Аннотация. В статье представлен обзор последних достижений нейронных сетей применительно к задаче прогнозирования инфляции. Показано, что во многих случаях точность прогнозов, полученных с помощью нейросетевых методов, оказывается выше точности прогнозов, полученных традиционными методами экономической науки. Поднимается вопрос о глубинном противоречии между традиционным эконометрическим инструментарием и нейронными сетями, так как первые проигрывают вторым по точности расчетов, а вторые по сравнению с первыми не имеют под собой никакой осмысленной теории. Вместе с тем авторы показывают, что указанное противоречие может быть снято путем объединения двух видов прогнозного инструментария. В развитие данного тезиса в статье предложена двухшаговая модель краткосрочного прогнозирования инфляции. Сущность авторского подхода состоит в построении малоразмерной (пятифакторной) эконометрической модели инфляции, которая обладает хорошими статистическими характеристиками и дает адекватное теоретическое объяснение моделируемому процессу, однако при этом не позволяет прогнозировать месячные темпы инфляции с высокой точностью. Авторами показано, что данная проблема является типичной для современной макроэкономики и представляет собой частное проявление так называемой фундаментальной проблемы атрибуции данных в макромоделях. В статье показано, что данная проблема не имеет решения в рамках традиционных макроэкономических моделей. В связи с этим для повышения точности прогнозов был использован своеобразный вычислительный фильтр в виде нейронной сети, обучение которой позволило для отобранных факторов инфляции провести калибровку расчетов и довести их качество до необходимого уровня. Показаны преимущества предложенной схемы последовательного сопряжения эконометрической модели и нейронной сети. Ключевые слова: инфляция; индекс потребительских цен; центральный банк; эконометрика; регрессионный анализ; нейронные сети.

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The creation of territories with a high concentration of innovative, scientific and technological potential is a global practice. The theoretical basis of these initiatives is mainly the concept of "innovation helices", involving close interaction of the state, business and higher education (academic) sectors, as well as civil society. In economically developed countries there is a trend towards the concentration of innovative potential. At the same time, there are states with longterm and permanent territorial leaders (USA, France, Russia), and states for which there is the emergence of new territorial entities, gradually acquiring the status of national "places of growth" (China, Germany). In some countries, the regions-innovative leaders were formed by the efforts of the private sector, in others-the instruments of state stimulation played the dominant role. Such initiatives in Russia are significantly inferior in efficiency to foreign analogues. To assess the validity of the regions, which are going to become scientific and educational centers, the article offers tools for diagnosing the innovative potential of territories. Firstly, the assessment of the ratio of domestic R&D costs to technological innovation costs allows determining the deficit or surplus in demand for R&D results. Secondly, the study of statistics of requests in the service "Yandex words", which was used to construct an Index of innovation interest of the population. As the analysis shows, all three subjects of the Russian Federation, proposed for the creation of scientific and educational centers, in general, have the necessary parameters for the implementation of this measure.

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In this paper, we study the factors, motivations and barriers for productivity growth in Russia. The data is based on a survey of 700 companies of Russian basic non-resource industries. We find inter- and intra-industry divergence of companies by labor productivity level and discuss the evidence for further divergence. Revealed are the factors of high labor productivity level, among which are scale of business, investments into fixed assets and human capital, application of modern digital technologies, export activity and training of employees. The growth of labor productivity is positively associated with firm size, investment activity, digitalization and R&D spending. There is no positive and significant impact of innovation activities on productivity level and its dynamics, which may be a result of low innovation intensity and time lags in effects of innovation activities on revenue. The evidence suggests that innovative firms with positive dynamics of innovation performance are followers of foreign competitors. We find that firms with the leading and lagging levels of labor productivity have different strategies for human capital accumulation. Leading firms combine significant staff turnover with intensive professional development of existing staff, while lagging in productivity firms are not involved in staff turnover and investment in training. While leading in productivity firms compete for the best personnel, lagging firms compete for financial resources. In addition, leading companies find among the highest the risks that qualified personnel would be diverted, while the lagging companies find among the highest the risks of employees’ low motivation. Most of the leading in productivity firms are interested in continuous improvements of labor productivity, while among lagging in productivity firms this problem is important only for one fourth of them. Lack of internal motivation to improve their productivity may reflect failures in the corporate governance system. At the same time, the established model of relations with the state has a significant impact on the respective motivations of companies.

International experience has shown that close cooperation with the national scientific diaspora is crucial to strengthening the scientific and technical potential of a society. A review of academic papers revealed specific features of the global movement of scientific personnel, which causes, among other things, mutually beneficial cooperation of emigrant scientists and the donor country. For example, China, India, Korea, Taiwan, and Iran have instituted successful policies with regard to national scientific diasporas. However, the literature reveals that in Russia measures in this direction instituted by the state are inconsistent. In this study, the authors formulate proposals that should contribute to the expansion of interaction with the Russian scientific diaspora.