Using Google Trends to forecast migration from Russia: Search query aggregation and accounting for lag structure
DOI:
https://doi.org/10.22394/1993-7601-2024-73-78-101Keywords:
международная миграция, Россия, Германия, Google Trends, поисковые запросы, наукастинг, большие данные, прогнозированиеAbstract
This paper proposes an approach for predicting migration statistics using Google Trends Index (GTI) search query data. We improved the existing methodology in two directions: firstly, we proposed an approach of aggregating key search queries 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 migration goals and the associated GTIs. We demonstrate the performance of the proposed approaches on monthly data from the German statistical office on migration volume from Russia to Germany from January 2011 to August 2022. The results show that distributed lag migration models with GTI are better predict migration than SARIMA models. Average lag estimates, i.e. the reaction time of migration statistics to search queries on the topics “embassy”, “work” and “study”, were 5.6, 6.5 and 8 months, respectively. We demonstrate that for forecasting migration from Russia to Germany, it is sufficient to consider only search queries related to the topic “embassy”.Agencies
The authors express their gratitude to O. S. Chudinovskikh for assistance in gaining access to migration statistics data from the German statistical office, as well as to the anonymous reviewers and A. D. Slastnikov for their valuable comments and suggestions.Downloads
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