Skip to main content
SpringerLink
Log in

A real-time regional accounts database for Germany with applications to GDP revisions and nowcasting

  • Published:
Empirical Economics Aims and scope Submit manuscript

Abstract

Accurate real-time macroeconomic data are essential for policy-making and economic nowcasting. The rising interest in analyses at the sub-national level cannot be served as such data are currently not available. In this paper, I introduce a real-time database for German regional economic accounts. The database contains real-time information for nine macroeconomic aggregates and the 16 German states. I conduct both a revision analysis and a nowcasting experiment for real gross domestic product. By pooling the states together, the first official estimates show no systematic revision errors. The pooling, however, suppresses the revision characteristics of single states. For half of the 16 German states I find that the first estimates are no optimal predictions, thus, leaving room for improvements in the future. The real-time nowcasts for real gross domestic product growth based on a mixed-frequency vector autoregression are very accurate and beat several benchmark models. More regional data would help to better inform the model, thereby increasing its nowcast performance even further.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data Availability

The real-time database will regularly be updated and is freely accessible at https://www.robertlehmann.net/data.

Notes

  1. Unfortunately, older publications with vintages before 2003 are, to the best of my knowledge, not accessible.

  2. In case of Norway, Helliesen et al. (2022) state that growth rates of real national accounts figures—with the exception of gross fixed capital formation—are unbiased, efficient and accurate.

  3. The German speaking blog can be accessed via the following permanent link: https://blog.wdr.de/landtagsblog/das-maerchen-vom-null-wachstum.

  4. Lehmann and Wohlrabe (2014) survey contributions prior to the year 2015.

  5. All details on regional accounts in Germany can be found at the working group’s homepage: https://www.statistikportal.de/de/vgrdl.

  6. The data can be accessed free of charge at: https://www.robertlehmann.net/data.

  7. The current, German-speaking version of the quality report can be accessed here: https://www.statistikportal.de/sites/default/files/2022-09/vgrdl_Qualitaetsbericht_2022.pdf.

  8. The revision reports for each general revision can be accessed here: https://www.statistikportal.de/de/vgrdl/methoden-und-informationen#revisionen.

  9. The vector \(y_\tau \) also includes exogenous predictors, but I focus on the relationship across GDP figures.

References

  • Andreini P, Hasenzagl T, Reichlin L, Senftleben-König C, Strohsal T (2023) Nowcasting German GDP: foreign factors, financial markets, and model averaging. Int J Forecast 39(1):298–313

    Article  Google Scholar 

  • Aruoba SB (2008) Data revisions are not well behaved. J Money Credit Bank 40(2/3):319–340

    Article  Google Scholar 

  • Barbaglia L, Frattarolo L, Hauzenberger N, Hirschbuehl D, Huber F, Onorante L, Pfarrhofer M, Tiozzo Pezzoli L (2024) Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model. arXiv:2401.10054v1 [econ.EM]

  • Bokun KO, Jackson LE, Kliesen KL, Qwyang MT (2023) FRED-SD: a real-time database for state-level data with forecasting applications. Int J Forecast 39(3):279–297

    Article  Google Scholar 

  • Chernis T, Cheung C, Velasco G (2020) A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth. Int J Forecast 36(3):851–872

    Article  Google Scholar 

  • Claudio JC, Heinisch K, Holtemöller O (2020) Nowcasting East German GDP growth: a MIDAS approach. Empir Econ 58(1):29–54

    Article  Google Scholar 

  • Croushore D (2011) Frontiers of real-time data analysis. J Econ Lit 49(1):72–100

    Article  Google Scholar 

  • Diebold FX, Mariano RS (1995) Comparing Predictive Accuracy. J Bus Econ Stat 13(3):253–263

    Google Scholar 

  • Döhrn R (2021) Zur Revisionspraxis der VGR der Länder. AStA Wirtschafts- und Sozialstatistisches Archiv 15(1):27–48

    Article  Google Scholar 

  • Gefang D, Koop G, Poon A (2020) Computationally efficient inference in large Bayesian mixed frequency VARs. Econ Lett 191:109120

    Article  MathSciNet  Google Scholar 

  • Gil M, Leiva-León D, Perez J, Urtasun A (2019) An application of dynamic factor models to nowcast regional economic activity in Spain. Banco de España Occasional Papers No. 1904

  • Harvey DI, Leybourne SJ, Newbold P (1997) Testing the equality of prediction mean squared errors. Int J Forecast 13(2):281–291

    Article  Google Scholar 

  • Helliesen MK, Hungnes H, Skjerpen T (2022) Revisions in the Norwegian National Accounts: accuracy, unbiasedness and efficiency in preliminary figures. Empir Econ 62(3):1079–1121

