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.
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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.
Item Type: | MPRA Paper |
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Original Title: | Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia) |
English Title: | Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia) |
Language: | English |
Keywords: | Unemployment, Google Trends, PHK, ARIMAX |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E24 - Employment ; Unemployment ; Wages ; Intergenerational Income Distribution ; Aggregate Human Capital ; Aggregate Labor Productivity E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E39 - Other J - Labor and Demographic Economics > J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers J - Labor and Demographic Economics > J6 - Mobility, Unemployment, Vacancies, and Immigrant Workers > J64 - Unemployment: Models, Duration, Incidence, and Job Search |
Item ID: | 105042 |
Depositing User: | Mr Muhammad Fajar |
Date Deposited: | 31 Dec 2020 12:10 |
Last Modified: | 31 Dec 2020 12:10 |
References: | 1. Badan Pusat Statistik, “May 5 edition,” Berita Resmi Statistik (BRS), Indonesia, 2020. 2. Badan Pusat Statistik, “June 2 edition,” Berita Resmi Statistik (BRS), Indonesia, 2020. 3. Badan Pusat Statistik, “June 15 edition,” Berita Resmi Statistik (BRS), Indonesia, 2020. 4. D.N. Gujarati, “Basic Economectrics, 4th Edition.,” The McGraw-Hill Companies Inc., New York, 2004. 5. G.C. Chow and A.L. Lin, “Best linear unbiased interpolation, distribution, and extrapolation of time series by related series,” The review of Economics and Statistics, 372-375, 1971. 6. H.J. Bierens, “ARMAX model specification testing, with an apllication to unemployment in the Netherlands,” Journal of Econometrics, 35 (1), 161-190, 1987. 7. J.J.M. Moreno, A.P. Pol, A.S. Abad, and B.C. Blasco, “Using the R-MAPE index as a resistant measure of forecast accuracy,” Psicothema 25 (4), 500-506., 2013. 8. J. Woo and A.L. Owen, “Forecasting private consumption with Google Trends data,” Journal of Forecasting,38, 81– 91, 2019. 9. Lembaga Administrasi Negara, “Dampak COVID-19 terhadap kondisi sosial-ekonomi Indonesia,” Webinar of COVID-19 dan tantangan mewujudkan pembangunan berkelanjutan on June 27, 2020 addressed by Chief Statistician of Statistics Indonesia, 2020. 10. M.Y. Huang, R.R. Rojas, and P.D. Convery, “Forecasting stock market movements using Google Trend searches,” Empirical Economics, 2019. 11. S. Poyyamozhi and A. Kachi Mohideen, “Forecasting Analysis for Tuberculosis (TB) Incidence in Tamilnadu,” International Journal of Scientific Research in Mathematical and Statistical Sciences, 2018. 12. W. Anggraeni and A. Laras, “Using Google Trend data in forecasting number of dengue fever cases with ARIMAX method case study: Surabaya, Indonesia,” 2016 International Conference on Information & Communication Technology and Systems (ICTS). IEEE, 114-11, 2016. 13. W. Enders, “Applied Econometric Time Series, 2nd Edition,” John Wiley & Sons, Inc., New York, 2004. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/105042 |