For the Russian economy, the previous fifteen years have been a peculiar period of ongoing experimentation in fiscal regulation. Every year, the country's fiscal system was changed both qualitatively and quantitatively: tax rates, tax accounting and bookkeeping systems, sets of fiscal instruments and taxpayer requirements. The stated purpose of reforms was to normalize economic relations and to boost economic growth. In practice, however, the overwhelming majority of fiscal innovations have either had no influence on economic trends or aggravated the domestic producer's situation. The resulting paradoxical situation calls for the development of an adequate analytical apparatus for explaining the mechanism of the above-mentioned economic anomalies.Basic analytical construction: an economic growth model. In order to study the influence of fiscal reforms on the national economy, let us use a simple business growth model, which comprises four relations:(1)where F is fixed assets; X is the volume of revenue (production output); I is investment in fixed capital (capital investment); π is net profit; ν is the rate of capital retirement; g is capital productivity ratio; m is the proportion of net profit reinvested in fixed assets; r is the share of net profit in revenue; t is the time (year) index. The coefficients ν , g , m , and r act as model parameters and are assumed to be time-constant.Relations (1)-(4) can easily be reduced to a homogeneous difference equation) X t , = whose solution is given by the following exponential function:where X 0 is corporate revenue at start time.If a production growth rate characteristic, λ = X t / X t -1 -1, is introduced, then the following relation follows from formula (6), which is fundamental for our further analysis:Formula (7) relates the rate of economic growth to key reproduction characteristics of the company.Let us consider an obvious balance:where Z is total fiscal costs, i.e., the costs related to fiscal payments; U is other corporate costs (with the exception of total fiscal costs.) If we pass to the relative values of z = Z / X , u = U / X , and r = π / X , relation (8) can be written as (9) Formula (9) gives the dependence of the rate of return on fiscal and other costs. Further on, the coefficients u , z , and r can be treated as parameters. Combining relations (7) and (9), we obtain the basic equation:Therefore, the rate of economic growth depends on the rate of capital retirement ( ν ) and the productivity ( g ) of fixed assets, the average propensity to invest ( m ), the level of specific production ( u ), and fiscal ( z ) costs. 1 The gist of equation (10) is the formalization of the following regularity: any increase of total fiscal costs is actually a decrease in the company's financial assets 1 Equation (10) can be supplemented with additional factors. For example, if we write as λ = -ν + sm σ (1 -u -z ), where s = X / M is the efficiency of production capacities M ; σ = M / F is the utilization rate of production capacities.). + = Abstract -A conceptual framewo...

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.

Цитирование: Балацкий Е.В. (2019) Измерения власти по С. Льюксу // Мир России. Т. 28. № 2. С. 172–187. DOI: 10.17323/1811-038X-2019-28-2-172-187 В 2010 году в России была издана на русском языке книга Стивена Льюкса «Власть: Радикальный взгляд». Хотя в международном политологическом дискурсе эта монография давно стала классической, в России ее идеи до сих пор не получили широкого распространения. В связи с этим в статье сделана попытка не только дать краткий дайджест идей американского ученого, но и рассмотреть ряд современных примеров, которые могут быть плодотворно проинтерпретированы в терминах концепции С. Льюкса, и осмыслить некоторые следствия усиления феномена власти в информационном обществе, где возникают широкие возможности для манипулирования общественным мнением. Для этого проводятся параллели между концепцией трех измерений власти С. Льюкса, доктриной имплозии Ж. Бодрийяра и теорией дефицита внимания Д. Дзоло.

The article contains a review of inflation forecasting models, including the most popular class of models as one-factor models: random walk, direct autoregression, recursive autoregression, stochastic volatility with an unobserved component and of the integrated model of autoregression with moving average. Also, we discussed the possibilities of various modifications of models based on the Phillips curve (including the “triangle model”), vector autoregressive models (including the factor-extended model of B. Bernanke’s vector autoregression), dynamic general equilibrium models and neural networks. Further, we considered the comparative advantages of these classes of models. In particular, we revealed a new trend in inflation forecasting, which consists of the introduction of synthetic procedures for private forecasts accounting obtained by different models. An important conclusion of the study is the superiority of expert assessments in comparison with all available models. We have shown that in the conditions of a large number of alternative methods of inflation modelling, the choice of the adequate approach in specific conditions (for example, for the Russian economy of the current period) is a non-trivial procedure. Based on this conclusion, the authors substantiate the thesis that large prognostic possibilities are inherent in the mixed strategies of using different methodological approaches, when implementing different modelling tools at different stages of modelling, in particular, the multifactorial econometric model and the artificial neural network.

The study's relevance is due to the gradual transition of different countries of the world to a post-industrial economy, in which the share of industrial employment is significantly reduced. However, this process is usually associated with high social costs and management mistakes. Russia is not a happy exception to this rule. The article aims to identify the pain points of the Russian labour market and the higher education system caused by the transition process. For this purpose, based on the data of Rosstat, we considered the phenomenon of the educational bubble in the university sphere in 1992-2008 and the reasons for its occurrence. By using Russian and international statistics, it was possible to justify the gap between the sphere of higher education in Russia and the real sector of the economy. The analysis of the macroeconomic (aggregated) sectoral structure of the Russian economy and the higher education system did not reveal the existing personnel imbalances in Russia. This task we achieved by combining an external view of the manufacturing industry (comparison with other countries) and an internal one (study of its human resources potential). The main conclusion is that Russia is rebuilding the employment structure in the direction of the post-industrial stage of development. Still, at the same time, it does not have adequate support in the form of effective agricultural and industrial sectors. Such a transitive model of economic evolution is extremely inefficient and is fraught with the transformation of the country into a kind of “civilized colony” of the world system. To prevent this negative scenario, it is necessary, on the one hand, the most aggressive borrowing by the Russian industry of new technologies (including robots), on the other — the restoration of extremely close ties between universities and enterprises of the real sector of the economy. The model of the reintegration of universities and enterprises is a promising direction for further research.