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Das ifo Importklima – ein erster Frühindikator für die Prognose der deutschen Importe

Author

Listed:
  • Christian Grimme
  • Robert Lehmann
  • Marvin Nöller

Abstract

Typischerweise zählen die Importe zu jenen Größen in der Konjunkturprognose, die die größten Prognosefehler aufweisen. Neben der erheblichen Volatilität der Importwachstumsraten ist dies dem Umstand geschuldet, dass bis dato kein bewährter Vorlaufindikator für die Importe Deutschlands vorliegt. In diesem Artikel wird ein erster Frühindikator, basierend auf Unternehmens- und Konsumentenbefragungen – das ifo Importklima –, für die Prognose der deutschen Importe vorgeschlagen. Das Importklima nutzt die Exporterwartungen der wichtigsten Handelspartner Deutschlands, um die deutsche Importnachfrage abzubilden. Ein Prognoseexperiment für das laufende und das kommende Quartal unterstreicht die Prognosegüte des ifo Importklimas, da es geringere Prognosefehler als andere Indikatoren, wie bspw. der Spezialhandel oder die Auftragseingänge, verursacht. Damit ist das ifo Importklima ein vielversprechender Indikator für die praktische Prognosearbeit.

Suggested Citation

  • 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.
    • Handle: RePEc:ces:ifosdt:v:71:y:2018:i:12:p:27-32
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    References listed on IDEAS

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    1. 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.
    2. Grimme, Christian & Lehmann, Robert & Noeller, Marvin, 2021. "Forecasting imports with information from abroad," Economic Modelling, Elsevier, vol. 98(C), pages 109-117.
    3. 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.
    4. 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.
    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. 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.
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    Cited by:

    1. 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.
    2. 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.

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    JEL classification:

    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • F10 - International Economics - - Trade - - - General

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