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Forecasting imports with information from abroad

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  • Grimme, Christian
  • Lehmann, Robert
  • Noeller, Marvin

Abstract

Globalization has led to huge increases in import volumes. Since imports fluctuate heavily over time, they are difficult to forecast and reliable leading indicators are needed. Our paper introduces the first leading indicator to forecast import growth, the Import Climate. While surveys are an often-used source for leading indicators, these data typically do not include information about expected import demand of firms and households. Therefore, our approach builds on the idea that import demand of the domestic country should be reflected in the expected export developments of its main trading partners, which can be measured by standard surveys. We show for six advanced economies that the Import Climate outperforms standard business cycle indicators that mainly reflect domestic demand. Thus, the Import Climate is a reliable tool for import forecasting for both academics and policymakers.

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  • Grimme, Christian & Lehmann, Robert & Noeller, Marvin, 2021. "Forecasting imports with information from abroad," Economic Modelling, Elsevier, vol. 98(C), pages 109-117.
  • Handle: RePEc:eee:ecmode:v:98:y:2021:i:c:p:109-117
    DOI: 10.1016/j.econmod.2021.02.013
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    1. Katja Drechsel & Laurent Maurin, 2011. "Flow of conjunctural information and forecast of euro area economic activity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 336-354, April.
    2. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    4. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2009. "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 595-611.
    5. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    6. Robert Lehmann, 2021. "Forecasting exports across Europe: What are the superior survey indicators?," Empirical Economics, Springer, vol. 60(5), pages 2429-2453, May.
    7. Heinisch Katja & Scheufele Rolf, 2019. "Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, De Gruyter, vol. 20(4), pages 170-200, December.
    8. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    9. José Manuel Campa & Linda S. Goldberg, 2005. "Exchange Rate Pass-Through into Import Prices," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 679-690, November.
    10. Barhoumi, Karim, 2006. "Differences in long run exchange rate pass-through into import prices in developing countries: An empirical investigation," Economic Modelling, Elsevier, vol. 23(6), pages 926-951, December.
    11. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    12. 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.
    13. Antonello D’Agostino & Michele Modugno & Chiara Osbat, 2017. "A Global Trade Model for the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 13(4), pages 1-34, December.
    14. Carmine Pappalardo & Gianfranco Piras, 2004. "Vector-Autoregression Approach to Forecast Italian Imports," ISAE Working Papers 42, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    15. Heinrich, Markus & Carstensen, Kai & Reif, Magnus & Wolters, Maik, 2017. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model. An Application to the German Business Cycle," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168206, Verein für Socialpolitik / German Economic Association.
    16. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 10, pages 515-554, Elsevier.
    17. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    18. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    19. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    20. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    21. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    22. Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
    23. Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
    24. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    25. Esteves, Paulo Soares, 2013. "Direct vs bottom–up approach when forecasting GDP: Reconciling literature results with institutional practice," Economic Modelling, Elsevier, vol. 33(C), pages 416-420.
    26. Mallick, Sushanta & Marques, Helena, 2012. "Pricing to market with trade liberalization: The role of market heterogeneity and product differentiation in India’s exports," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 310-336.
    27. Döhrn Roland & Schmidt Christoph M., 2011. "Information or Institution?: On the Determinants of Forecast Accuracy," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 9-27, February.
    28. Sandra Hanslin Grossmann & Rolf Scheufele, 2019. "PMIs: Reliable indicators for exports?," Review of International Economics, Wiley Blackwell, vol. 27(2), pages 711-734, May.
    29. Kilian, Lutz & Rebucci, Alessandro & Spatafora, Nikola, 2009. "Oil shocks and external balances," Journal of International Economics, Elsevier, vol. 77(2), pages 181-194, April.
    30. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    31. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    32. Campa, Jose Manuel & Gonzalez Minguez, Jose M., 2006. "Differences in exchange rate pass-through in the euro area," European Economic Review, Elsevier, vol. 50(1), pages 121-145, January.
    33. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    34. Steffen Elstner & Christian Grimme & Ulrich Haskamp, 2013. "Das ifo Exportklima – ein Frühindikator für die deutsche Exportprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(04), pages 36-43, March.
    35. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    36. Hahn, Elke & Skudelny, Frauke, 2008. "Early estimates of euro area real GDP growth: a bottom up approach from the production side," Working Paper Series 975, European Central Bank.
    37. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    38. Giuseppe Parigi & Roberto Golinelli, 2007. "The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 77-94.
    39. Dr. Sandra Hanslin Grossmann & Dr. Rolf Scheufele, 2016. "Foreign PMIs: A reliable indicator for exports?," Working Papers 2016-01, Swiss National Bank.
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    Cited by:

    1. Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
    2. Christian Grimme & Robert Lehmann & Marvin Nöller, 2018. "Das ifo Importklima – ein erster Frühindikator für die Prognose der deutschen Importe," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(12), pages 27-32, June.
    3. Robert Lehmann, 2021. "Forecasting exports across Europe: What are the superior survey indicators?," Empirical Economics, Springer, vol. 60(5), pages 2429-2453, May.
    4. 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.
    5. Behrens, Christoph, 2019. "Evaluating the Joint Efficiency of German Trade Forecasts. A nonparametric multivariate approach," Working Papers 9, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    6. Christoph Behrens, 2019. "A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts under Flexible Loss," Economies, MDPI, vol. 7(3), pages 1-23, September.
    7. Christian Grimme & Robert Lehmann, 2020. "The ifo Export Climate – A Leading Indicator to Forecast German Export Growth," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 20(04), pages 36-42, January.
    8. Timo Wollmershäuser & Silvia Delrio & Marcell Göttert & Christian Grimme & Jochen Güntner & Carla Krolage & Stefan Lautenbacher & Robert Lehmann & Sebastian Link & Wolfgang Nierhaus & Magnus Reif & Ra, 2018. "ifo Konjunkturprognose Sommer 2018: Gewitterwolken am deutschen Konjunkturhimmel," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(12), pages 33-87, June.
    9. Behrens, Christoph, 2020. "German trade forecasts since 1970: An evaluation using the panel dimension," Working Papers 26, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    10. Stamer, Vincent, 2021. "Thinking outside the container: A machine learning approach to forecasting trade flows," Kiel Working Papers 2179, Kiel Institute for the World Economy (IfW Kiel).

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    More about this item

    Keywords

    Import climate; Import forecasting; Survey data; Price competitiveness;
    All these keywords.

    JEL classification:

    • F01 - International Economics - - General - - - Global Outlook
    • F10 - International Economics - - Trade - - - General
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation

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