Munich Personal RePEc Archive

Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia)

Fajar, Muhammad and Prasetyo, Octavia Rizky and Nonalisa, Septiarida and Wahyudi, Wahyudi (2020): Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia). Published in: International Journal of Scientific Research in Multidisciplinary Studies , Vol. 6, No. 11 (30 November 2020): pp. 29-33.

[img]
Preview
PDF
MPRA_paper_105042.pdf

Download (767kB) | Preview

Abstract

The outbreak of COVID-19 is having a significant impact on the contraction of Indonesia`s economy, which is accompanied by an increase in unemployment. This study aims to predict the unemployment rate during the COVID-19 pandemic by making use of Google Trends data query share for the keyword “phk” (work termination) and former series from official labor force survey conducted by Badan Pusat Statistik (Statistics Indonesia). The method used is ARIMAX. The results of this study show that the ARIMAX model has good forecasting capabilities. This is indicated by the MAPE value of 13.46%. The forecast results show that during the COVID-19 pandemic period (March to June 2020) the open unemployment rate is expected to increase, with a range of 5.46% to 5.70%. The results of forecasting the open unemployment rate using ARIMAX during the COVID-19 period produce forecast values are consistent and close to reality, as an implication of using the Google Trends index query as an exogenous variable can capture the current conditions of a phenomenon that is happening. This implies that the time series model which is built based on the causal relationship between variables reflects current phenomenon if the required data is available and real-time, not only past historical data.

Logo of the University Library LMU Munich
MPRA is a RePEc service hosted by
the University Library LMU Munich in Germany.