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UN Launches Real-Time Global Trade and GDP Monitor

Mike Schuler
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July 7, 2022

The UN’s trade body has launched a new site providing real-time estimates on global trade and GDP to show the impacts of the multiple global economic shocks in real-time.

The website, launched this month by the United Nations Conference on Trade and Development (UNCTAD), provides data-based estimates, which it calls “nowcasts,” to shed light on the continuing economic pressures of the war in Ukraine and the persistent strong inflationary concerns, offering timely information to help guide policy responses.

UNCTAD has long produced nowcasts on the global quarterly growth of merchandise exports, providing a timely picture of the development of global trade since the start of the COVID-19 pandemic, but the fact that they are disseminated via end-of-quarter bulletins has been limiting.

To generate the real-time estimates, UNCTAD says the latest data available and data revisions are fed into models on a weekly basis to update and revise the nowcasts and provide insight on current economic and trade conditions well before final figures are published with several months’ delay.

“The estimates are drawn from models for UNCTAD’s three trade series covering global merchandise exports expressed in values and volumes and services exports. And for the first time – the estimates include global annual GDP growth,” UNCTAD explains.

“Besides the estimates, the website displays the nowcasts’ development over time, showing how data releases have shaped them as the economic situation has evolved.”

According to UNCTAD, models behind the nowcasts are known as long short-term memory artificial neural networks (LSTM), which outperform its own previous dynamic factor model approach.

More about the methodology and information on the accompanying open-source, multi-programming language library are available in an UNCTAD research paper. A second UNCTAD research paper details the methodology’s performance during the pandemic, while a third research paper compares its performance to other common nowcasting and machine learning methodologies.

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