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Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations

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  • Pinkwart, Nicolas

Abstract

We present a comprehensive disaggregate approach for short-term forecasting economic activity in Germany by explicitly taking into account the supply or production side and the demand side of GDP. The GDP figures calculated by the two sides usually yield different results and the official GDP release is somewhere in between. We make use of this statistical procedure by separately modeling the two sides of GDP in a system of bridge equations at the most disaggregate level available and combining the resulting two aggregate GDP forecasts. Comparing several specification schemes in an out-of-sample forecast evaluation setup, we are able to find informative forecasts for most of the underlying GDP components. We then show first, that both approaches already yield informative aggregate forecasts for forecast horizons of up to 28 weeks and second, that combining the production side and the demand side projections substantially improves the forecast performance, in particular for the shorter forecast horizons.

Suggested Citation

  • Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdps:362018
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    Cited by:

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    2. Thomas B Götz & Klemens Hauzenberger, 2021. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 442-461.
    3. Tomas Adam & Filip Novotny, 2018. "Assessing the External Demand of the Czech Economy: Nowcasting Foreign GDP Using Bridge Equations," Working Papers 2018/18, Czech National Bank.
    4. Robert Lehmann & Magnus Reif, 2021. "Predicting the German Economy: Headline Survey Indices Under Test," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 215-232, November.
    5. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    6. de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
    7. Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
    8. Drudi, Francesco & Moench, Emanuel & Holthausen, Cornelia & Weber, Pierre-François & Ferrucci, Gianluigi & Setzer, Ralph & Adao, Bernardino & Dées, Stéphane & Alogoskoufis, Spyros & Téllez, Mar Delgad, 2021. "Climate change and monetary policy in the euro area," Occasional Paper Series 271, European Central Bank.
    9. Andreini, Paolo & Hasenzagl, Thomas & Reichlin, Lucrezia & Senftleben-König, Charlotte & Strohsal, Till, 2023. "Nowcasting German GDP: Foreign factors, financial markets, and model averaging," International Journal of Forecasting, Elsevier, vol. 39(1), pages 298-313.

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    More about this item

    Keywords

    German Economy; GDP; Disaggregation; Forecasting; Nowcasting; Bridge Equations;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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