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National Unemployment Rate Forecast with Google Trends

Savaş Gayaker(Ankara Hacı Bayram Veli Üniversitesi)0000-0002-7186-1532
Hasan Türe(ANKARA HACI BAYRAM VELİ ÜNİVERSİTESİ)0000-0002-1975-9063
Sayfa
—
Yayın Tarihi
2025-03-31
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The findings indicate that models incorporating GT data derived from various keywords yield more successful results than traditional models.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The significant economic recession and ongoing COVID-19 pandemic are impacting various sectors.
📊
Temel Bulgular & Veri:The decrease in employment, one of the main consequences of this economic stagnation, is felt intensely in Türkiye.
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Sonuç & Katkı:The findings indicate that models incorporating GT data derived from various keywords yield more successful results than traditional models.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:: Unemployment · Google Trends · VAR

Özet

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.

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.

🔖

Bu Makaleye Atıf Yap

Gayaker, S., Türe, H. (2025). National Unemployment Rate Forecast with Google Trends. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Volume: 12 Issue: 1. https://doi.org/10.30798/makuiibf.1446639

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