Об использовании фиктивных переменных для решения проблемы сезонности в моделях общего экономического равновесия
This paper considers a seasonal adjustment procedure that is capable of preparing data to the use in applied general equilibrium models. It is shown that standard seasonal adjustment procedures do not satisfy the property of invariance to deflating, that hinders their use in applied general equilibrium models. A system of axioms that describes the desired properties of a seasonal adjustment procedure is suggested. The impossibility of simultaneous fulfillment of additivity and invariance to deflation properties is shown. Therefore, one needs to choose the desired property depending on the type of the task that is solved. The proposed procedure models the seasonality as a set of seasonal multiplicative dummy variables, so it can not only remove the seasonality, but also return it to the data in order to obtain forecasts. The procedure also has a built-in outlier detector, which enables it to handle noise and outliers in data of different types. It is compared to the popular X12 seasonal adjustment procedure using Monte-Carlo method. It is shown that the preciseness of the proposed procedure is comparable to X12 in terms of resistance to outliers and preservation of statistical properties of the series in the specific set of problems connected to the estimation of general equilibrium models. Several examples of its application to real data are shown. The obtained results allow us to make a conclusion about applicability of the suggested procedure to the removal of seasonality from the data that is used in the estimation of macroeconomic models.
Seasonality and cyclicity - are two influential factors that affect dynamics of macroeconomic indicators both during the year and longer periods of time. In this article are discussed methodological questions that arise during seasonal decomposition of the GDP by factors for the year when balance aggregate and factors ratio is constant. Economic cycles mechanisms origin and their identification questions based on the combination of classical methods of spectral analysis and historic approach. Presented is the fact that along with more regular cycles such as investment and Kondratiev wave, influence of shocks (such as «oil prices crises») appear so called causal cycles that lead to a serious change in technological base of production. Particular importance (emphasis is placed on ) a new technological wave which is expected to strike the world in 2020 th and those goal set before the Russia. This research is done on the basis of world and Russian (national) statistics.
To large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels. BI is now impacted by the “Big Data” phenomena and the evolution of society and users. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications. In addition, they must be able to provide their results also on mobile devices, taking into account location-based or time-based environmental data. The lectures held at the Third European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI and BPM technologies, but extend into innovative aspects that are important in this new environment and for novel applications, e.g., pattern and process mining, business semantics, Linked Open Data, and large-scale data management and analysis. Combining papers by leading researchers in the field, this volume equips the reader with the state-of-the-art background necessary for creating the future of BI. It also provides the reader with an excellent basis and many pointers for further research in this growing field.
The main goal of this paper is to study interconnections between credit ratings and financial indicators of industrial companies from BRICS countries. We use method of patterns, one of the modern methods of nonlinear modeling, to identify groups of heterogeneous objects with different influence on ratings. Additionally, in this research, we evaluate Tobit regression model for selected groups and establish some credit rating patterns for the BRICS industrial companies. Our results of Tobin model, may have practical implementation in short-term financial management.
The analysis of short-term tendency of economic dynamics can be performed on seasonally adjusted data only. This implies that each time series is to be transformed in two: the seasonal component and the remaining part. The result of such decomposition depends on the specific features of the seasonal adjustment algorithm. Most uncertainty is expected within the neighborhood of crises when the economic indicators are likely to demonstrate substantial changes. Under such circumstances, the seasonal adjustment procedures are likely to generate spurious signals that deteriorate the seasonally adjusted series.
In this paper we analyze distortions of seasonally adjusted time series of economic data that appear in the neighborhood of crises. We examined the aberrations caused by sharp level shifts as well as by changes in seasonal pattern and showed that under these circumstances the standard algorithms of seasonal adjustment can generate spurious signals similar to first signs of a crisis or its second and following waves. We consider these misleading signals from two points of view: first, as an economic historian who operates with long time series of unchanging data; second, as an analyst of short-term dynamics monitoring the data that is subject to revisions.
We show that these aberrations can be misleading for understanding of short-run dynamics especially during the first years after a crisis. The identification of the end of a recession and estimation of seasonally adjusted values of observations right after the peak (or bottom) of a fluctuation seem to be the most problematic. Monitoring within this “blind zone” appears to be very complicated. We compared aberrations produced by X-12-ARIMA and TRAMO/SEATS. Some recommendations to soften the distortions are proposed.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.