IDEAS home Printed from https://ideas.repec.org/p/zbw/ifwkkb/41.html
   My bibliography  Save this paper

Deutsche Konjunktur im Frühjahr 2018 - Deutsche Wirtschaft näher am Limit
[German Economy Spring 2018 - German economy closer to its limit]

Author

Listed:
  • Ademmer, Martin
  • Boysen-Hogrefe, Jens
  • Fiedler, Salomon
  • Groll, Dominik
  • Hauber, Philipp
  • Jannsen, Nils
  • Kooths, Stefan
  • Potjagailo, Galina

Abstract

Die Luft für den Aufschwung in Deutschland wird dünner. Nach dem kräftigen Anstieg von 2,2 Prozent im Jahr 2017 dürfte sich die gesamtwirtschaftliche Produktion weiter beschleunigen. Im laufenden Jahr wird das Bruttoinlandsprodukt wohl um 2,5 Prozent zulegen; für das kommende Jahr rechnen wir mit einem Anstieg um 2,3 Prozent. Bei bereits spürbar über normal ausgelasteten Kapazitäten driftet die deutsche Wirtschaft zusehends in die Hochkonjunktur. Besonders bemerkbar machen sich Kapazitätsengpässe bereits in der Bauwirtschaft. Dort schaffen es die Unternehmen offenbar kaum noch, die eingehenden Aufträge abzuarbeiten. Vor diesem Hintergrund dürfte die Bautätigkeit trotz der äußerst anregenden Rahmenbedingungen vorerst nur noch verhalten zulegen, dafür aber die Baupreise spürbar anziehen. Anspannungen machen sich zunehmend auch auf dem Arbeitsmarkt bemerkbar. Zwar dürfte die Beschäftigung vorerst noch weiter kräftig steigen, allerdings werden die zunehmenden Probleme der Unternehmen, geeignetes Fachpersonal zu finden, zu beschleunigt steigenden Effektivverdiensten führen. In der Folge werden die Bruttolöhne und -gehälter kräftig steigen und so den privaten Konsum stimulieren, der durch Abgabensenkungen und Leistungsausweitungen der neuen Bundesregierung zusätzlich angeregt wird. Wir rechnen damit, dass diese fiskalischen Maßnahmen zu großen Teilen ab dem Jahr 2019 wirksam werden und wesentlich dazu beitragen, dass die Nettolöhne und -gehälter mit einer Zuwachsrate von 5,4 Prozent so stark zulegen werden wie seit dem Jahr 1992 nicht mehr. Bei der zunehmenden Auslastung der Produktionskapazitäten dürften sich mehr und mehr Unternehmen dazu veranlasst sehen, ihre Kapazitäten zu erweitern, so dass auch die Unternehmensinvestitionen merklich anziehen werden. Trotz der im Koalitionsvertrag vereinbarten zusätzlichen Haushaltsbelastungen dürften die Budgetüberschüsse vorerst in der Tendenz weiter aufwärts gerichtet bleiben, da die hohe konjunkturelle Dynamik deutliche Einnahmezuwächse mit sich bringt.

Suggested Citation

  • Ademmer, Martin & Boysen-Hogrefe, Jens & Fiedler, Salomon & Groll, Dominik & Hauber, Philipp & Jannsen, Nils & Kooths, Stefan & Potjagailo, Galina, 2018. "Deutsche Konjunktur im Frühjahr 2018 - Deutsche Wirtschaft näher am Limit [German Economy Spring 2018 - German economy closer to its limit]," Kieler Konjunkturberichte 41, Kiel Institute for the World Economy (IfW Kiel).
  • Handle: RePEc:zbw:ifwkkb:41
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/209453/1/kkb_41_2018-q1_deutschland.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Hinz, Julian, 2017. "Friendly fire: Zu den Handelsauswirkungen der Russlandsanktionen," Kiel Insight 2017.17, Kiel Institute for the World Economy (IfW Kiel).
    3. 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.
    4. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    5. Gordon, Robert J, 1988. "The Role of Wages in the Inflation Process," American Economic Review, American Economic Association, vol. 78(2), pages 276-283, May.
    6. 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.
    7. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016. "Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
    8. Chih-Ping Chang & Kenneth M. Emery, 1996. "Do wages help predict inflation?," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Q I, pages 2-9.
    9. Michael Biggs & Thomas Mayer & Andreas Pick, 2009. "Credit and economic recovery," DNB Working Papers 218, Netherlands Central Bank, Research Department.
    10. Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
    11. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    12. Jonas Dovern & Nils Jannsen, 2017. "Prognosen in verschiedenen Konjunkturphasen [Forecasts in Different Business Cycle States]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 97(7), pages 527-528, July.
    13. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    14. Blanchard, Oliver & Cerutti, Eugenio & SUmmers, Lawrence, 2015. "Inflation and Activity - Two Explorations and Their Monetary Policy Implications," Working Paper Series 15-070, Harvard University, John F. Kennedy School of Government.
    15. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2013. "Pooling Versus Model Selection For Nowcasting Gdp With Many Predictors: Empirical Evidence For Six Industrialized Countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 392-411, April.
    16. Carstensen, Kai & Wolters, Maik H., 2017. "Normaler Abschwung oder schwere Rezession? Ein neues Modell für die Prognose der Konjunkturphasen in Deutschland," Kiel Insight 2017.14, Kiel Institute for the World Economy (IfW Kiel).
    17. Dovern, Jonas & Jannsen, Nils, 2017. "Systematic errors in growth expectations over the business cycle," International Journal of Forecasting, Elsevier, vol. 33(4), pages 760-769.
    18. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fiedler, Salomon & Görg, Holger & Hornok, Cecília & Jannsen, Nils & Kooths, Stefan & Marchal, Léa & Potjagailo, Galina, 2018. "Direktinvestitionen im Ausland - Effekte auf die deutsche Leistungsbilanz und Spillovers in den Empfängerländern," Kieler Beiträge zur Wirtschaftspolitik 16, Kiel Institute for the World Economy (IfW Kiel).

