Abstract
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”.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
