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Nowcasting Regional GDP: The Case of the Free State of Saxony

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  • Henzel Steffen R.

    (Ifo Institute – Leibniz-Institute for Economic Research at the University of Munich e.V., Poschingerstrasse 5, 81679 Munich, Germany, CESifo)

  • Lehmann Robert

    (Ifo Institute – Leibniz-Institute for Economic Research at the University of Munich e.V., Branch Dresden, Einsteinstrasse 3, 01069 Dresden, Germany)

  • Wohlrabe Klaus

    (Ifo Institute – Leibniz-Institute for Economic Research at the University of Munich e.V., Poschingerstrasse 5, 81679 Munich, Germany, CESifo)

Abstract

We tackle the nowcasting problem at the regional level, using a large set of indicators (regional, national and international) for the years 1998 to 2013. We explicitly take into account the ragged-edge data structure and consider the different information sets faced by a regional forecaster within each quarter. It appears that regional survey results in particular improve forecasting accuracy. Among the 10% best performing models for the short forecasting horizon, one fourth contain regional indicators. Hard indicators from the German manufacturing sector and the Composite Leading Indicator for Europe also deliver useful information for the prediction of regional GDP in Saxony. Unlike national GDP forecasts, the performance of regional GDP is similar across different information sets within a quarter.

Suggested Citation

  • 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.
    • Handle: RePEc:lus:reveco:v:66:y:2015:i:1:p:71-98
    DOI: 10.1515/roe-2015-0105
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    Cited by:

    1. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    2. Robert Lehmann & Klaus Wohlrabe, 2017. "Boosting and regional economic forecasting: the case of Germany," Letters in Spatial and Resource Sciences, Springer, vol. 10(2), pages 161-175, July.
    3. Robert Lehmann & Sascha Möhrle, 2024. "Forecasting regional industrial production with novel high‐frequency electricity consumption data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1918-1935, September.
    4. Lehmann, Robert & Wikman, Ida, 2022. "Quarterly GDP Estimates for the German States," MPRA Paper 112642, University Library of Munich, Germany.
    5. Robert Lehmann & Felix Leiss & Simon Litsche & Stefan Sauer & Michael Weber & Annette Weichselberger & Klaus Wohlrabe, 2019. "Mit den ifo-Umfragen regionale Konjunktur verstehen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(09), pages 45-49, May.
    6. 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.
    7. Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
    8. Pascal Bührig & Klaus Wohlrabe, 2016. "Forecasting revisions of German industrial production," Applied Economics Letters, Taylor & Francis Journals, vol. 23(15), pages 1062-1064, October.
    9. Valerij Gamukin, 2017. "Structural Change of Gross Regional Product in the Subjects of Ural Federal District," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(2), pages 410-421.
    10. Robert Lehmann, 2024. "A real-time regional accounts database for Germany with applications to GDP revisions and nowcasting," Empirical Economics, Springer, vol. 67(2), pages 817-838, August.
    11. Concha Artola & María Gil & Javier J. Pérez & Alberto Urtasun & Alejandro Fiorito & Diego Vila, 2018. "Monitoring the Spanish economy from a regional perspective: main elements of analysis," Occasional Papers 1809, Banco de España.
    12. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    13. María Gil & Danilo Leiva-Leon & Javier J. Pérez & Alberto Urtasun, 2019. "An application of dynamic factor models to nowcast regional economic activity in Spain," Occasional Papers 1904, Banco de España.
    14. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.
    15. Luca Barbaglia & Lorenzo Frattarolo & Niko Hauzenberger & Dominik Hirschbuehl & Florian Huber & Luca Onorante & Michael Pfarrhofer & Luca Tiozzo Pezzoli, 2024. "Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model," Papers 2401.10054, arXiv.org.
    16. Steffen Henzel & Robert Lehmann & Klaus Wohlrabe, 2015. "Die Machbarkeit von Kurzfristprognosen für den Freistaat Sachsen," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 22(04), pages 21-25, August.
    17. V. Gamukin V. & В. Гамукин В., 2018. "Управление структурой валового регионального продукта в субъектах Южного федерального округа // Managing the Gross Regional Product Structure in the Territorial Subjects of the Southern Federal Distri," Управленческие науки // Management Science, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 8(2), pages 18-29.
    18. Stefan Sauer & Michael Weber & Klaus Wohlrabe, 2018. "Das neue ifo Geschäftsklima Ostdeutschland und Sachsen: Hintergründe und Anpassungen," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 25(03), pages 20-24, June.
    19. Robert Lehmann & Klaus Wohlrabe, 2017. "Boosting and regional economic forecasting: the case of Germany," Letters in Spatial and Resource Sciences, Springer, vol. 10(2), pages 161-175, July.

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    More about this item

    Keywords

    nowcasting; regional gross domestic product; bridge equations; regional economic forecasting; mixed frequency; nowcasting; regional gross domestic product; bridge equations; regional economic forecasting; mixed frequency;
    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
    • 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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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