Google Arama Verileri İşsizlik Oranı Tahmin Modelini İyileştirebilir mi? Türkiye için Ampirik Bir Analiz

Year 2022, , 229 - 244, 29.07.2022

Gülşah Şentürk

https://doi.org/10.26650/JEPR963438

Abstract

İnternet kullanımı esnasında depolanan verilerin insan davranışları, sorunları ve ihtiyaçları için önemli bir bilgi kaynağı haline geldiği günümüzde, Google arama verileri gerçek zamanlı olarak elde edilmesi nedeniyle araştırmacıların odağı haline gelmektedir. Pek çok ekonomik gösterge için tahmin modellerine dahil edilmeye başlanan Google Trends verilerinin işsizlik oranı tahmininde de kullanılması giderek yaygınlaşmaktadır. Bu çalışma, Türkiye’de işsizlik oranının tahmin edilmesinde Google arama verilerinin öngörü modeline dahil edilmesinin modelin öngörü yeteneğini iyileştirip iyileştirmediğini araştırmaktadır. Ocak 2005’ten Ağustos 2020’ye kadar olan dönem için mevsimsellikten arındırılmış aylık işsizlik oranları ile işsizlik sigortası konusuna dair aylık Google Trends verileri ele alınarak öngörü modeli oluşturulmaktadır. ARIMA ve ARIMAX yöntemleri aracılığıyla yapılan tahminlerin öngörü performansı kıyaslanmaktadır.

Keywords

Google trendler, İşsizlik oranı, Zaman serisi modelleri, Gelecek tahmini

Thanks

Bu çalışmanın yürütülmesinde tüm destek ve katkıları için Prof. Dr. Halil Tunalı ve Prof. Dr. Ferda Yerdelen Tatoğlu’na teşekkürlerimi sunarım.

Can Google Search Data Improve the Unemployment Rate Forecasting Model? An Empirical Analysis for Turkey

Abstract

Today, data accumulated during internet use have become an important source of information for people’s behaviour, issues, and needs, and due to real-time data acquisition, Google search data have become a focal point for researchers. As a result, it has been become more common to use GT data, which have been included in forecasting models for many economic indicators, including unemployment rate forecasting. Therefore, this study aims to determine whether including Google search data in forecasting models can improve the model’s performance in forecasting the unemployment rate in Turkey. In this context, out-of- sample forecasting was performed in this study using seasonally adjusted monthly unemployment rates for the period between January 2005 and August 2020 and monthly GT data about the topic of unemployment insurance. In addition, the forecasting performance of ARIMA and ARIMAX methods were compared.

Keywords

Google trends, Unemployment rate, Time-series model, Forecasting, ARIMA

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