Keeping track of global trade in real time
Jaime Martinez-Martin () and
Elena Rusticelli ()
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Elena Rusticelli: OECD
No 2019, Working Papers from Banco de España
This paper builds an innovative composite world trade cycle index (WTI) by means of a dynamic factor model to perform short-term forecasts of world trade growth of both goods and (usually neglected) services. The selection of trade indicator series is made using a multidimensional approach, including Bayesian model averaging techniques, dynamic correlations and Granger non-causality tests in a linear VAR framework. To overcome the real-time forecasting challenges, the dynamic factor model is extended to account for mixed frequencies, to deal with asynchronous data publication and to include hard and survey data along with leading indicators. Nonlinearities are addressed with a Markov switching model. In the empirical application, simulations analysis in pseudo real-time suggest that: i) the global trade index is a very useful tool for tracking and forecasting world trade in real time; ii) the model is able to infer global trade cycles very precisely and better than several competing alternatives; and iii) global trade finance conditions seem to lead the trade cycle, in line with the theoretical literature.
Keywords: real-time forecasting; world trade; dynamic factor models; markov switching models (search for similar items in EconPapers)
JEL-codes: C22 E27 E32 (search for similar items in EconPapers)
Pages: 32 pages
New Economics Papers: this item is included in nep-int, nep-mac and nep-ore
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https://www.bde.es/f/webbde/SES/Secciones/Publicac ... 20/Files/dt2019e.pdf First version, July 2020 (application/pdf)
Journal Article: Keeping track of global trade in real time (2021)
Working Paper: Keeping track of global trade in real time (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:bde:wpaper:2019
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