National Unemployment Rate Forecast with Google Trends

The significant economic recession and ongoing COVID-19 pandemic are impacting various sectors. The decrease in employment, one of the main consequences of this economic stagnation, is felt intensely in Türkiye. The concern that today's unemployment problem will be experienced more intensely in the future brings to the fore studies on unemployment forecasting. To date, unemployment forecasting studies have received extensive coverage in the literature. This study aims to make more successful forecasts of unemployment data by using Google Trends (GT), which is frequently used in different fields today. Four GT-based variables were incorporated into traditional forecasting methods, including ARIMA, ARIMAX, and VAR models. The VAR GT3 model, which integrates GT data with annual inflation, provided the best forecasting performance among all tested models. The findings indicate that models incorporating GT data derived from various keywords yield more successful results than traditional models.

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Publication Details

Journal
Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi
Published
2025-03-28
DOI
https://doi.org/10.30798/makuiibf.1446639
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
0.00
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article

National Unemployment Rate Forecast with Google Trends

Hasan Türe, Savaş Gayaker
Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi
Data-Driven Disease Surveillance
article

National Unemployment Rate Forecast with Google Trends

Hasan Türe, Savaş Gayaker
article en

Abstract

The significant economic recession and ongoing COVID-19 pandemic are impacting various sectors. The decrease in employment, one of the main consequences of this economic stagnation, is felt intensely in Türkiye. The concern that today's unemployment problem will be experienced more intensely in the future brings to the fore studies on unemployment forecasting. To date, unemployment forecasting studies have received extensive coverage in the literature. This study aims to make more successful forecasts of unemployment data by using Google Trends (GT), which is frequently used in different fields today. Four GT-based variables were incorporated into traditional forecasting methods, including ARIMA, ARIMAX, and VAR models. The VAR GT3 model, which integrates GT data with annual inflation, provided the best forecasting performance among all tested models. The findings indicate that models incorporating GT data derived from various keywords yield more successful results than traditional models.

Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi DergisiVol. 12(1)
Ankara Hacı Bayram Veli University (TR)
Openalex Percentile: Top 91%
Data-Driven Disease Surveillance
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