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News
October 12, 2026
Road Injuries Leading Cause of Death Among Men Aged 10 to 39
Worldwide, road injuries are the leading cause of death among males aged 10–39. An international team of researchers reached this conclusion as part of the Global Burden of Disease Study 2023, analysing data from 204 countries between 1990 and 2023. Their findings have been published in The Lancet Public Health, with HSE University Professor Vasily Vlassov listed among the collaborators.
October 8, 2026
HSE Experts Take Part in 23rd Annual Meeting of Valdai Discussion Club
The 23rd Annual Meeting of the Valdai Discussion Club was held from September 28 to October 1, 2026 under the theme ‘Responsibility for the Future: Limits of the Possible, or Limitless Possibilities?’ The forum brought together 120 experts from 40 countries, including representatives of China, the United States, India, Brazil, the United Kingdom, Germany, Egypt, Iran, and Japan.
October 7, 2026
‘Our Team Consists of True Leaders in Their Respective Academic Disciplines
The HSE International Centre of Decision Choice and Analysis studies a wide range of methods for analysing decision-making and possible scenarios for the development of natural, socio-economic, and political phenomena using various mathematical models. The application of advanced mathematical methods to forecasting helps to prevent negative outcomes and avoid erroneous decisions. The HSE News Service spoke to the centre’s director, Prof. Fuad Aleskerov, about its work.

 

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Применение Google Trends для прогнозирования миграции из России: агрегация поисковых запросов и учет лаговой структуры

Прикладная эконометрика. 2024. № 73. С. 78–101.
Bronitsky G., Vakulenko E.

This paper proposes a method for predicting migration based on search query data statistics using Google Trends Index (GTI). We improved the existing methodology in two directions: firstly, we proposed an approach for selecting key search queries and aggregating them based on various statistical criteria; secondly, we showed the importance of including in the migration model the time lag structure of search queries, depending on the goals of the migration and the associated GTIs. Based on the monthly data of the German statistical office on the volume of migration from Russia to Germany from January 2011 to August 2022, we demonstrate the efficiency of the proposed approaches. The results show that distributed lag migration models with GTI are better predict migration than SARIMA models. Average lag estimates, i.e. the response time of migration statistics to search queries on the topics “embassy”, “work” and “study”, turned out to be 5.6, 6.5 and 8 months, respectively. It is shown that in order to predict migration from Russia to Germany, it is sufficient to take into account only search queries related to the topic "embassy".

Research target: Economics and Management Sociology (including Demography and Anthropology
Language: Russian
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DOI
Text on another site
Keywords: ГерманияРоссиямеждународная миграцияRussiaGermanyinternational migrationбольшие данныеbig datanowcastingGoogle TrendsGoogle Trendsпоисковые запросынаукастингsearch queries
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