    Article  Google Scholar 

  • Henzel S, Lehmann R, Wohlrabe K (2015) Nowcasting regional GDP: the case of the free state of Saxony. Rev Econ 66(1):71–98

    Article  Google Scholar 

  • Kholodilin KA, Siliverstovs B, Kooths S (2008) A dynamic panel data approach to the forecasting of the GDP of German Länder. Spat Econ Anal 3(2):195–207

    Article  Google Scholar 

  • Kishor NK, Koenig EF (2012) VAR estimation and forecasting when data are subject to revision. J Bus Econ Stat 30(2):181–190

    MathSciNet  Google Scholar 

  • Koop G, McIntyre S, Mitchell J, Poon A (2020) Regional output growth in the United Kingdom: more timely and higher frequency estimates from 1970. J Appl Economet 35(2):176–197

    Article  MathSciNet  Google Scholar 

  • Koop G, McIntyre S, Mitchell J, Poon A (2022) Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates. Int J Forecast forthcoming

  • Kuck K, Schweikert K (2021) Forecasting Baden-Württemberg’s GDP growth: MIDAS regressions versus dynamic mixed-frequency factor models. J Forecast 40(5):861–882

    Article  MathSciNet  Google Scholar 

  • Lehmann R (2023) The forecasting power of the ifo business survey. J Bus Cycle Res 19(1):43–94

    Article  Google Scholar 

  • Lehmann R, Wikman I (2023) Quarterly GDP Estimates for the German States: New Data for Business Cycle Analyses and Long-Run Dynamics. CESifo Working Paper No. 10280

  • Lehmann R, Wohlrabe K (2014) Regional economic forecasting: state-of-the-art methodology and future challenges. Econ Bus Lett 3(4):218–231

    Article  Google Scholar 

  • Lehmann R, Wohlrabe K (2015) Forecasting GDP at the regional level with many predictors. German Econ Rev 16(2):226–254

    Article  Google Scholar 

  • Lehmann R, Wohlrabe K (2017) Boosting and regional economic forecasting: the case of Germany. Lett Spat Resour Sci 10(2):161–175

    Article  Google Scholar 

  • Lehmann R, Wollmershäuser T (2020) The macroeconomic projections of the German government: a comparison to an independent forecasting institution. German Econ Rev 21(2):235–270

    Article  Google Scholar 

  • Mariano RS, Murasawa Y (2003) A new coincident index of business cycles based on monthly and quarterly series. J Appl Economet 18(4):427–443

    Article  Google Scholar 

  • Mariano RS, Murasawa Y (2010) A coincident index, common factors, and monthly real GDP. Oxford Bull Econ Stat 72(1):27–46

    Article  Google Scholar 

  • Mitchell J, Smith RJ, Weale MR, Wright S, Salazar EL (2005) An indicator of monthly GDP and an early estimate of quarterly GDP growth. Econ J 115(501):F108–F129

    Article  Google Scholar 

  • Orphanides A (2001) Monetary policy rules based on real-time data. Am Econ Rev 91(4):964–985

    Article  Google Scholar 

  • Reif M (2022) Time-varying dynamics of the German business cycle: a comprehensive investigation. Oxford Bull Econ Stat 84(1):80–102

    Article  Google Scholar 

  • Schorfheide F, Song D (2015) Real-time forecasting with a mixed-frequency VAR. J Bus Econ Stat 33(3):366–380

    Article  MathSciNet  Google Scholar 

  • Strohsal T, Wolf E (2022) Data revisions to German national accounts: Are initial releases good nowcasts? Int J Forecast 36(4):1252–1259

    Article  Google Scholar 

Download references

Acknowledgements

I am very grateful to two anonymous referees for helpful comments and suggestions that clearly improved the quality of the paper. I wish to thank Jannik A. Nauerth and Niels Gillmann for providing me the raw data and Klaus Wohlrabe for important suggestions. I am also grateful to the Working Group Regional Accounts that authorized the publication of the data, provide important information especially on the revision process and very helpful comments on an earlier version of this paper.

Author information

Authors and Affiliations

  1. ifo Institute Munich and CESifo, Munich, Germany

    Robert Lehmann

Authors
  1. Robert Lehmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robert Lehmann.

Ethics declarations

Conflicts of interest

The authors have no relevant financial or non-financial interests to disclose. No funding was received for conducting this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 361 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lehmann, R. A real-time regional accounts database for Germany with applications to GDP revisions and nowcasting. Empir Econ (2024). https://doi.org/10.1007/s00181-024-02566-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00181-024-02566-3

Keywords

JEL Classification

Search

Navigation