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The IWH Forecasting Dashboard: From Forecasts to Evaluation and Comparison

Author

Listed:
  • Heinisch Katja

    (Halle Institute for Economic Research (IWH), Halle, Germany)

  • Behrens Christoph

    (Freie und Hansestadt Hamburg, Hamburg, Germany)

  • Döpke Jörg

    (Hochschule Merseburg, Merseburg, Germany)

  • Foltas Alexander

    (Helmut-Schmidt-Universität Hamburg, Hamburg, Germany)

  • Fritsche Ulrich

    (Universität Hamburg, Hamburg, Germany)

  • Köhler Tim

    (Hochschule Merseburg, Merseburg, Germany)

  • Müller Karsten

    (Deutsches Zentrum für Luft- und Raumfahrt, Institut für Vernetzte Energiesysteme, Stuttgart, Germany)

  • Puckelwald Johannes

    (Deutsches Maritimes Zentrum Hamburg, Hamburg, Germany)

  • Reichmayr Hannes

    (Martin-Luther-Universität Halle-Wittenberg, Halle, Germany)

Abstract

The paper describes the “Halle Institute for Economic Research (IWH) Forecasting Dashboard (ForDas)”. This tool aims at providing, on a non-commercial basis, historical and actual macroeconomic forecast data for the Germany economy to researchers and interested audiences. The database renders it possible to directly compare forecast quality across selected institutions and over time. It is partly based on data collected in the DFG-funded project “Macroeconomic forecasts in great crisis”.

Suggested Citation

  • Heinisch Katja & Behrens Christoph & Döpke Jörg & Foltas Alexander & Fritsche Ulrich & Köhler Tim & Müller Karsten & Puckelwald Johannes & Reichmayr Hannes, 2024. "The IWH Forecasting Dashboard: From Forecasts to Evaluation and Comparison," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 244(3), pages 277-288, June.
    • Handle: RePEc:jns:jbstat:v:244:y:2024:i:3:p:277-288:n:3
    DOI: 10.1515/jbnst-2023-0011
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    References listed on IDEAS

    as
    1. Stark, Tom & Croushore, Dean, 2002. "Forecasting with a real-time data set for macroeconomists," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 507-531, December.
    2. Birger Antholz, 2006. "Geschichte der quantitativen Konjunkturprognose-Evaluation in Deutschland," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 75(2), pages 12-33.
    3. Christoph Behrens & Christian Pierdzioch & Marian Risse, 2020. "Do German economic research institutes publish efficient growth and inflation forecasts? A Bayesian analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(4), pages 698-723, March.
    4. Alexander Foltas & Christian Pierdzioch, 2022. "On the efficiency of German growth forecasts: an empirical analysis using quantile random forests and density forecasts," Applied Economics Letters, Taylor & Francis Journals, vol. 29(17), pages 1644-1653, October.
    5. Engelke, Carola & Heinisch, Katja & Schult, Christoph, 2019. "How forecast accuracy depends on conditioning assumptions," IWH Discussion Papers 18/2019, Halle Institute for Economic Research (IWH).
    6. Döpke, Jörg & Müller, Karsten & Tegtmeier, Lars, 2018. "The economic value of business cycle forecasts for potential investors – Evidence from Germany," Research in International Business and Finance, Elsevier, vol. 46(C), pages 445-461.
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    More about this item

    Keywords

    forecasting; macroeconomic data;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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