[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117159-en":3,"doc-seo-117159-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117159,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Nowcasting world trade with machine learning: a three-step approach - Working Paper Series - No. 2836","The paper nowcasts world trade using machine learning, contrasting tree-based methods (random forest, gradient boosting) with regression-based counterparts (macroeconomic random forest, linear gradient boosting). Regression-based approaches deliver higher and more consistent performance than tree methods and a range of traditional benchmarks including OLS, Markov-switching, quantile regression, and PCA-OLS. A flexible three-step pipeline—pre-selection, factor extraction, and machine learning regression—improves predictive accuracy. The approach also scales seamlessly beyond world trade forecasting.","Chinn, Menzie David; Meunier, Baptiste; Stumpner, Sebastian  \nWorking Paper  \nNowcasting world trade with machine learning: A threestep approach  \nECB Working Paper, No. 2836  \nProvided in Cooperation with:  \nEuropean Central Bank (ECB)  \nSuggested Citation: Chinn, Menzie David; Meunier, Baptiste; Stumpner, Sebastian (2023) : Nowcasting world trade with machine learning: A three-step approach, ECB Working Paper, No. 2836, ISBN 978-92-899-6121-9, European Central Bank (ECB), Frankfurt a. M., [https://doi.org/10.2866/744676](https://doi.org/10.2866/744676)  \nThis Version is available at:  \n[https://hdl.handle.net/10419/278668](https://hdl.handle.net/10419/278668)  \nStandard-Nutzungsbedingungen:  \nDie Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.  \nSie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.  \nSofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.  \nTerms of use:  \nDocuments in EconStor maybe saved and copied foryour personal and scholarly purposes.  \nYou are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.  \nIf the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.  \nWorking Paper Series  \nMenzie Chinn, Baptiste Meunier, Sebastian Stumpner  \nNowcasting world trade with machine learning: a three-step approach  \nNo 2836  \nDisclaimer: This paper should not be reported as representing the views of the European Central Bank (ECB) . The views expressed are those of the authors and do not necessarily reflect those of the ECB.  \nAbstract  \nWe nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform not only the tree-based techniques, but also more “traditional” linear and non-linear techniques (OLS, Markov-switching, quantile regression). They do so significantly and consistently across different horizons and real-time datasets. To further improve performances when forecasting with machine learning, we propose a flexible three-step approach composed of (step 1) pre-selection, (step 2) factor extraction and (step 3) machine learning regression. We find that both pre-selection and factor extraction significantly improve the accuracy of machine-learning-based predictions. This three-step approach also outperforms workhorse benchmarks, such as a PCA-OLS model, an elastic net, or a dynamic factor model. Finally, on top of high accuracy, the approach is flexible and can be extended seamlessly beyond world trade.  \nKeywords: Forecasting, big data, large dataset, factor model, pre-selection  \nJEL classification: C53, C55, E37  \nNon-technical summary1  \nReal-time economic analysis often faces the fact that indicators are published with significant lags. This problem is encountered for world trade in volumes: the earliest indicator is published by the Dutch Centraal Plan Bureau (CPB) roughly eight weeks after month end – meaning that March 2023 data is available around May 25th. Since these data are widely used among economists, this poses a challenge policy-wise as decisions should rely on timely information about the current business cycle. In the meantime, a number of early indicators are available. The purpose of this paper is to exploit such informa","cbCainpOemUUM6hW","https://ap.wps.com/l/cbCainpOemUUM6hW","pdf",2071912,1,52,"English","en",105,"# Abstract\n# Non-technical summary\n## Data and motivation\n## Machine learning approach and results\n## Three-step forecasting framework","[{\"question\":\"What problem does the paper address in world trade analysis?\",\"answer\":\"Indicators for world trade volumes arrive with substantial publication lags, making timely policy-relevant assessment difficult. The paper aims to generate advanced estimates ahead of official releases.\"},{\"question\":\"How do the paper’s machine learning methods differ?\",\"answer\":\"It compares tree-based methods (random forest, gradient boosting) with regression-based techniques (macroeconomic random forest, linear gradient boosting). The regression-based methods perform better on the paper’s dataset.\"},{\"question\":\"What are the three steps in the proposed forecasting approach?\",\"answer\":\"Step 1 performs pre-selection of informative predictors; Step 2 extracts and orthogonalizes selected variables into a few factors; Step 3 uses machine learning regression on those factors.\"}]","Nowcasting world trade with machine learning: a three-step approach - Working Paper Series - No. 2836 | PDF",1785674168,131,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"nowcasting-world-trade-with-machine-learning-a-three-step-approach-working-paper-series-no-2836","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/nowcasting-world-trade-with-machine-learning-a-three-step-approach-working-paper-series-no-2836/117159/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address in world trade analysis?","Question",{"text":76,"@type":77},"Indicators for world trade volumes arrive with substantial publication lags, making timely policy-relevant assessment difficult. The paper aims to generate advanced estimates ahead of official releases.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do the paper’s machine learning methods differ?",{"text":81,"@type":77},"It compares tree-based methods (random forest, gradient boosting) with regression-based techniques (macroeconomic random forest, linear gradient boosting). 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