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Tracking the German Business Cycle

Author

Listed:
  • Tino Berger

    (University of Goettingen)

  • Christian Ochsner

    (University of Goettingen)

Abstract

The German economy is an important economic driver in the Euro-area in terms of gross domestic product, labour force and international integration. We provide a state of the art estimate of the German output gap between 1995 and 2021 and present a nowcasting scheme that accurately predicts the Germany output gap up to three months prior to a gross domestic product data release. To this end, we elicit a mixed-frequency vector-autoregressive model in the spirit of Berger, Morley, and Wong (forthcoming) who propose to use monthly information to form an expectation about the current-quarter output gap. The mean absolute error of our nowcast is very small (0.25 percentage points) after only one month of observed data. Moreover, we show that international trade and labour market aggregates consistently explain large shares of variation in the German output gap.

Suggested Citation

  • Tino Berger & Christian Ochsner, 2022. "Tracking the German Business Cycle," MAGKS Papers on Economics 202212, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  • Handle: RePEc:mar:magkse:202212
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    File URL: https://www.uni-marburg.de/en/fb02/research-groups/economics/macroeconomics/research/magks-joint-discussion-papers-in-economics/papers/2022-papers/12-2022_berger.pdf
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    References listed on IDEAS

    as
    1. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.
    2. Günes Kamber & James Morley & Benjamin Wong, 2018. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 550-566, July.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    5. Berger, Tino & Richter, Julia & Wong, Benjamin, 2022. "A unified approach for jointly estimating the business and financial cycle, and the role of financial factors," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    6. Claudio BorioBy & Piti Disyatat & Mikael Juselius, 2017. "Rethinking potential output: embedding information about the financial cycle," Oxford Economic Papers, Oxford University Press, vol. 69(3), pages 655-677.
    7. Tino Berger & Paul David Boll & James Morley & Benjamin Wong, 2021. "Cyclical signals from the labor market," CAMA Working Papers 2021-91, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Eickmeier, Sandra, 2007. "Business cycle transmission from the US to Germany--A structural factor approach," European Economic Review, Elsevier, vol. 51(3), pages 521-551, April.
    9. Berger, Tino & Richter, Julia & Wong, Benjamin, 2022. "A unified approach for jointly estimating the business and financial cycle, and the role of financial factors," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    10. Karel Havik & Kieran Mc Morrow & Fabrice Orlandi & Christophe Planas & Rafal Raciborski & Werner Roeger & Alessandro Rossi & Anna Thum-Thysen & Valerie Vandermeulen, 2014. "The Production Function Methodology for Calculating Potential Growth Rates & Output Gaps," European Economy - Economic Papers 2008 - 2015 535, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    11. James D. Hamilton, 2018. "Why You Should Never Use the Hodrick-Prescott Filter," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 831-843, December.
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    Cited by:

    1. Morley, James & Rodríguez-Palenzuela, Diego & Sun, Yiqiao & Wong, Benjamin, 2023. "Estimating the euro area output gap using multivariate information and addressing the COVID-19 pandemic," European Economic Review, Elsevier, vol. 153(C).

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    More about this item

    Keywords

    output gap; Germany; nowcast; mixed frequency; vector-autoregression;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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