IDEAS home Printed from https://ideas.repec.org/p/zbw/dicedp/149.html
   My bibliography  Save this paper

Do media data help to predict German industrial production?

Author

Listed:
  • Kholodilin, Konstantin A.
  • Thomas, Tobias
  • Ulbricht, Dirk

Abstract

In an uncertain world, decisions by market participants are based on expectations. Thus, sentiment indicators reflecting expectations are proven at predicting economic variables. However, survey respondents largely perceive the world through media reports. Typically, crude media information, like word-count indices, is used in the prediction of macroeconomic and financial variables. Here, we employ a rich data set provided by Media Tenor International, based on sentiment analysis of opinion-leading media in Germany from 2001 to 2014, transformed into several monthly indices. German industrial production is predicted in a real-time out-of-sample forecasting experiment using more than 17,000 models formed of all possible combinations with a maximum of 3 out of 48 macroeconomic, survey, and media indicators. Media data are indispensable for the prediction of German industrial production both for individual models and as a part of combined forecasts, particularly during the global financial crisis.

Suggested Citation

  • Kholodilin, Konstantin A. & Thomas, Tobias & Ulbricht, Dirk, 2014. "Do media data help to predict German industrial production?," DICE Discussion Papers 149, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
  • Handle: RePEc:zbw:dicedp:149
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/98712/1/790217295.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Konstantin A. Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?: A Real-Time Evidence for the US," Discussion Papers of DIW Berlin 997, DIW Berlin, German Institute for Economic Research.
    2. Jan Grossarth-Maticek & Johannes Mayr, 2008. "Medienberichte als Konjunkturindikator," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 61(07), pages 17-29, April.
    3. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
    4. J. M. Keynes, 1937. "The General Theory of Employment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 51(2), pages 209-223.
    5. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    6. Ilaria Bordino & Stefano Battiston & Guido Caldarelli & Matthieu Cristelli & Antti Ukkonen & Ingmar Weber, 2012. "Web Search Queries Can Predict Stock Market Volumes," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-17, July.
    7. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
    8. Arthur T. Denzau & Douglass C. North, 1994. "Shared Mental Models: Ideologies and Institutions," Kyklos, Wiley Blackwell, vol. 47(1), pages 3-31, February.
    9. von Hayek, Friedrich August, 1989. "The Pretence of Knowledge," American Economic Review, American Economic Association, vol. 79(6), pages 3-7, December.
    10. Michael J. Lamla & Thomas Maag, 2012. "The Role of Media for Inflation Forecast Disagreement of Households and Professional Forecasters," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(7), pages 1325-1350, October.
    11. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    12. Matthias W. Uhl, 2010. "Explaining US Consumer Behavior and Expectations with News Sentiment," KOF Working papers 10-263, KOF Swiss Economic Institute, ETH Zurich.
    13. Konstantin Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?," KOF Working papers 10-256, KOF Swiss Economic Institute, ETH Zurich.
    14. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    15. Ammann, Manuel & Frey, Roman & Verhofen, Michael, 2012. "Do Newspaper Articles Predict Aggregate Stock Returns?," Working Papers on Finance 1204, University of St. Gallen, School of Finance.
    16. Matthias W. Uhl, 2011. "Nowcasting Private Consumption with TV Sentiment," KOF Working papers 11-293, KOF Swiss Economic Institute, ETH Zurich.
    17. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    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. Julia Wolfinger & Lars P. Feld & Ekkehard A. Köhler & Tobias Thomas, 2018. "57 Channels (And Nothin On) - Does TV-News on the Eurozone Affect Government Bond Yield Spreads?," CESifo Working Paper Series 7437, CESifo.
    2. Henzel Steffen R. & Lehmann Robert & Wohlrabe Klaus, 2015. "Nowcasting Regional GDP: The Case of the Free State of Saxony," Review of Economics, De Gruyter, vol. 66(1), pages 71-98, April.
    3. Bernhardt, Lea & Dewenter, Ralf & Thomas, Tobias, 2023. "Measuring partisan media bias in US newscasts from 2001 to 2012," European Journal of Political Economy, Elsevier, vol. 78(C).
    4. Berger, Johannes & Strohner, Ludwig & Thomas, Tobias, 2017. "Auswirkungen der Fluchtmigration auf Wachstum und Beschäftigung in Österreich," Policy Notes 13, EcoAustria – Institute for Economic Research.
    5. Benesch, Christine & Loretz, Simon & Stadelmann, David & Thomas, Tobias, 2019. "Media coverage and immigration worries: Econometric evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 160(C), pages 52-67.
    6. Ksenia Yakovleva, 2018. "Text Mining-based Economic Activity Estimation," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 26-41, December.
    7. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series 8639, CESifo.
    8. Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
    9. Ralf Dewenter & Uwe Dulleck & Tobias Thomas, 2020. "Does the 4th estate deliver? The Political Coverage Index and its application to media capture," Constitutional Political Economy, Springer, vol. 31(3), pages 292-328, September.
    10. Hirsch, Patrick & Feld, Lars P. & Köhler, Ekkehard A. & Thomas, Tobias, 2024. "“Whatever It Takes!” How tonality of TV-news affected government bond yield spreads during the European debt crisis," European Journal of Political Economy, Elsevier, vol. 82(C).
    11. Müller, Henrik & Hornig, Nico, 2020. ""I heard the News today, oh Boy": An updated Version of our Uncertainty Perception Indicator (UPI) – and some general thoughts on news-based economic indicators," DoCMA Working Papers 2-2020, TU Dortmund University, Dortmund Center for Data-based Media Analysis (DoCMA).
    12. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "News media versus FRED‐MD for macroeconomic forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 63-81, January.
    13. Konstantin A. Kholodilin & Christian Kolmer & Tobias Thomas & Dirk Ulbricht, 2015. "Asymmetric Perceptions of the Economy: Media, Firms, Consumers, and Experts," Discussion Papers of DIW Berlin 1490, DIW Berlin, German Institute for Economic Research.
    14. Benjamin Beckers & Konstantin A. Kholodilin & Dirk Ulbricht, 2017. "Reading between the Lines: Using Media to Improve German Inflation Forecasts," Discussion Papers of DIW Berlin 1665, DIW Berlin, German Institute for Economic Research.
    15. Bernhardt, Lea & Dewenter, Ralf & Thomas, Tobias, 2020. "Watchdog or loyal servant? Political media bias in US newscasts," DICE Discussion Papers 348, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    16. Hirsch, Patrick & Köhler, Ekkehard A. & Feld, Lars P. & Thomas, Tobias, 2020. ""Whatever it takes!": How tonality of TV-news affects government bond yield spreads during crises," Freiburg Discussion Papers on Constitutional Economics 20/9, Walter Eucken Institut e.V..
    17. Dorine Boumans & Henrik Müller & Stefan Sauer, 2022. "How Media Content Influences Economic Expectations: Evidence from a Global Expert Survey," ifo Working Paper Series 380, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    18. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    19. Yiqiao Chen & Elisabete A. Silva & José P. Reis, 2021. "Measuring policy debate in a regrowing city by sentiment analysis using online media data: A case study of Leipzig 2030," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(3), pages 675-692, June.

