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http://dx.doi.org/10.14400/JDC.2019.17.9.135

Application of Google Search Queries for Predicting the Unemployment Rate for Koreans in Their 30s and 40s  

Jung, Jae Un (Department of Management Information Systems, Dong-A University)
Hwang, Jinho (Department of Management Information Systems, Dong-A University)
Publication Information
Journal of Digital Convergence / v.17, no.9, 2019 , pp. 135-145 More about this Journal
Abstract
Prolonged recession has caused the youth unemployment rate in Korea to remain at a high level of approximately 10% for years. Recently, the number of unemployed Koreans in their 30s and 40s has shown an upward trend. To expand the government's employment promotion and unemployment benefits from youth-centered policies to diverse age groups, including people in their 30s and 40s, prediction models for different age groups are required. Thus, we aimed to develop unemployment prediction models for specific age groups (30s and 40s) using available unemployment rates provided by Statistics Korea and Google search queries related to them. We first estimated multiple linear regressions (Model 1) using seasonal autoregressive integrated moving average approach with relevant unemployment rates. Then, we introduced Google search queries to obtain improved models (Model 2). For both groups, consequently, Model 2 additionally using web queries outperformed Model 1 during training and predictive periods. This result indicates that a web search query is still significant to improve the unemployment predictive models for Koreans. For practical application, this study needs to be furthered but will contribute to obtaining age-wise unemployment predictions.
Keywords
30s and 40s Unemployment; Web Search Query; Prediction; SARIMA Model; Machine Learning;
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Times Cited By KSCI : 2  (Citation Analysis)
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