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

Author

Listed:
  • Fajar, Muhammad
  • Prasetyo, Octavia Rizky
  • Nonalisa, Septiarida
  • Wahyudi, Wahyudi

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.

Suggested Citation

  • Fajar, Muhammad & Prasetyo, Octavia Rizky & Nonalisa, Septiarida & Wahyudi, Wahyudi, 2020. "Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia)," MPRA Paper 105042, University Library of Munich, Germany, revised 30 Nov 2020.
    • Handle: RePEc:pra:mprapa:105042
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    References listed on IDEAS

    as
    1. Jaemin Woo & Ann L. Owen, 2019. "Forecasting private consumption with Google Trends data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(2), pages 81-91, March.
    2. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    3. Bierens, Herman J., 1987. "Armax model specification testing, with an application to unemployment in the Netherlands," Journal of Econometrics, Elsevier, vol. 35(1), pages 161-190, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. D.V. Firsov & T.C. Chernyshevа, 2021. "Review of Successful Practices of Applying Nowcasting in Socio-Economic Forecasting," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 20(2), pages 269-293.
    2. Gamze Bayın Donar & Seda Aydan, 2022. "Association of COVID‐19 with lifestyle behaviours and socio‐economic variables in Turkey: An analysis of Google Trends," International Journal of Health Planning and Management, Wiley Blackwell, vol. 37(1), pages 281-300, January.
    3. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.

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    More about this item

    Keywords

    Unemployment; Google Trends; PHK; ARIMAX;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E39 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Other
    • J6 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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