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Do Media Data Help to Predict German Industrial Production?

Author

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  • Dirk Ulbricht
  • Konstantin A. Kholodilin
  • Tobias Thomas

Abstract

Expectations form the basis of economic decisions of market participants in an uncertain world. Sentiment indicators reflect those expectations and thus have a proven track record for predicting economic variables. However, respondents of surveys perceive the world to a large extent with the help of media. So far, mainly very crude media information, such as word-count indices, has been used in the prediction of macroeconomic and financial variables. In this paper, we employ a rich data set provided by Media Tenor International, based on the sentiment analysis of all relevant media information in Germany from 2001 to 2014, whose results are 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. It is demonstrated that media data are indispensable when it comes to the prediction of German industrial production both for individual models and as a part of combined forecasts. They increase reliability by improving accuracy and reducing instability of the forecasts, particularly during the recent global financial crisis.
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Suggested Citation

  • Dirk Ulbricht & Konstantin A. Kholodilin & Tobias Thomas, 2017. "Do Media Data Help to Predict German Industrial Production?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 483-496, August.
    • Handle: RePEc:wly:jforec:v:36:y:2017:i:5:p:483-496
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    Cited by:

    1. 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..
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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).
    7. Kholodilin, Konstantin & Kolmer, Christian & Thomas, Tobias & Ulbricht, Dirk, 2015. "Asymmetric perceptions of the economy: Media, firms, consumers, and experts," DICE Discussion Papers 188, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    8. 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.
    9. 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).
    10. 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).
    11. 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.
    12. 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.
    13. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series 8639, CESifo.
    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. Berger, Johannes & Strohner, Ludwig & Thomas, Tobias, 2017. "Auswirkungen der Fluchtmigration auf Wachstum und Beschäftigung in Österreich," Policy Notes 13, EcoAustria – Institute for Economic Research.
    16. 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.
    17. 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.
    18. 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.
    19. 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.

    More about this item

    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

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