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Real-Time Weakness of the Global Economy: A First Assessment of the Coronavirus Crisis

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  • Pérez-Quirós, Gabriel
  • Leiva-León, Danilo
  • Rots, Eyno

Abstract

We propose an empirical framework to measure the degree of weakness of the global economy in real-time. It relies on nonlinear factor models designed to infer recessionary episodes of heterogeneous deepness, and fitted to the largest advanced economies (U.S., Euro Area, Japan, U.K., Canada and Australia) and emerging markets (China, India, Russia, Brazil, Mexico and South Africa). Based on such inferences, we construct a Global Weakness Index that has three main features. First, it can be updated as soon as new regional data is released, as we show by measuring the economic effects of coronavirus. Second, it provides a consistent narrative of the main regional contributors of world economy’s weakness. Third, it allows to perform robust risk assessments based on the probability that the level of global weakness would exceed a certain threshold of interest in every period of time.

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  • Pérez-Quirós, Gabriel & Leiva-León, Danilo & Rots, Eyno, 2020. "Real-Time Weakness of the Global Economy: A First Assessment of the Coronavirus Crisis," CEPR Discussion Papers 14484, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:14484
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    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Francisco Corona & Graciela González-Farías & Pedro Orraca, 2017. "A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-35, December.
    3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    4. Kilian, Lutz, 2019. "Measuring global real economic activity: Do recent critiques hold up to scrutiny?," Economics Letters, Elsevier, vol. 178(C), pages 106-110.
    5. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2009. "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 595-611.
    6. Yunjong Eo & Chang-Jin Kim, 2016. "Markov-Switching Models with Evolving Regime-Specific Parameters: Are Postwar Booms or Recessions All Alike?," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 940-949, December.
    7. Mark Aguiar & Gita Gopinath, 2007. "Emerging Market Business Cycles: The Cycle Is the Trend," Journal of Political Economy, University of Chicago Press, vol. 115, pages 69-102.
    8. Daniela Bragoli & Luca Metelli & Michele Modugno, 2015. "The importance of updating: Evidence from a Brazilian nowcasting model," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2015(1), pages 5-22.
    9. Neumeyer, Pablo A. & Perri, Fabrizio, 2005. "Business cycles in emerging economies: the role of interest rates," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 345-380, March.
    10. David Kohn & Fernando Leibovici & Håkon Tretvoll, 2021. "Trade in Commodities and Business Cycle Volatility," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(3), pages 173-208, July.
    11. Dahlhaus, Tatjana & Guénette, Justin-Damien & Vasishtha, Garima, 2017. "Nowcasting BRIC+M in real time," International Journal of Forecasting, Elsevier, vol. 33(4), pages 915-935.
    12. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, March.
    13. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    14. Chernis, Tony & Cheung, Calista & Velasco, Gabriella, 2020. "A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth," International Journal of Forecasting, Elsevier, vol. 36(3), pages 851-872.
    15. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    16. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    17. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-996, November.
    18. Camacho, Maximo & Perez-Quiros, Gabriel & Poncela, Pilar, 2018. "Markov-switching dynamic factor models in real time," International Journal of Forecasting, Elsevier, vol. 34(4), pages 598-611.
    19. N/A, 2004. "Index for 2004," European Union Politics, , vol. 5(4), pages 511-512, December.
    20. Camacho, Maximo & Perez Quiros, Gabriel & Poncela, Pilar, 2014. "Green shoots and double dips in the euro area: A real time measure," International Journal of Forecasting, Elsevier, vol. 30(3), pages 520-535.
    21. Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-277, April.
    22. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
    23. Jerzmanowski, Michal, 2006. "Empirics of hills, plateaus, mountains and plains: A Markov-switching approach to growth," Journal of Development Economics, Elsevier, vol. 81(2), pages 357-385, December.
    24. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    25. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
    26. James D. Hamilton, 2019. "Measuring Global Economic Activity," NBER Working Papers 25778, National Bureau of Economic Research, Inc.
    27. Chauvet, Marcelle, 2001. "A Monthly Indicator of Brazilian GDP," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 21(1), May.
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    Cited by:

    1. Phurichai Rungcharoenkitkul, 2021. "Macroeconomic effects of COVID‐19: A mid‐term review," Pacific Economic Review, Wiley Blackwell, vol. 26(4), pages 439-458, October.
    2. Massimiliano Ferraresi, 2022. "The regional (re)allocation of migrants during the Great Lockdown in Italy," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 39(2), pages 403-426, July.
    3. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    4. Salisu, Afees A. & Gupta, Rangan & Bouri, Elie, 2023. "Testing the forecasting power of global economic conditions for the volatility of international REITs using a GARCH-MIDAS approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 303-314.
    5. Pérez Quirós, Gabriel, 2020. "Global Weakness Index – reading the economy’s vital signs during the COVID-19 crisis," Research Bulletin, European Central Bank, vol. 72.
    6. Donato Masciandaro, 2020. "Covid-19 Helicopter Money, Monetary Policy And Central Bank Independence: Economics And Politics," BAFFI CAREFIN Working Papers 20137, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    7. Funke, Michael & Tsang, Andrew, 2020. "The People’s bank of China’s response to the coronavirus pandemic: A quantitative assessment," Economic Modelling, Elsevier, vol. 93(C), pages 465-473.
    8. Balsalobre-Lorente, Daniel & Driha, Oana M. & Bekun, Festus & Sinha, Avik & Fatai Adedoyin, Festus, 2020. "Consequences of COVID-19 on the social isolation of the Chinese economy: accounting for the role of reduction in carbon emissions," MPRA Paper 102894, University Library of Munich, Germany, revised 2020.
    9. Donato Masciandaro, 2020. "Ecb Helicopter Money: Economic And Political Economy Arithmetics," BAFFI CAREFIN Working Papers 20138, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    10. Hervé Le Bihan & Danilo Leiva-León & Matías Pacce, 2023. "Underlying inflation and asymetric risks," Working Papers 2319, Banco de España.
    11. Piotr Skórka & Beata Grzywacz & Dawid Moroń & Magdalena Lenda, 2020. "The macroecology of the COVID-19 pandemic in the Anthropocene," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.
    12. Mary Oluwatoyin Agboola & Festus Victor Bekun & Daniel Balsalobre-Lorente, 2021. "Implications of Social Isolation in Combating COVID-19 Outbreak in Kingdom of Saudi Arabia: Its Consequences on the Carbon Emissions Reduction," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    13. repec:zbw:bofitp:2020_012 is not listed on IDEAS
    14. Boriss Siliverstovs, 2021. "Gauging the Effect of Influential Observations on Measures of Relative Forecast Accuracy in a Post-COVID-19 Era: Application to Nowcasting Euro Area GDP Growth," Working Papers 2021/01, Latvijas Banka.
    15. Sułkowski Łukasz, 2020. "Covid-19 Pandemic; Recession, Virtual Revolution Leading to De-globalization?," Journal of Intercultural Management, Sciendo, vol. 12(1), pages 1-11, March.

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    More about this item

    Keywords

    International business cycles; Factor model; Nonlinear; Coronavirus;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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