Abstract
The subject of this article is to describe possible areas for improvement of long-term economic forecasting methodology based on the mathematical toolkit of econometrics. Design issues are discussed with regard to methodological principles of applying this toolkit to forecasting and analytics. Special attention is paid to the development of a forecasting and analytical calculation scheme that ensures interconnected generation of long-term indicators of economic development at the macroeconomic and sectoral levels.
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Specifically, by using the input–output function model as a tool for macroeconomic calculations, one can assess the effect of technological changes at the level of the national economy as a whole. Working with time series of input–out coefficient in forecasting and analytical constructions allows one to identify trends in technological changes at the level of individual sectors (economic activities).
It is important to mark that, the persons invited for the aforementioned expertise of scientific and technological problems are (overwhelmingly) not developers of new types of equipment, materials, etc.
In this case, we assume beforehand that the column vectors of the initial attribute matrix describe the change over time in each of the indicators included into the initial set of the analyzed data. This approach does not limit the generality of the subsequent conclusions.
In this case, the year-by-year differences between the logarithms of the outputs are considered to be equivalent to the rates of growth (decline) of the said indicators.
Standardization is necessary because in the general case, the analyzed data can be measured in different units.
Naturally, the values of {a3i} are assumed to be zero for those economic activities for which the change over time in the third PC is insignificant (see Table 1).
A possible approach to identifying the stochastic component in (11) is proposed in [13].
The term volatility is broadly used for analysis of economic growth processes. However, historically, this term has been applied primarily to analysis of financial indices. In this case, we consider the terms volatility and variability as synonyms.
Elements of this scheme of forecasting and analytical calculations are discussed in [21].
It should be noted that the term economic technology, which is used in the IOF theory is essentially inapplicable to many activities in the service sector. Therefore, it would be logical to confine the IOF toolkit to the real sector; the relationship between changes in TOrs and GDP can be described using a special statistical model.
In (14), imports have a minus sign because, by definition, the total end demand is C1 + C2 + I + E–Im.
In [22], we used the input–output indicators as reported for 2011 according to Rosstat.
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This work was supported by the Russian Foundation for Basic Research, project no. 20-010-00344 “Theoretical, methodological, and applied aspects of designing model tools and methods for long-term forecasting of development trends in the Russian economy.”
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Translated by A. Kobkova
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Suvorov, N.V., Treshchina, S.V. & Beletskii, Y.V. Design of Methods for Long-Term Forecasting of Development Trends in the Russian Economy (Methodology and Model Toolkit). Stud. Russ. Econ. Dev. 31, 636–646 (2020). https://doi.org/10.1134/S107570072006012X
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DOI: https://doi.org/10.1134/S107570072006012X