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Forecasting Euro-Area Variables with German Pre-EMU Data

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  • Ralf Brueggemann
  • Helmut Luetkepohl
  • Massimiliano Marcellino

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 ten 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 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.

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Bibliographic Info

Paper provided by European University Institute in its series Economics Working Papers with number ECO2006/30.

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Date of creation: 2006
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Handle: RePEc:eui:euiwps:eco2006/30

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Keywords: Aggregation; forecasting; European monetary union; constructing EMU data;

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  1. Meese, Richard & Geweke, John, 1984. "A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 191-200, July.
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  3. Marcellino, Massimliano, 2004. "Forecasting EMU macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 20(2), pages 359-372.
  4. Ralf Brueggemann & Helmut Luetkepohl, 2004. "A Small Monetary System for the Euro Area Based on German Data," Economics Working Papers ECO2004/24, European University Institute.
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  8. Nautz, Dieter & Offermanns, Christian J., 2006. "Does the Euro follow the German Mark? Evidence from the monetary model of the exchange rate," European Economic Review, Elsevier, vol. 50(5), pages 1279-1295, July.
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Cited by:
  1. Ralf Brüggemann & Jing Zeng, 2012. "Forecasting Euro-Area Macroeconomic Variables Using a Factor Model Approach for Backdating," Working Paper Series of the Department of Economics, University of Konstanz 2012-15, Department of Economics, University of Konstanz.
  2. 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.
  3. Heather Anderson & Mardi Dungey & Denise R. Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," CAMA Working Papers 2007-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  4. 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.
  5. 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, 09.
  6. 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.
  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. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.

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