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Forecasting euro area variables with German pre-EMU data

Author

Listed:
  • Ralf Brüggemann

    (Lehrstuhl für Statistik und Ökonometrie, Universität Konstanz, Konstanz, Germany)

  • Helmut Lütkepohl

    (Department of Economics, European University Institute, Florence, Italy)

  • Massimiliano Marcellino

    (Bocconi University, Milan, Italy)

Abstract

It is investigated whether euro area variables can be forecast better based on synthetic time series for the pre-euro period or by using just data from Germany for the pre-euro period. Our forecast comparison is based on quarterly data for the period 1970Q1-2003Q4 for 10 macroeconomic variables. The years 2000-2003 are used as forecasting period. A range of different univariate forecasting methods is applied. Some of them are based on linear autoregressive models and we also use some nonlinear or time-varying coefficient models. It turns out that most variables which have a similar level for Germany and the euro area such as prices can be better predicted based on German data, while aggregated European data are preferable for forecasting variables which need considerable adjustments in their levels when joining German and European Monetary Union (EMU) data. These results suggest that for variables which have a similar level for Germany and the euro area it may be reasonable to consider the German pre-EMU data for studying economic problems in the euro area. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Ralf Brüggemann & Helmut Lütkepohl & Massimiliano Marcellino, 2008. "Forecasting euro area variables with German pre-EMU data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 465-481.
  • Handle: RePEc:jof:jforec:v:27:y:2008:i:6:p:465-481
    DOI: 10.1002/for.1064
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    References listed on IDEAS

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    Cited by:

    1. Bekiros, Stelios & Cardani, Roberta & Paccagnini, Alessia & Villa, Stefania, 2016. "Dealing with financial instability under a DSGE modeling approach with banking intermediation: A predictability analysis versus TVP-VARs," Journal of Financial Stability, Elsevier, vol. 26(C), pages 216-227.
    2. Anderson, Heather M. & Dungey, Mardi & Osborn, Denise R. & Vahid, Farshid, 2011. "Financial integration and the construction of historical financial data for the Euro Area," Economic Modelling, Elsevier, vol. 28(4), pages 1498-1509, July.
    3. Michael Ehrmann & Marcel Fratzscher & Roberto Rigobon, 2011. "Stocks, bonds, money markets and exchange rates: measuring international financial transmission," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 948-974, September.
    4. Bekiros Stelios & Paccagnini Alessia, 2015. "Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 107-136, April.
    5. Heather Anderson & Mardi Dungey & Denise Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," Money Macro and Finance (MMF) Research Group Conference 2006 99, Money Macro and Finance Research Group.
    6. Bekiros, Stelios D. & Paccagnini, Alessia, 2014. "Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 298-323.
    7. Amisano, Gianni & Fagan, Gabriel, 2013. "Money growth and inflation: A regime switching approach," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 118-145.
    8. Ralf Brüggemann & Jing Zeng, 2015. "Forecasting Euro-Area Macroeconomic Variables Using a Factor Model Approach for Backdating," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 22-39, February.
    9. Bekiros, Stelios D. & Paccagnini, Alessia, 2015. "Macroprudential Policy And Forecasting Using Hybrid Dsge Models With Financial Frictions And State Space Markov-Switching Tvp-Vars," Macroeconomic Dynamics, Cambridge University Press, vol. 19(07), pages 1565-1592, October.
    10. Helmut Lütkepohl, 2010. "Forecasting Aggregated Time Series Variables: A Survey," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-26.
    11. Stelios D. Bekiros & Alessia Paccagnini, 2016. "Policy‐Oriented Macroeconomic Forecasting with Hybrid DGSE and Time‐Varying Parameter VAR Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(7), pages 613-632, November.
    12. Gianni Amisano & Roberta Colavecchio, 2013. "Money Growth and Inflation: evidence from a Markov Switching Bayesian VAR," Macroeconomics and Finance Series 201304, Hamburg University, Department Wirtschaft und Politik.
    13. Jing Zeng, 2015. "Combining Country-Specific Forecasts when Forecasting Euro Area Macroeconomic Aggregates," Working Paper Series of the Department of Economics, University of Konstanz 2015-11, Department of Economics, University of Konstanz.
    14. Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 43(2), pages 415-444, May.
    15. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, vol. 45(1), pages 635-664, August.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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