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Forecasting gross value-added at the regional level: are sectoral disaggregated predictions superior to direct ones?

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  • Robert Lehmann

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  • Klaus Wohlrabe

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Abstract

In this paper, we ask whether it is possible to forecast gross value-added (GVA) and its sectoral sub-components at the regional level. With an autoregressive distributed lag model we forecast total and sectoral GVA for one German state (Saxony) with more than 300 indicators from different regional levels (international, national and regional) and additionally make usage of several forecast pooling strategies and factor models. Our results show that we are able to increase forecast accuracy of GVA for every sector and for all forecast horizons (one up to four quarters) compared to an autoregressive process. Finally, we show that sectoral forecasts contain more information in the short term (one quarter), whereas direct forecasts of total GVA are preferable in the medium (two and three quarters) and long term (four quarters). Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • 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.
  • Handle: RePEc:spr:jahrfr:v:34:y:2014:i:1:p:61-90
    DOI: 10.1007/s10037-013-0083-8
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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. Catalina MOTOFEI, 2017. "Sectorial evolutions in former communist economies, current EU members," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 15(146), pages 266-266.
    3. Henzel Steffen R. & Wohlrabe Klaus & Lehmann Robert, 2015. "Nowcasting Regional GDP: The Case of the Free State of Saxony," Review of Economics, De Gruyter, vol. 66(1), pages 71-98, April.
    4. 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.
    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, 2020. "The Forecasting Power of the ifo Business Survey," CESifo Working Paper Series 8291, CESifo.
    7. Federico Lampis, 2016. "Forecasting the sectoral GVA of a small Spanish region," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 38-44.
    8. RobertLehmann & KlausWohlrabe, 2013. "Sektorale Prognosen und deren Machbarkeit auf regionaler Ebene – Das Beispiel Sachsen," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 20(04), pages 22-29, August.
    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. Christian Grimme & Robert Lehmann & Marvin Noeller, 2018. "Forecasting Imports with Information from Abroad," CESifo Working Paper Series 7079, CESifo.
    11. Christian Seiler & Klaus Wohlrabe, 2013. "Das ifo Geschäftsklima und die deutsche Konjunktur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.
    12. 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;Occasional Papers Homepage.
    13. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    14. 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.
    15. 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.

    More about this item

    Keywords

    Regional forecasting; Gross value-added; Forecast combination; Disaggregated forecasts; Factor models; C32; C52; C53; E37; R11;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • 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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