The article describes methods for verification of a statistical model, which, firstly, is represented by time series of initial data and, secondly, is linear in the parameters being estimated. Traditionally, the use of econometric methods is based on the representation of the process under study in the form of a linear regression model. In this case, if the sets of explanatory and explained variables are represented by time series, the standard regression model allows obtaining only estimates of the structural parameters averaged over the time interval of the observed model variables. A natural generalization of the classical regression model (representing a wide class of practically important numerical problems both in the field of economic and statistical research, and in technical and other fields) is a model in which the structural parameters to be estimated on empirical data are variable in time. The article substantiates the methods of estimating the structural parameters of various types of statistical models, with reference to which the problem of estimating the dynamics of these parameters is relevant.

The article is devoted to the problem of increasing the accuracy of forecasting by combining individual forecasts. It presents a modification of one of the most popular forecast combination methods proposed by Granger and Ramanathan, which is based on minimizing the prediction error. These methods are often used in practical calculations. However, they can give negative coefficients and, thus, some weights may exceed one. The paper considers a modification of the methodology based on the use of an iterative procedure in order to exclude the negative weights from combining the forecasts. The authors carried out comparative analysis of both the proposed method and the traditional Granger-Ramanathan combination with restrictions on weights summing to one, and individual forecasts on the basis of which the combining is performed. The comparison was made on the basis of time series on the production of a number of types of products in physical terms.  The results of the study made it possible to conclude that the proposed modification of the Granger-Ramanathan method using sequential prediction does slightly reduce its accuracy, but it saves the approach in question from negative weighting coefficients. As a result it broadens the applicability of the real practice-oriented forecasting.

 

This article presents results of a study conducted using mathematical and statistical modelling of regional economic specifics of the Russian Northern and Arctic regions in comparison to the national economic trend. The research continues a series of works on modelling the dynamics of GRP production aimed at improving the measurement quality of the interaction between the main production factors and assessing the specificity of their influence on the economic growth in the regions under review. The article considers the impact of investments on the GRP production in the Northern and Arctic regions is argued which is determined by a new approach to the management of these territories through the formation of support zones of development based on the implementation of interconnected different scale investment projects. Mathematical and statistical modelling of GRP production in the Northern and Arctic regions and the Russian Federation as a whole was carried out using the multiplicative production function and the CES production function. For the eight regions of the North [Karelia, Komi Republic, Arkhangelsk region, Nenets autonomous area, Yamal-Nenets autonomous area, Republic of Tuva, Republic of Sakha (Yakutia), Kamchatka territory, and Magadan region] and for the Russian Federation as a whole, were matched variants of a model, linking GRP with investments. For four constituent entities (Murmansk region, Sakhalin region, Khanty-Mansi autonomous area - Yugra, and Chukotka autonomous area), it was not possible to select such variants of the model. In this regard the authors point out that there is a need for further improvement of mathematical and statistical modelling of the dependence of economic development of the regions on the dynamics of investment activity.

Chemical industry (chemical complex) is the basic segment of industrial production of Russia and is one of the leading fields of activities in world’s advanced economies. The evaluation of development trends of the country’s chemical complex, analysis and forecasting of various segments of the chemical production market necessitates the improvement of collection and accumulation of necessary information. The authors generalized foreign experience of empirical research on key development trends in the chemical industry. This article presents ways to better the statistical monitoring system of the chemical industry sector in Russia (on the example of certain types of activity of the chemical complex, such as the production of plastic products, chemical fibers and threads), which is viewed as a kind of statistical survey. In the final part of the article there are the authors’ proposals for the improvement of statistics (in part of the chemical complex statistics - the industrial production segment). Among them: the specification of individual sections of the Russian Classification and accounting of certain types of chemical products in accordance with international standards, restoring the reporting practice for indicators of the state of fixed assets.

This article considers statistical approaches to measuring agglomeration effects on the example of the so-called «economic density» that is measured by the profit indicators. The authors determined and analyzed the indicators of profit, productivity, infrastructure, transactions and labor input density in the urban districts of the regions of the Privolzhsky (Volga) Federal District for 2013-2016. There is a comparative analysis that showed significant agglomeration effects for the development of urban districts with a population of more than 100,000 people compared to all urban settlements in the regions. Agglomeration processes (density indicators) have positive impact on social parameters, including the development of human potential. There is a strong direct relationship between the effects of agglomeration development and infrastructure coverage and the size of local businesses.

This article presents retropective economic and statistical analysis of the role of agriculture in the economy of the Republic of Bashkortostan. The study examined the patterns and trends occurring in Russian and regional economies in the long-term dynamics of 1990 - 2016; macroeconomic indicators outlining the role of agriculture in both regional economy and Russian economy in general were compared. The authors constructed and analyzed trend models of the share of gross value added (GVA) of agriculture in the total gross value added of all branches of the regional economy, fixed assets of agriculture in the total volume of fixed assets and the level of fixed capital investment in agriculture in total investment of republic. Comparative analysis of the Russian average macroeconomic indicators with the indicators of Republic of Bashkortostan demonstrated that, in general, the trends in the republic’s agricultural sector are more encouraging compared to the general national trends.

This article presents the study of international practice in organizing and conducting statistical surveys on the performance of research and development in organizations. The authors characterize the organization of statistical agencies in the USA, Canada, England, France, the Czech Republic, China, Japan and Australia. Methodological approaches to organizing surveys according to international standards, and certain aspects associated with adapting them to suit national purposes are considered. The article analyses principles on which science statistics in foreign countries is based, using differentiated approach to data collection for individual institutional sectors. Findings are summed up; general conclusions that would be of interest to Russian statisticians are formulated.

This article covers questions of enabling a better use of the data from national agricultural censuses, in particular, that of the 2016 Russian Agricultural Census (VSHP-2016). Analysis of programmes and materials for the VSHP regulatory and legal support and organizational and methodological documents that so far have been prepared for the FAO World Programme for the Census of Agriculture 2020, served as a basis for identifying groups of agricultural censuses data users. The author focuses on the need to apply international standards of various aspects of organization and carrying out of a such wide-scale statistical work to Russian conditions. The article formulates proposes on improving interactions between agricultural data producers and users, which in the author’s opinion should guarantee a more efficient use of the VSHP-2016.