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Nowcasting Economic Activity in Times of COVID-19 : An Approximation from the Google Community Mobility Report

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  • Sampi Bravo,James Robert Ezequiel
  • Jooste,Charl

Abstract

This paper proposes a leading indicator, the "Google Mobility Index," for nowcasting monthly industrial production growth rates in selected economies in Latin America and the Caribbean. The index is constructed using the Google COVID-19 Community Mobility Report database via a Kalman filter. The Google database is publicly available starting from February 15, 2020. The paper uses a backcasting methodology to increase the historical number of observations and then augments a lag of one week in the mobility data with other high-frequency data (air quality) over January 1, 2019 to April 30, 2020. Finally, mixed data sampling regression is implemented for nowcasting industrial production growth rates. The Google Mobility Index is a good predictor of industrial production. The results suggest a significant decline in output of between 5 and 7 percent for March and April, respectively, while indicating a trough in output in mid-April.

Suggested Citation

  • 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.
    • Handle: RePEc:wbk:wbrwps:9247
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    References listed on IDEAS

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    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. 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.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    5. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
    6. Onatski, Alexei, 2015. "Asymptotic analysis of the squared estimation error in misspecified factor models," Journal of Econometrics, Elsevier, vol. 186(2), pages 388-406.
    7. 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.
    8. Onatski, Alexei, 2012. "Asymptotics of the principal components estimator of large factor models with weakly influential factors," Journal of Econometrics, Elsevier, vol. 168(2), pages 244-258.
    9. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    10. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    11. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    12. repec:hal:journl:peer-00844811 is not listed on IDEAS
    13. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    14. 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.
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    Cited by:

    1. 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).
    2. Joshua Rosenberg & Ilan Strauss & Gilad Isaacs, 2021. "COVID‐19 impact on SADC labour markets: Evidence from high‐frequency data and other sources," African Development Review, African Development Bank, vol. 33(S1), pages 177-193, April.
    3. Steven J. Davis & Dingqian Liu & Xuguang Simon Sheng, 2022. "Stock Prices and Economic Activity in the Time of Coronavirus," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 70(1), pages 32-67, March.
    4. Davide Furceri & Siddharth Kothari & Longmei Zhang, 2021. "The effects of COVID‐19 containment measures on the Asia‐Pacific region," Pacific Economic Review, Wiley Blackwell, vol. 26(4), pages 469-497, October.
    5. Emily Schmidt & Paul Dorosh & Rachel Gilbert, 2021. "Impacts of COVID‐19 induced income and rice price shocks on household welfare in Papua New Guinea: Household model estimates," Agricultural Economics, International Association of Agricultural Economists, vol. 52(3), pages 391-406, May.
    6. Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.
    7. Franky Juliano Galeano-Ramírez & Nicolás Martínez-Cortés & Carlos D. Rojas-Martínez, 2021. "Nowcasting Colombian Economic Activity: DFM and Factor-MIDAS approaches," Borradores de Economia 1168, Banco de la Republica de Colombia.
    8. Noel Rapa, 2021. "Mitigation measures, prevalence response and public mobility during the COVID-19 emergency," CBM Working Papers WP/03/2021, Central Bank of Malta.
    9. Velias, Alina & Georganas, Sotiris & Vandoros, Sotiris, 2022. "COVID-19: Early evening curfews and mobility," Social Science & Medicine, Elsevier, vol. 292(C).
    10. Chakrabarty, Debajyoti & Bhatia, Bhanu & Jayasinghe, Maneka & Low, David, 2023. "Relative deprivation, inequality and the Covid-19 pandemic," Social Science & Medicine, Elsevier, vol. 324(C).
    11. Mantas Lukauskas & Vaida Pilinkienė & Jurgita Bruneckienė & Alina Stundžienė & Andrius Grybauskas & Tomas Ruzgas, 2022. "Economic Activity Forecasting Based on the Sentiment Analysis of News," Mathematics, MDPI, vol. 10(19), pages 1-22, September.

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    Keywords

    International Trade and Trade Rules; Health Care Services Industry; Pharmaceuticals Industry; ICT Policy and Strategies; ICT Legal and Regulatory Framework; Economic Conditions and Volatility;
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