IDEAS home Printed from https://ideas.repec.org/a/zbw/zewwka/126016.html
   My bibliography  Save this article

Die Weltkonjunktur aus deutscher Sicht

Author

Listed:
  • Dick, Christian

Abstract

Deutschland ist als exportabhängiges Land in besonderer Weise den Schwankungen der Weltkonjunktur ausgesetzt. Konjunkturbeobachter haben deswegen nicht nur die Entwicklung im Inland, sondern ebenso in diversen anderen Ländern im Blick. Allerdings finden existierende Vorlaufindikatoren, die sich auf die Weltkonjunktur als Aggregat beziehen, in der deutschen Medienlandschaft nur wenig Beachtung. In dieser Studie werden die Prognoseeigenschaften eines "ZEW-Weltindikators" untersucht, der sich aus den monatlichen Antworten der ZEW-Finanzmarkttestumfrage zusammensetzt.

Suggested Citation

  • Dick, Christian, 2010. "Die Weltkonjunktur aus deutscher Sicht," ZEW Wachstums- und Konjunkturanalysen, ZEW - Leibniz Centre for European Economic Research, vol. 13(1), pages 8-9.
  • Handle: RePEc:zbw:zewwka:126016
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/126016/1/2010-01_3.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    2. 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.
    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. Faria, Gonçalo & Verona, Fabio, 2023. "Forecast combination in the frequency domain," Bank of Finland Research Discussion Papers 1/2023, Bank of Finland.
    2. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    3. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    4. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    5. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
    6. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    7. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    8. Heejoon Han & Myung D. Park, 2013. "Comparison of Realized Measure and Implied Volatility in Forecasting Volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 522-533, September.
    9. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.
    10. Abdullah Almansour and Margaret Insley, 2016. "The Impact of Stochastic Extraction Cost on the Value of an Exhaustible Resource: An Application to the Alberta Oil Sands," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    11. Pablo Pincheira B., 2014. "Predictive Evaluation of Sectoral and Total Employment Based on Entrepreneurial Confidence Indicators," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 17(1), pages 66-87, April.
    12. Sepideh Dolatabadi & Paresh Kumar Narayan & Morten Ørregaard Nielsen & Ke Xu, 2018. "Economic significance of commodity return forecasts from the fractionally cointegrated VAR model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(2), pages 219-242, February.
    13. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    14. Pablo Pincheira & Hernán Rubio, 2010. "The Low Predictive Power of Simple Phillips Curves in Chile: A Real-Time Evaluation," Working Papers Central Bank of Chile 559, Central Bank of Chile.
    15. Tanya Molodtsova & Alex Nikolsko-Rzhevskyy & David H. Papell, 2011. "Taylor Rules and the Euro," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43, pages 535-552, March.
    16. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    17. Robert Lehmann, 2021. "Forecasting exports across Europe: What are the superior survey indicators?," Empirical Economics, Springer, vol. 60(5), pages 2429-2453, May.
    18. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2018. "On The Sources Of Uncertainty In Exchange Rate Predictability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(1), pages 329-357, February.
    19. Lin, Hai & Tao, Xinyuan & Wu, Chunchi, 2022. "Forecasting earnings with combination of analyst forecasts," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 133-159.
    20. Rodrigo Fuentes & Fabián Gredig & Mauricio Larraín, 2007. "Estimating the Output Gap for Chile," Working Papers Central Bank of Chile 455, Central Bank of Chile.

    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:zbw:zewwka:126016. 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/zemande.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.