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Thinking Outside the Container: A Sparse Partial Least Squares Approach to Forecasting Trade Flows

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  • Stamer, Vincent

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  • Stamer, Vincent, 2022. "Thinking Outside the Container: A Sparse Partial Least Squares Approach to Forecasting Trade Flows," VfS Annual Conference 2022 (Basel): Big Data in Economics 264096, Verein für Socialpolitik / German Economic Association.
  • Handle: RePEc:zbw:vfsc22:264096
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    1. Giulia Brancaccio & Myrto Kalouptsidi & Theodore Papageorgiou, 2017. "Geography, Search Frictions and Endogenous Trade Costs," NBER Working Papers 23581, National Bureau of Economic Research, Inc.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Eickmeier, Sandra & Ng, Tim, 2011. "Forecasting national activity using lots of international predictors: An application to New Zealand," International Journal of Forecasting, Elsevier, vol. 27(2), pages 496-511, April.
    4. Sharat Ganapati & Woan Foong Wong & Oren Ziv, 2024. "Entrepôt: Hubs, Scale, and Trade Costs," American Economic Journal: Macroeconomics, American Economic Association, vol. 16(4), pages 239-278, October.
    5. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    6. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    7. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    8. Jörg Breitung & Malte Knüppel, 2021. "How far can we forecast? Statistical tests of the predictive content," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(4), pages 369-392, June.
    9. Mr. Serkan Arslanalp & Mr. Marco Marini & Ms. Patrizia Tumbarello, 2019. "Big Data on Vessel Traffic: Nowcasting Trade Flows in Real Time," IMF Working Papers 2019/275, International Monetary Fund.
    10. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    11. Ulltveit-Moe, Karen Helene & Heiland, Inga & Moxnes, Andreas & Zi, Yuan, 2019. "Trade From Space: Shipping Networks and The Global Implications of Local Shocks," CEPR Discussion Papers 14193, C.E.P.R. Discussion Papers.
    12. Grimme, Christian & Lehmann, Robert & Noeller, Marvin, 2021. "Forecasting imports with information from abroad," Economic Modelling, Elsevier, vol. 98(C), pages 109-117.
    13. Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2015. "Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 576-595, June.
    14. Oya Celasun & Mr. Niels-Jakob H Hansen & Ms. Aiko Mineshima & Mariano Spector & Jing Zhou, 2022. "Supply Bottlenecks: Where, Why, How Much, and What Next?," IMF Working Papers 2022/031, International Monetary Fund.
    15. Maximo Camacho & Gabriel Perez-Quiros, 2010. "Introducing the euro-sting: Short-term indicator of euro area growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 663-694.
    16. Mr. Diego A. Cerdeiro & Andras Komaromi & Yang Liu & Mamoon Saeed, 2020. "World Seaborne Trade in Real Time: A Proof of Concept for Building AIS-based Nowcasts from Scratch," IMF Working Papers 2020/057, International Monetary Fund.
    17. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    18. Hyonho Chun & Sündüz Keleş, 2010. "Sparse partial least squares regression for simultaneous dimension reduction and variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 3-25, January.
    19. Christian Grimme & Klaus Wohlrabe, 2014. "Die ifo Exporterwartungen – ein neuer Indikator zur Lage der Exportindustrie in Deutschland," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 67(23), pages 64-65, December.
    20. Theodore Papageorgiou & Myrto Kalouptsidi & Giulia Brancaccio, 2017. "Geography, Search Frictions and Trade Costs," 2017 Meeting Papers 1105, Society for Economic Dynamics.
    21. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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