Forecasting internal migration in Russia using Google Trends: Evidence from Moscow and Saint Petersburg

Dean Fantazzini, Julia Pushchelenko, Alexey Mironenkov and Alexey Kurbatskii
Authors registered in the RePEc Author Service: Alexey Nikolaevich Kurbatskiy

MPRA Paper from University Library of Munich, Germany

Abstract: 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.

Keywords: Migration; Forecasting; Google Trends; VAR; Cointegration; ARIMA; Russia; Time-varying VAR; Multivariate Ridge regression. (search for similar items in EconPapers)
JEL-codes: C22 C32 C52 C53 C55 F22 J11 O15 R23 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cis, nep-for, nep-ore, nep-pay, nep-tra and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Published in Forecasting 3.4(2021): pp. 774-804

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Related works:
Journal Article: Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:110452

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