用多区域因素模型预测世界贸易

Nowcasting World TradeWith a Multi-RegionFactor Model Chris Jackson and Daniel Rivera Greenwood WP/26/48 IMF Working Papersdescribe research inprogress by the author(s) and are published toelicit comments and to encourage debate.The views expressed in IMF Working Papers are 2026MAR IMF Working Paper Research Department Prepared by Chris Jackson and Daniel Rivera Greenwood* Authorized for distribution by Rafael Portillo IMF Working Papersdescribe research in progress by the author(s) and are published to elicitcomments and to encourage debate.The views expressed in IMF Working Papers are those of the ABSTRACT:This paper presents a nowcasting model for global trade that allows for regional dynamics andspillovers. World trade growth is driven by common global factors but also regional trends. While existing tradenowcasting models have focused on the former, we allow for the latter using a dynamic factor model (DFM)with a multi-factor block structure. By directly modeling global trends, regional variation and spillovers, weimprove on the performance of standard trade nowcasting models, particularly periods characterized by WORKING PAPERS Nowcasting world trade with a multi-region factor model Prepared by Chris Jackson and Daniel Rivera Greenwood 1Introduction Trade is an important part of the global economy and viewed as a leading indicator of globalactivity. The ability to track global trade growth reliably in real time is therefore of keen interestto policymakers. Doing so, however, presents several challenges. Global trade data are publishedwith a lag. A variety of more timely relevant indicators exist to help forecast trade, but they are Dynamic Factor Models (DFMs) present a way to deal with these challenges. DFMs are a populartool for nowcasting macroeconomic data because they efficiently utilize information from large andvaried datasets. This is achieved by modeling the common dynamics of several series as explainedby a small number of latent factors (Stock and Watson 2016). They also have the advantage of As a result, the literature on nowcasting global trade has tended to focus on DFMs or related prin-cipal components analysis, including Guichard and Rusticelli 2011 and more recently Barhoumi etal. 2016 and Mart´ınez-Mart´ın and Rusticelli 2021. These papers model world trade as a function ofa common global factor, with Guichard and Rusticelli 2011 finding that this method outperforms This paper builds on this literature in using a multi-factor DFM to nowcast global trade growth.While global factors are an important determinant of world trade growth, the forecasting perfor-mance of a DFM can be improved by allowing for regional factors and spillovers. Specifically, amulti-factor block structure can account for variation due to a global cycle, regional fluctuationsand series-specific variation. As argued by Doz and Fuleky 2020, if these regional fluctuationswere not properly modeled it would appear either as weak common factors or errors that are cor- In methodology, this paper is closely related to Cascaldi-Garcia et al. 2024, who use a similarmulti-region DFM for nowcasting activity in the euro area as a whole and in its largest membercountries. It is also related to Kose et al. 2012 and Mumtaz and Musso 2021 who use a similar Recent literature has employed big data to construct real-time indicators of global trade. For in-stance, Arslanalp, Koepke, et al. 2021 utilizes AIS signals to monitor vessel activity, while Ar-slanalp, Choi, et al. 2025 extends this approach to nowcast global trade volumes. These methodolo-gies offer near-live insights, particularly valuable during periods of abrupt disruption. In contrast, We find that the nowcasting performance of a multi-region DFM improves on that of a single globalfactor model, as well as other statistical benchmarks. First, we present case studies to highlightthe advantages of a regional model. Our model outperforms a single-factor DFM during the Covidcontraction in 2020 and recovery in world trade in 2023. This reflects its ability to identify theimpact of regional trade shocks - particularly those from China - which diverge from the globaltrend. In contrast, it does not outperform a single-factor DFM during the Global Financial Crisisin 2008 as the slowdown was global and synchronous in nature. Second, we conduct a more sys-tematic pseudo out-of-sample forecasting exercise over two periods: the global trade slowdown in The model also highlights the regional sources of fluctuations in global trade. We use a gener-alized forecast error variance decomposition, developed by Pesaran and Shin 1998 and Dieboldand Yilmaz 2012, to analyze the spillovers between the global and regional factors in the model.The framework measures spillovers among the different factors. We find that the global and Asian 2Data Our main variable of interest is the index of the volume of global goods trade published by theNetherlands Bureau for Economic Policy