    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. Cristea, R. G., 2020. "Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?," Cambridge Working Papers in Economics 20108, Faculty of Economics, University of Cambridge.
    2. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    3. Eraslan, Sercan & Schröder, Maximilian, 2019. "Nowcasting GDP with a large factor model space," Discussion Papers 41/2019, Deutsche Bundesbank.
    4. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    5. Jannsen, Nils, 2018. "Prognosen des IfW und tatsächliche Entwicklung 2017," Kiel Insight 2018.2, Kiel Institute for the World Economy (IfW Kiel).
    6. Ademmer, Martin & Boysen-Hogrefe, Jens & Fiedler, Salomon & Groll, Dominik & Jannsen, Nils & Kooths, Stefan & Potjagailo, Galina & Wolters, Maik H., 2017. "Deutsche Konjunktur im Herbst 2017 - Deutsche Wirtschaft nähert sich der Hochkonjunktur [German Economy Autumn 2017 - German economy approaches boom period]," Kieler Konjunkturberichte 35, Kiel Institute for the World Economy (IfW Kiel).
    7. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    8. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    9. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    10. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    11. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017. "Tracking the Slowdown in Long-Run GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
    12. Gehrke, Britta & Weber, Enzo, 2018. "Identifying asymmetric effects of labor market reforms," European Economic Review, Elsevier, vol. 110(C), pages 18-40.
    13. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 692-711.
    14. Jos Jansen & Jasper de Winter, 2016. "Improving model-based near-term GDP forecasts by subjective forecasts: A real-time exercise for the G7 countries," DNB Working Papers 507, Netherlands Central Bank, Research Department.
    15. Petrella, Ivan & Drechsel, Thomas & Antolin-Diaz, Juan, 2014. "Following the Trend: Tracking GDP when Long-Run Growth is Uncertain," CEPR Discussion Papers 10272, C.E.P.R. Discussion Papers.
    16. Christian Glocker & Philipp Wegmueller, 2020. "Business cycle dating and forecasting with real-time Swiss GDP data," Empirical Economics, Springer, vol. 58(1), pages 73-105, January.
    17. Ademmer, Martin & Boysen-Hogrefe, Jens & Fiedler, Salomon & Groll, Dominik & Hauber, Philipp & Jannsen, Nils & Kooths, Stefan & Potjagailo, Galina & Wolters, Maik H., 2018. "Deutsche Konjunktur im Sommer 2018 - Deutsche Wirtschaft: Luftloch im konjunkturellen Höhenflug [German Economy Summer 2018 - German economy: Temporary slowdown, boom not over yet]," Kieler Konjunkturberichte 44, Kiel Institute for the World Economy (IfW Kiel).
    18. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    19. Cem Cakmakli & Hamza Demircan, 2020. "Using Survey Information for Improving the Density Nowcasting of US GDP with a Focus on Predictive Performance during Covid-19 Pandemic," Koç University-TUSIAD Economic Research Forum Working Papers 2016, Koc University-TUSIAD Economic Research Forum.
    20. 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.

    More about this item

    Keywords

    Konsum; Prognosefehlerevaluierung; Konjunkturprognose; Stabilisierungspolitik; Frühindikatoren; Ausblick; Faktormodell;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zbw:ifwkkb:41. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/iwkiede.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.