Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg
| Autor: | Alexey Kurbatskii, Dean Fantazzini, Julia Pushchelenko, Alexey Mironenkov |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
Science (General) Population Google Trends forecasting ARIMA migration Russia Q1-390 Econometrics QA1-939 Autoregressive integrated moving average Macro education education.field_of_study Cointegration business.industry Internal migration Univariate multivariate ridge regression time-varying VAR co-integration Geography The Internet VAR business Mathematics |
| Zdroj: | Forecasting, Vol 3, Iss 48, Pp 774-803 (2021) Forecasting Volume 3 Issue 4 Pages 48-803 |
| ISSN: | 2571-9394 |
| Popis: | This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data of the two Russian cities with the largest migration inflows: Moscow and Saint Petersburg. The empirical analysis does not provide evidence that the more people search online, the more likely they are to relocate to other regions. However, the inclusion of Google Trends data in a model improves the forecasting of the migration flows, because the forecasting errors are lower for models with internet search data than for models without them. These results also hold after a set of robustness checks that consider multivariate models able to deal with potential parameter instability and with a large number of regressors. |
| Databáze: | OpenAIRE |
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