Date of Publication
6-21-2022
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Applied Economics major in Industrial Economics
Subject Categories
Labor Economics | Work, Economy and Organizations
College
School of Economics
Department/Unit
Economics
Thesis Advisor
Myrna S. Austria
Ma. Ella C. Oplas
Tereso S. Tullao, Jr.
Winfred M. Villamil
Defense Panel Chair
Tereso S. Tullao, Jr.
Defense Panel Member
Myrna S. Austria
Ma. Ella C. Oplas
Winfred M. Villamil
Abstract/Summary
With search engines gaining traction for job seekers, Internet searches have become a viable data source in forecasting unemployment in developed countries. The project seeks to answer if search data from Google Trends are useful as a factor in forecasting the unemployment rates in the context of developing countries such as the Philippines. Search and matching theory is the basis for the use of Google Trends as the theory uses search intensity to model unemployment outcomes. The models used in forecasting are VAR and the ARIMA regression models. The data on the chosen variables are taken from the Google Trends website and the quarterly LFS. The RMSE, MAE, and MAPE error measures were applied to test the accuracy between the models’ forecasts. The tests show Google Trends models as more appropriate for short-term forecasting and may be less appropriate for long-term forecasting.
Abstract Format
html
Keywords
Unemployment—Philippines—Forecasting
Recommended Citation
Bellosillo, D. A., Bernardo, L. A., Ng, M. C., & Villanueva, C. B. (2022). Google trends indices as factors in forecasting unemployment rates in the Philippines. Retrieved from https://animorepository.dlsu.edu.ph/etdb_econ/41
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Embargo Period
6-23-2024
