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Tracking the Ups and Downs in Indonesia’s Economic Activity During COVID-19 Using Mobility Index: Evidence from Provinces in Java and Bali

Author

Listed:
  • Yose Rizal Damuri

    (Centre for Strategic and International Studies (CSIS), Indonesia)

  • Prabaning Tyas

    (Tenggara Strategics, Indonesia)

  • Haryo Aswicahyono

    (Centre for Strategic and International Studies (CSIS), Indonesia)

  • Lionel Priyadi

    (Tenggara Strategics, Indonesia)

  • Stella Kusumawardhani

    (Tenggara Strategics, Indonesia)

  • Ega Kurnia Yazid

    (Centre for Strategic and International Studies (CSIS), Indonesia)

Abstract

A timely and reliable prediction of economic activities is crucial in policymaking, especially in the current COVID-19 pandemic situation, which requires real-time decisions. However, making frequent predictions is challenging due to the substantial delays in releasing aggregate economic data. This study aims to nowcast Indonesia’s economic activities during the COVID-19 pandemic using the novel high-frequency Facebook Mobility Index as a predictor. Employing mixed-frequency, mixed-data sampling, and benchmark least-squares models, we expanded the mobility index and used it to track the growth dynamics of the gross regional domestic product of provinces in Java and Bali and performed a bottom-up approach to estimate the aggregated economic growth of the provinces altogether. Our results suggested that the daily Facebook Mobility Index was a considerably reliable predictor for projecting economic activities on time. All models almost consistently produced reliable directional predictions. Notably, we found the mixed data sampling-autoregressive model to be slightly superior to the other models in terms of overall precision and directional predictive accuracy across observations.

Suggested Citation

  • Yose Rizal Damuri & Prabaning Tyas & Haryo Aswicahyono & Lionel Priyadi & Stella Kusumawardhani & Ega Kurnia Yazid, 2021. "Tracking the Ups and Downs in Indonesia’s Economic Activity During COVID-19 Using Mobility Index: Evidence from Provinces in Java and Bali," Working Papers DP-2021-18, Economic Research Institute for ASEAN and East Asia (ERIA).
  • Handle: RePEc:era:wpaper:dp-2021-18
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    References listed on IDEAS

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    1. Matteo Luciani & Madhavi Pundit & Arief Ramayandi & Giovanni Veronese, 2018. "Nowcasting Indonesia," Empirical Economics, Springer, vol. 55(2), pages 597-619, September.
    2. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    3. Sumedha Gupta & Laura Montenovo & Thuy Nguyen & Felipe Lozano‐Rojas & Ian Schmutte & Kosali Simon & Bruce A. Weinberg & Coady Wing, 2023. "Effects of social distancing policy on labor market outcomes," Contemporary Economic Policy, Western Economic Association International, vol. 41(1), pages 166-193, January.
    4. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
    5. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    6. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
    7. Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.
    8. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    9. Giovanni Bonaccorsi & Francesco Pierri & Matteo Cinelli & Andrea Flori & Alessandro Galeazzi & Francesco Porcelli & Ana Lucia Schmidt & Carlo Michele Valensise & Antonio Scala & Walter Quattrociocchi , 2020. "Economic and social consequences of human mobility restrictions under COVID-19," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(27), pages 15530-15535, July.
    10. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    11. Nano Prawoto & Eko Priyo Purnomo & Abitassha Az Zahra, 2020. "The Impacts of Covid-19 Pandemic on Socio-Economic Mobility in Indonesia," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 57-71.
    12. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    13. repec:hal:journl:peer-00844811 is not listed on IDEAS
    14. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    15. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    16. Tarsidin & Idham & Robbi Nur Rakhman, 2018. "Nowcasting Household Consumption And Investment In Indonesia," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 20(3), pages 1-30, January.
    17. Sampi Bravo,James Robert Ezequiel & Jooste,Charl, 2020. "Nowcasting Economic Activity in Times of COVID-19 : An Approximation from the Google Community Mobility Report," Policy Research Working Paper Series 9247, The World Bank.
    18. Ligang Song & Yixiao Zhou, 2020. "The COVID‐19 Pandemic and Its Impact on the Global Economy: What Does It Take to Turn Crisis into Opportunity?," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 28(4), pages 1-25, July.
    19. Ozili, Peterson & Arun, Thankom, 2020. "Spillover of COVID-19: Impact on the Global Economy," MPRA Paper 99317, University Library of Munich, Germany.
    20. Liu, Chengxi & Susilo, Yusak O. & Karlström, Anders, 2014. "Examining the impact of weather variability on non-commuters’ daily activity–travel patterns in different regions of Sweden," Journal of Transport Geography, Elsevier, vol. 39(C), pages 36-48.
    21. Jennie Bai & Eric Ghysels & Jonathan H. Wright, 2013. "State Space Models and MIDAS Regressions," Econometric Reviews, Taylor & Francis Journals, vol. 32(7), pages 779-813, October.
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    More about this item

    Keywords

    COVID-19; nowcasting; GDP; mobility; Mixed-frequency;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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