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Early estimates of euro area real GDP growth: a bottom up approach from the production side

Author

Listed:
  • Hahn, Elke
  • Skudelny, Frauke

Abstract

This paper derives forecasts for euro area real GDP growth based on a bottom up approach from the production side. That is, GDP is forecast via the forecasts of value added across the different branches of activity, which is quite new in the literature. Linear regression models in the form of bridge equations are applied. In these models earlier available monthly indicators are used to bridge the gap of missing GDP data. The process of selecting the best performing equations is accomplished as a pseudo real time forecasting exercise, i.e. due account is taken of the pattern of available monthly variables over the forecast cycle. Moreover, by applying a very systematic procedure the best performing equations are selected from a pool of thousands of test bridge equations. Our modelling approach, finally, includes a further novelty which should be of particular interest to practitioners. In practice, forecasts for a particular quarter of GDP generally spread over a prolonged period of several months. We explore whether over this forecast cycle, where GDP is repeatedly forecast, the same set of equations or different ones should be used. Changing the set of bridge equations over the forecast cycle could be superior to keeping the same set of equations, as the relative merit of the included monthly indictors may shift over time owing to differences in their data characteristics. Overall, the models derived in this forecast exercise clearly outperform the benchmark models. The variables selected in the best equations for different situations over the forecast cycle vary substantially and the achieved results confirm the conjecture that allowing the variables in the bridge equations to differ over the forecast cycle can lead to substantial improvements in the forecast accuracy. JEL Classification: C22, C52, C53, E27

Suggested Citation

  • Hahn, Elke & Skudelny, Frauke, 2008. "Early estimates of euro area real GDP growth: a bottom up approach from the production side," Working Paper Series 975, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:2008975
    Note: 854549
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    Citations

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    Cited by:

    1. Robert Lehmann, 2016. "Wirtschaftswachstum und Konjunkturprognosen auf regionaler Ebene," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    2. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    3. C. Thubin & T. Ferrière & E. Monnet & M. Marx & V. Oung, 2016. "The PRISME model: can disaggregation on the production side help to forecast GDP?," Working papers 596, Banque de France.
    4. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    5. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methoden der ifo-Kurzfristprognose," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    6. Robert Lehmann & Klaus Wohlrabe, 2014. "Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 34(1), pages 61-90, February.
    7. Martin Feldkircher & Florian Huber & Josef Schreiner & Julia Woerz & Marcel Tirpak & Peter Toth, 2015. "Small-scale nowcasting models of GDP for selected CESEE countries," Working and Discussion Papers WP 4/2015, Research Department, National Bank of Slovakia.
    8. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    9. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    10. Mahmut Gunay, 2016. "Forecasting Turkish GDP Growth : Bottom-Up vs Direct?," CBT Research Notes in Economics 1622, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    11. Christian Grimme & Robert Lehmann & Marvin Noeller, 2019. "Forecasting Imports with Information from Abroad," ifo Working Paper Series 294, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    12. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    13. Christian Grimme & Robert Lehmann & Marvin Noeller, 2018. "Forecasting Imports with Information from Abroad," CESifo Working Paper Series 7079, CESifo Group Munich.
    14. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    15. Martin Feldkircher & Florian Huber & Josef Schreiner & Marcel Tirpák & Peter Tóth & Julia Wörz, 2015. "Bridging the information gap: small-scale nowcasting models of GDP growth for selected CESEE countries," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 56-75.
    16. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    17. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland, Institute for Economies in Transition.
    18. Bulligan, Guido & Marcellino, Massimiliano & Venditti, Fabrizio, 2015. "Forecasting economic activity with targeted predictors," International Journal of Forecasting, Elsevier, vol. 31(1), pages 188-206.

    More about this item

    Keywords

    bottom up approach; bridge equations; euro area; forecasting; GDP;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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