The aim of the research was 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, and analytical approaches to modeling of contemporary economic processes in separate branches of Russian economy.
There were made some significant improvements during this year. Some sectorial models of Russian economy have been modified. Declining growth rate of the global economy 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 modelling of heterogeneous Russian banking system as a whole, revealing learning-by-exporting effect in Russian manufacturing industries, risk evaluation at financial markets and testing of the hypothesis of market efficiency, modeling of influence of foreign direct investments on technological efficiency in Russian agricultural industry, modelling of dynamics of interregional migration in Russia.
The main scientific results are the following.
This year analysis shows that Russian small credit organizations do not suffer from the “poverty trap”. They have enough chances to move into the group of medium credit organizations. There was estimated that the main financial indicators of the Russian banks are distributed with Pareto IV distribution and AEP distribution. Some banks can “move” along the distribution but the structure of the distribution remains stable.
There was developed a new procedure of multiplicative de-seasoning of dynamic series. It is invariant to deflating and it turned out to be as much accurate as the popular de-seasoning procedure X12, at least in cases when there is no evolution of a “seasonal wave”. The Monte-Carlo simulation showed that in presence of both noise and structural breaks the multiplicative procedure approximates a dynamic series as much accurate as procedure X12. At the same time, this new procedure gives less bias under unit-root testing.
We used the SFA approach for analysis of productivity on firm level for Russian manufacturing industries. This year analysis revealed the absence of so-called learning-by-exporting effect in these industries. Both regression analysis and propensity score matching supported this conclusion.
The analysis of FDI in Russian food firms revealed the negative influence of regional agricultural output and regional land under cultivation on propensity of FDI. The positive influence of agricultural firms’ profitability on propensity of FDI revealed only in few cases.
The model of interregional migration in Russia in the framework of new economic geography (NEG) was developed. This model includes an additional sector, namely, the exporting of natural resources. The fix-effect model showed that measures of reginal politics could not change the “western drift” in Russia.
This year research examines "fat tails puzzle" at financial markets. Ignoring the rate of convergence in Central Limit Theorem (CLT) provides the "fat tail" uncertainty. We implemented to the market indices of 24 countries the innovative method of Yuri Gabovich (G-bounds approach) based on the rate of convergence in CLT to the normal distribution. The G-bounds evaluate risk at financial markets more carefully than models based on Gaussian distributions. We tested this statement for the 24 financial markets exploring their stock indexes. Besides this have tested Weak-Form Market Efficiency for the investigated markets. As a result, we found out the negative correlation between the weak effectiveness of a stock market and the thickness of the left tail of the profitability density function. Therefore the closer the risk of losses on the stock market to the corresponding risk of loss for a normal distribution, the higher probability that the market is weak form effective. For non - effective markets, the probability of large losses is much higher than for weak effective ones.
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/
- Duchene Gerard, Université Paris-Est Créteil Val-de-Marne, http://www.en.u-pec.fr/
- Sasha Sardavar, Wienna University of Economics and Business,http://www.wu.ac.at/wgi/institut/team/forscher/sardadvar