Технология Высокопроизводительных Вычислений В Исследовании Влияния Сектора Биотехнологий На Макропоказатели Развития Экономики Кировской Области[High Performance Computing in Research of Biotechnology Sector Impact on Macroindexes of Efficiency and Development for Kirov Region Economy]

A normative balance mathematical model for regional economy contains a lot of unspecified parameters which are not defined directly by the data of economic statistics. Only confidence intervals for the unknown parameters can be computed from the statistical data. A method for estimation of the model parameters by application of parallel computations on multi-processors systems are presented here. They determine the unknown parameters of economic model by indirect way, comparing time series for macro indexes calculated by model with statistical time series for these indexes. The use of the method is illustrated by the parameter estimation of a macroeconomic model of Kirov Region of Russia for 2000-2006. Production sectors in the regional economic model are presented by three sectors: (X) the timber industry complex including forestry; (Y) a complex of new innovation industries in biotechnology and the chemistry, including science and education; (Z) combination of the remained industries. The each production sector shadow money stock grows due to sale of shadow final product to households and as intermediate product to other sectors. The simulation model of regional economy enables to receive a quantitative estimation of dynamics of macroindexes for regional economy. Calibrated model is used for estimation of the Regional Government economic politics and for research of bio-technology sector impact on Kirov Region economy. The model constructed here is an innovative product and the experience received on its parameter estimation is those "know-how" which can be used in the adaptation of the given model for concrete regional economic systems. The work is in part supported by the Russian Humanitarian Scientific Foundation (Grant 06-02-91821); by the Russian Foundation of Basic Research (Grant 08-01-00377); by the Program of State Support of Leading Scientific Schools (Grant SS- 2982.2008.1); and by the Program of Basic Research no.15 of Presidium of the Russian Academy of Sciences.