5 conclusions motivated by recent events that signal

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5 Conclusions Motivated by recent events that signal a potentially global synchronized slowdown, we pro- pose an econometric framework able to provide timely and accurate real-time inferences about the state of the world economy. We introduce a mixed frequency dynamic factor model that allows for heterogeneous deepness across recessionary episodes. The proposed model is fitted to twelve of the world’s largest economic regions. Our estimates show that allowing for heteroge- neous recessions turns to be crucial in accurately inferring periods of weak real activity growth associated with both advanced and emerging economies, outperforming frameworks previously proposed in the literature. Next, the estimated regional inferences are summarized into a Global Weakness Index (GWI), which is able to provide daily real-time updates of the global economy strength, its underlying sources, and risk assessments, as new information across the regions is released. The proposed framework for monitoring the state of the world economy can be extended in different ways. For example, by determining the most adequate set of economic indicators associated to each region in order to maximize the accuracy when inferring region-specific re- cessions. Also, a wider range of regions can be incorporated in the construction of the global ECB Working Paper Series No 2381 / March 2020 23
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weakness index with the aim of sharpen the accuracy when inferring world recessions. ECB Working Paper Series No 2381 / March 2020 24
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References Adrian, T., N. Boyarchenko, and D. Giannone (2019). Vulnerable growth. American Economic Review 109 (4), 1263–1289. 4.2 Aguiar, M. and G. Gopinath (2007). Emerging market business cycles: The cycle is the trend. Journal of Political Economy 115 (1), 69–102. 1 , 3 Banbura, M., D. Giannone, and L. Reichlin (2011). Now-casting and the real-time data flow. Oxford Handbook on Economic Forecasting, , 193–224. 3.3 Banbura, M., M. Modugno, and L. Reichlin (2012). Now-casting and the real-time data flow. Handnook of Economic Forecasting 2 . 3.2 Barnett, W., M. Chauvet, and D. Leiva-Leon (2016). Real-time nowcasting of nominal gdp with structural breaks. Journal of Econometrics 191 , 312–324. 1 Bragoli, D. and J. Fosten (2017). Nowcasting indian gdp. Oxford Bulletin of Economics and Statistics 80 (2), 259–282. 3.5 Bragoli, D., L. Metelli, and M. Modugno (2015). The importance of updating: Evidence from a brazilian now- casting model. OECD Journal: Journal of Business Cycle Measurement and Analysis 1 , 5–22. 3.5 Burns, A. and W. Mitchell (1946). Measuring Business Cycles . NBER Book Series Studies in Business Cycles. New York. 1 Camacho, M., G. Perez-Quiros, and P. Poncela (2018). Markov-switching dynamic factor models in real time. International Journal of Forecasting 34 (4), 598–611. 3.1 , 3.2 Camacho, M., G. P. Quiros, and P. Poncela (2014). Green shoots and double dips in the euro area: A real time measure. International Journal of Forecasting 30 (3), 520–535. 1 , 3.3 Carter, C. K. and R. Kohn (1994). On gibbs sampling for state space models. Biometrika 81 (3), 541–553. 2 , 1 , 3 , A.2 Cepni, O., E. Guney, and N. Swanson (2019a). Forecasting and nowcasting emerging market gdp growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors. Journal of Forecasting , Forthcoming.
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  • Fall '19
  • Economics, Recession, Late-2000s recession, GWI

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