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

Yıl 2022, , 229 - 244, 29.07.2022

Gülşah Şentürk

https://doi.org/10.26650/JEPR963438

Öz

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.

Anahtar Kelimeler

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

Teşekkür

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Aouadi, A., Arouri, M., & Teulon, F. (2013). Investor attention and stock market activity: Evidence from France. Economic Modelling, 35, 674–681.
• Askitas, N., & Zimmermann, K. F. (2009). Google econometrics and unemployment forecasting. Discussion Paper, 4201.
• Baker, S., & Fradkin, A. (2013). The impact of unemployment insurance on job search: Evidence from Google search data. The Review of Economics and Statistics, 99(5), 756–768.
• Bangwayo-Skeete, P. F., & Skeete, R. W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management, 46, 454–464.
• Barreira, N., Godinho, P., & Melo, P. (2013). Nowcasting unemployment rate and new car sales in South-Western Europe with Google Trends. Netnomics, 14, 129–165.
• Beracha, E., & Wintoki, M. B. (2013). Forecasting residential real estate price changes from online search activity. Journal of Real Estate Research, 3, 283–312.
• Bolivar, Ortiz, & Rodrigo. (2019). Nowcasting Turkish unemployment using real time data from Google. BBVA Research.
• Box, G., & Jenkins, G. (1976). Time series analysis, forecasting and control. Holden-Day: California. Bughin, J. (2011). Nowcasting the Belgian economy.. SSRN working paper.
• Bui, V. X., & Nguyen, H. T. (2019). Stock market activity and Google Trends: The case of a developing economy. Journal of Economics and Development, 21(2), 191–212.
• Castelnuovo, E., & Tran, T. D. (2017). Google it up! A Google Trends-based uncertainty index for the United States and Australia. Economics Letters, 161, 149–153.
• Chadwick, M. G., & Şengül, G. (2012). Nowcasting unemployment rate in Turkey: Let's ask Google. TCMB Working Paper, 12(18).
• Choi, H., & Varian, H. (2009). Predicting initial claims for unemployment benefits. Technical Report.
• D’Amuri, F. (2009b). Predicting unemployment in short samples with internet job search query data. MPRA Paper, 18403
• D’Amuri, F., & Marcucci, J. (2009). ‘Google it!’ Forecasting the US uneployment rate with a Google job search index. . MPRA Paper, 18248.
• Da, Z., Engelberg, J., & Gao, P. (2011).In search of attention. The Journal of Finance, 1461–1499.
• Donadelli, M., & Gerotto, L. (2019). Non-macro based Google searches, uncertainity, and real economic activity. Research in International Business and Finance, 48, 111–142.
• Eurostat. (2019). https://ec.europa.eu/eurostat
• Fondeur, Y., & F. Karam’e. (2013). Can Google data help predict French youth unemployment? Economic Modelling, 30, 117–125.
• Ginsberg, J., Mohebbi, H. M., Patel, S. R., Brammer, L., Smolinski, S., & Brilliant, L. (2008). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014.
• Goddard, J., Kita, A., & Wang, Q. (2015). Investor attention and FX market volatility. Journal of International Financial Markets, 79–96.
• Google. (2017). https://support.google.com/trends Google Trends. (2020). https://trends.google.com
• Hamid, A., & Heiden, M. (2015). Forecasting volatility with empirical similarity and Google Trends. Journal of Economic Behavior & Organization, 117, 62–81.
• Han, L., Lv, Q., & Yin, L. (2017). Can investor attention predict oil prices? Energy Economics, 66, 547–558.
• Huang, M. Y., Rojas, R. R., & Convery, P. D. (2020). Forecasting stock market movements using Google Trend searches. Empirical Economics, 59, 2821–2839.
• Jain, A., & Biswal, P. C. (2019). Does internet search interest for gold move the gold spot, stock and exchange rate markets? A study from India. Resources Policy, 61, 501–507.
• Kholodilin, K. A., Podstawski, M., & Siliverstovs, B. (2010). Do google searches help in nowcasting private consumption? A real-time evidence for the US. KOF Working Paper.
• Mondria, J., Wu, T., & Zhang, Y. (2010). The determinants of international investment and attention allocation: Using internet search query data. Journal of International Economics, 82(1), 85–95.
• Niesert, R., Oorschot, J., Veldhuisen, C., Brons, K., & Lange, R. (2019). Can Google search data help predict macroeconomic series? Tinbergen Institute Discussion Paper.
• Pavlicek, J., & Kristoufek, L. (2015). Nowcasting unemployment rates with Google Searches: Evidence from the Visegrad group countries. PLOS One, 10(5), 1–11.
• Sevüktekin, M. & Çınar, M. (2017). Ekonometrik Zaman Serileri Analizi: EViews Uygulamalı (Fifth Edition).
• Bursa: Dora Yayıncılık.
• Smith, G. P. (2012). Google Internet search activity and volatility prediction in the market for foreign currency. Finance Research Letters, 9, 103–110.