ВШЭ, 2016. С. 467-476.
Малахов Д. И. Риски структурных изменений в отрасли и их последствия. // Риск-менеджмент в кредитной организации. 2015. № 4(20). С. 72-88.
Malakhov D., Pilnik N., Pospelov I. G. Stability of Distribution of Relative Sizes of Banks as an Argument for the Use of the Representative Agent Concept. / NRU Higher School of Economics. Series EC "Economics". 2015. No. WP BRP 116/EC/2015.
Гладышева А. А., Ратникова Т. А. Анализ прямых иностранных инвестиций в пищевую промышленность российских регионов // В кн.: Многомерный статистический анализ и эконометрика. /Труды IX-й Международной школы-семинара. /Под ред. С.А. Айвазяна. /Цахкадзор, 2016 г. – М.: ЦЭМИ РАН, 201 / Под общ. ред.: С. А. Айвазян. М. : ЦЭМИ РАН, 2016. С. 77-78.