Nowcasting of Russian manufacturing output using business survey data

This article is devoted to checking the possibility of using business survey data surveys to nowcasting of Russian manufacturing output: analysis of business cycles and short-term forecasting (1 month ahead). The study uses data from business surveys of the Federal State Statistics Service, the R...

Full description

Saved in:
Bibliographic Details
Main Author: R. E. Gartvich
Format: Article
Language:English
Published: Omsk State Technical University, Federal State Autonomous Educational Institution of Higher Education 2023-12-01
Series:Омский научный вестник: Серия "Общество. История. Современность"
Subjects:
Online Access:https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2023/%D0%A2.8,%20%E2%84%964%20(%D0%9E%D0%98%D0%A1)/152-160%20%D0%93%D0%B0%D1%80%D1%82%D0%B2%D0%B8%D1%87%20%D0%A0.%20%D0%95..pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article is devoted to checking the possibility of using business survey data surveys to nowcasting of Russian manufacturing output: analysis of business cycles and short-term forecasting (1 month ahead). The study uses data from business surveys of the Federal State Statistics Service, the Russian Union of Industrialists and Entrepreneurs and S&P Global. The results of these surveys are published promptly (20 days or more ahead of the release of official statistics) and are freely available. The study shows that the indicators of the Federal State Statistics Service (economic situation, number of employees, business confidence index, output, demand, export) allow to increase the accuracy of forecasts by 21–39 % and have a high correlation with business cycles of the manufacturing industry. In general, the use of most business survey indicators improves short-term forecasts of manufacturing output, more than half of the indicators have a correlation coefficient greater than 0,8 with the business cycles of this sector of the economy.
ISSN:2542-0488
2541-7983