[Estimation du commerce mondial en temps réel grâce à l’apprentissage automatique]"> [Estimation du commerce mondial en temps réel grâce à l’apprentissage automatique]"> [Estimati">
[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/bfr/bullbf/202324805.html
   My bibliography  Save this article

Nowcasting world trade in real time with machine learning
[Estimation du commerce mondial en temps réel grâce à l’apprentissage automatique]

Author

Listed:
  • Menzie Chinn
  • Baptiste Meunier
  • Sebastian Stumpner
Abstract
A key problem in economic assessment is that many time series arrive with long lags, posing a policy challenge. We address it for international trade in volumes by building a monthly “nowcast” (contemporaneous forecast). Using a dataset of 600 variables, our paper uses an innovative machine learning algorithm, the macroeconomic random forest – found to perform better than other linear and non-linear techniques. We employ a three-step approach composed of (i) variable pre-selection, (ii) factor extraction and (iii) machine learning regression. This approach delivers a substantially more accurate prediction compared to a Stock and Watson (2002) method based on factor extraction and OLS, with accuracy gains in between 15-30%. Compared to an autoregressive model, accuracy gains are around 30-40%. We illustrate the performance of the model during the Covid-19 pandemic. Un écueil majeur en économie réside dans les longs délais de publication de nombreux indicateurs, ce qui complique l’appréciation du cycle économique en temps réel. Pour y remédier, nous avons construit un « nowcast » (une estimation en temps réel) du commerce international. À partir d’une base de données de 600 variables, nous utilisons un nouvel algorithme d’apprentissage automatique, appelé « forêt aléatoire macroéconomique » (macroeconomic random forest), qui s’est avéré plus performant que d’autres techniques linéaires et non linéaires. Notre approche comporte trois étapes i) présélection des variables, ii) extraction des facteurs et iii) régression d’apprentissage automatique. Cette approche améliore la précision des prédictions (gain de 15 à 30% par rapport à la méthode en deux étapes de Stock et Watson (2002), et de 30 à 40% par rapport à un modèle autorégressif). Nous donnons des exemples de la performance du modèle pendant la pandémie de Covid-19.

Suggested Citation

  • Menzie Chinn & Baptiste Meunier & Sebastian Stumpner, 2023. "Nowcasting world trade in real time with machine learning [Estimation du commerce mondial en temps réel grâce à l’apprentissage automatique]," Bulletin de la Banque de France, Banque de France, issue 248.
    • Handle: RePEc:bfr:bullbf:2023:248:05
    as

    Download full text from publisher

    File URL: https://www.banque-france.fr/system/files/2023-11/823004_BDF248-5_EN_Nowcast_Vfinale.pdf
    Download Restriction: no
    File URL: https://www.banque-france.fr/system/files/2023-10/BDF248-5_Pr%C3%A9diction_web.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Barhoumi, Karim & Darné, Olivier & Ferrara, Laurent, 2016. "A World Trade Leading Index (WTLI)," Economics Letters, Elsevier, vol. 146(C), pages 111-115.
    3. Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," The World Economy, Wiley Blackwell, vol. 45(10), pages 3169-3191, October.
    4. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
    5. Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," Post-Print hal-04027843, HAL.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Poncela, Pilar, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Marie Bessec, 2013. "Short‐Term Forecasts of French GDP: A Dynamic Factor Model with Targeted Predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 500-511, September.
    3. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    4. Aastveit, Knut Are & Trovik, Tørres, 2014. "Estimating the output gap in real time: A factor model approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
    5. Filippo Ferroni & Benjamin Klaus, 2015. "Euro Area business cycles in turbulent times: convergence or decoupling?," Applied Economics, Taylor & Francis Journals, vol. 47(34-35), pages 3791-3815, July.
    6. Abberger, Klaus & Graff, Michael & Siliverstovs, Boriss & Sturm, Jan-Egbert, 2018. "Using rule-based updating procedures to improve the performance of composite indicators," Economic Modelling, Elsevier, vol. 68(C), pages 127-144.
    7. Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
    8. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
    9. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    10. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2009. "Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP," Economics Working Papers ECO2009/13, European University Institute.
    11. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    12. Mario Forni & Luca Gambetti, 2010. "Macroeconomic Shocks and the Business Cycle: Evidence from a Structural Factor Model," Center for Economic Research (RECent) 040, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    13. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
    14. Forni, Mario & Gambetti, Luca, 2010. "The dynamic effects of monetary policy: A structural factor model approach," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 203-216, March.
    15. Pilar Poncela & Esther Ruiz, 2016. "Small- Versus Big-Data Factor Extraction in Dynamic Factor Models: An Empirical Assessment," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 401-434, Emerald Group Publishing Limited.
    16. Fornaro, Paolo & Luomaranta, Henri & Saarinen, Lauri, 2017. "Nowcasting Finnish Turnover Indexes Using Firm-Level Data," ETLA Working Papers 46, The Research Institute of the Finnish Economy.
    17. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org, revised May 2023.
    18. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Non-Stationary Dynamic Factor Models for Large Datasets," Finance and Economics Discussion Series 2016-024, Board of Governors of the Federal Reserve System (U.S.).
    19. Breitung, Jörg & Eickmeier, Sandra, 2011. "Testing for structural breaks in dynamic factor models," Journal of Econometrics, Elsevier, vol. 163(1), pages 71-84, July.
    20. Rua, António, 2017. "A wavelet-based multivariate multiscale approach for forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bfr:bullbf:2023:248:05. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael brassart (email available below). General contact details of provider: https://edirc.repec.org/data/bdfgvfr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.