    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. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
    2. David Iselin & Boriss Siliverstovs, 2013. "Mit Zeitungen Konjunkturprognosen erstellen: Eine Vergleichsstudie für die Schweiz und Deutschland," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 7(3), pages 104-117, September.
    3. Giuseppe Garofalo, 2014. "Irreducible complexities: from Gödel and Turing to the paradigm of Imperfect Knowledge Economics," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3463-3474, November.
    4. Ashok Chakravarti, 2012. "Institutions, Economic Performance and the Visible Hand," Books, Edward Elgar Publishing, number 14751.
    5. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72, September.
    6. Lehmann Robert & Wohlrabe Klaus, 2015. "Forecasting GDP at the Regional Level with Many Predictors," German Economic Review, De Gruyter, vol. 16(2), pages 226-254, May.
    7. Semen Son Turan, 2014. "Internet Search Volume and Stock Return Volatility: The Case of Turkish Companies," Information Management and Business Review, AMH International, vol. 6(6), pages 317-328.
    8. Benjamin Beckers & Konstantin A. Kholodilin & Dirk Ulbricht, 2017. "Reading between the Lines: Using Media to Improve German Inflation Forecasts," Discussion Papers of DIW Berlin 1665, DIW Berlin, German Institute for Economic Research.
    9. David Dequech, 2008. "Varieties of uncertainty: a survey of the economic literature," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807211223070, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    10. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    11. Semen Son-Turan, 2016. "The Impact of Investor Sentiment on the "Leverage Effect"," International Econometric Review (IER), Econometric Research Association, vol. 8(1), pages 4-18, April.
    12. Schnellenbach, Jan & Schubert, Christian, 2015. "Behavioral political economy: A survey," European Journal of Political Economy, Elsevier, vol. 40(PB), pages 395-417.
    13. Gric, Zuzana & Ehrenbergerova, Dominika & Hodula, Martin, 2022. "The power of sentiment: Irrational beliefs of households and consumer loan dynamics," Journal of Financial Stability, Elsevier, vol. 59(C).
    14. Shubhankar Mohapatra & Nauman Ahmed & Paulo Alencar, 2020. "KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments," Papers 2003.04967, arXiv.org.
    15. Michael Ehrmann & Damjan Pfajfar & Emiliano Santoro, 2017. "Consumers' Attitudes and Their Inflation Expectations," International Journal of Central Banking, International Journal of Central Banking, vol. 13(1), pages 225-259, February.
    16. Chien-jung Ting & Yi-Long Hsiao, 2022. "Nowcasting the GDP in Taiwan and the Real-Time Tourism Data," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(3), pages 1-2.
    17. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    18. Rose, Andrew K. & Spiegel, Mark M., 2012. "Dollar illiquidity and central bank swap arrangements during the global financial crisis," Journal of International Economics, Elsevier, vol. 88(2), pages 326-340.
    19. Ding, Rong & Hou, Wenxuan & Liu, Yue (Lucy) & Zhang, John Ziyang, 2018. "Media censorship and stock price: Evidence from the foreign share discount in China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 112-133.
    20. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.

    More about this item

    Keywords

    forecast combination; media data; German industrial production; reliability index; R-word;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    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:dicedp:149. 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/diduede.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.