STUDI PENGGUNAAN DATA GOOGLE TRENDS: KASUS PERAMALAN TINGKAT PENGANGGURAN USIA MUDA

Authors

  • D. S. Priyarsono IPB University
  • Siti Magfirotul Laelia , Fakultas Ekonomi dan Manajemen, Institut Pertanian Bogor

DOI:

https://doi.org/10.26593/be.v27i2.5276.1-24

Keywords:

Google Trends data, forecasting method, youth unemployment

Abstract

Google Trends data is an unbiased sample of search data produced by the Google search machine. This data represents the number of searches for a specific keyword using the search machine.  The availability of this free of charge data creates various opportunities of utilization for forecasting economic variables.  On the other hand, data produced by standard methods (using surveys) cannot be issued very frequently because of financial constraints and the limited mobility during COVID-19 pandemic.  This article reports a methodological study on the use of Google Trends data. The objective of this study is to show that the use of Google Trends data can improve the quality of forecasting.  To achieve the objective, youth unemployment data are utilized and prepared for analysis of Autoregressive Integrated Moving Average.  The quality of forecasting utilizing additional Google Trends data is compared with that without involving Google Trends data.  It is concluded that the quality of the forecasting using the first method is better than that of the second one.  This conclusion implies that utilizing Google Trends data can potentially improve the quality of forecasting and partially solve the problem of data scarcity resulted from financial constraints and the COVID-19 pandemic.

References

Aaronson, D., Brave, S. A., Butters, R. A., Fogarty, M., Sacks, D. W., Seo, B. (2021). Forecasting Unemployment Insurance Claims in Realtime with Google Trends. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2021.04.001.

Babii, A., Ghysels, E., Striaukas, J. (2021). Machine Learning Time Series Regressions with an Application to Nowcasting. Journal of Business & Economic Statistics. https://doi.org/10.1080/07350015.2021.1899933. Terbit daring 21 April 2021.

Badan Pusat Statistik. (2020). Keadaan Angkatan Kerja di Indonesia 2005-2019. Jakarta.

Bantis, E., Clements, M., Urquhart, A. (2021). Forecasting GDP Growth Rates Using Google Trends in the United States and Brazil. ICMA Centre, Henley Business School, University of Reading.

Borup, D., Schutte, E. C. M. (2020). In Search of a Job: Forecasting Employment Growth Using Google Trends. Journal of Business & Economic Statistics. 40:1, 186-200.

Choi, H. & Varian, H. (2009a). Predicting Initial Claims for Unemployment Benefits. Google Inc.

Choi, H. & Varian, H. (2009b). Predicting the Present with Google Trends. Google Inc.

D’Amuri, F. &Marcucci, J. (2017). The Predictive Power of Google Searches in Forecasting US Unemployment. International Journal of Forecasting. 33: 801–816.

Fondeur, Y. & Karamé, F. (2013). Can Google Data Help Predict French Youth Unemployment? Economic Modelling. 30: 117–125.

González-Fernández, M. & González-Velasco, C. (2018). Can Google Econometrics Predict Unemployment? Evidence from Spain. Economics Letters. 170: 42-45.

Hanke, J. E. & Wichern, D. W. (2005). Business Forecasting, Eighth Edition. Pearson Education: New Jersey.

International Labour Organization. (2015). Toward Solutions for Youth Employment. https://www.ilo.org/wcmsp5/groups/public/---ed_emp/documents/publication/wcms_413826.pdf

International Labour Organization. (2017). Global Employment Trends For Youth 2017. https://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_598669.pdf

Jun, S. P., Yoo, H. S., Choi, S. (2018). Ten Years of Research Change Using Google Trends: From the Perspective of Big Data Utilizations and Applications. Technological Forecasting & Social Change. 130:69-87. https://doi.org/10.1016/j.techfore.2017.11.009.

Laelia, S. M. (2020). Dinamika dan Peramalan Tingkat Pengangguran Usia Muda di Indonesia dengan Menggunakan Data Google Trends. [Skripsi]. Bogor (ID): Institut Pertanian Bogor.

Mavragani, A., Ochoa, G., Tsagarakis, K. P. (2018). Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. Journal of Medical Internet Research. 20:11. https://doi.org/10.2196/jmir.9366.

Medeiros M. C., Pires, H. F. (2021). The Proper Use of Google Trends in Forecasting Models. Pontificial Catholic University of Rio de Janeiro. https://arxiv.org/pdf/2104.03065.pdf.

Naccarato, A., Falorsi, S., Loriga, S., Pierini, A. (2018). Combining Official and Google Trends Data to Forecast the Italian Youth Unemployment Rate. Technological Forecasting & Social Change. 130: 114-122.

Nagao, S., Takeda, F., Tanaka, R. (2019). Nowcasting of the U.S. Unemployment Rate Using Google Trends.Finance Research Letters. 30: 103–109.

Petropoulos, A., Siakoulis, V., Stavroulakis, E., Lazaris, P., Vlachogiannakis, N. (2021). Employing Google Trends and Deep Learning in Forecasting Financial Market Turbulence. Journal of Behavioral Finance. https://doi.org/10.1080/15427560.2021.1913160. Terbit daring 25 Mei 2021.

Rahman, A. (2008). Analisis Eksistensi Persistensi Pengangguran di Indonesia. [Skripsi]. Bogor (ID): Institut Pertanian Bogor.

Vicente, M. R., López-Menéndez, A. J., Pérez, R. (2015). Forecasting Unemployment with Internet Search Data: Does it Help to Improve Predictions When Job Destruction is Skyrocketing? Technological Forecasting & Social Change. 92:132–139.

Woloszko, N. (2020). Tracking Activity in Real Time with Google Trends. OECD Working Papers. www.oecd.org/eco/workingpapers.

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Published

2023-09-01