The use of annual, quarterly and monthly statistics in macroeconomic forecasting

This article summarizes model implementation experience in medium-term economic forecasting. The author reviews implementation issues of annual, quarterly and monthly time series with due regard to their content and representation of economic processes for medium-term macroeconomic forecasting. The article discusses the following questions: joint use of time series, exclusion of seasonal factor, allocation of cyclic components, development of factor models and simultaneous equations for groups of variables, using cross-industry models in forecasting. The author considers the relationship between cyclical factors and potential GDP growth. This time series study allowed identifying specific periods in the development of the Russian economy in recent decades; and development of factor models made it possible to define leading and specific factors of economic growth in periods under review. A significant change in the structure of potential GDP growth factors after 2012 led to examining growth factors that play crucial role in the new economic situation. Based on the study of cycles and related economic policy, the author draws some conclusions regarding the preparations for the forthcoming technological cycle. In conclusion, are noted some of the problems that arise in the development and use of a general equilibrium model for forecasting purposes.

временные ряды, месячная, квартальная, годовая статистика, сезонная «очистка», циклы, факторные модели, межотраслевые модели, time series, monthly, quarterly, annual statistics, seasonal «cleaning»? cycles, factor models, cross-sectoral models

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