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Using Google Trends to forecast migration from Russia: Search query aggregation and accounting for lag structure

Georgy T. Bronitsky & Elena Vakulenko

Applied Econometrics2024https://doi.org/10.22394/1993-7601-2024-73-78-101article
ABDC C
Weight
0.40

What the paper says

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”.

2 citations

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https://doi.org/https://doi.org/10.22394/1993-7601-2024-73-78-101

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@article{georgy2024,
  title        = {{Using Google Trends to forecast migration from Russia: Search query aggregation and accounting for lag structure}},
  author       = {Georgy T. Bronitsky & Elena Vakulenko},
  journal      = {Applied Econometrics},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.22394/1993-7601-2024-73-78-101},
}

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Using Google Trends to forecast migration from Russia: Search query aggregation and accounting for lag structure

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Evidence weight

0.40

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.24 × 0.4 = 0.09
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.