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Instability and non-linearity in the EMU


  • Massimiliano Marcellino


In this paper we evaluate the relative performance of linear, non-linear and time-varying models for about 500 macroeconomic variables for the countries in the Euro area, using real-time forecasting methodology. It turns out that linear models work well for about 35% of the series under analysis, time-varying models for another 35% and on-linear models for the remaining 30% of the series. The gains in forecasting accuracy from the choice of the best model can be substantial, in particular for longer forecast horizons.These results emerge from a detailed is aggregated analysis, while they are hidden when an average loss function is used. To explore in more detail the issue of parameter instability, we then apply a battery of tests, detecting non-constancy in about 20-30% of the time series. For these variables the forecasting performance of the time-varying and non-linear models further improves, with larger gains for a larger fraction of the series. Finally, we evaluate whether non-linear models perform better for three key macroeconomic variables: industrial production, inflation and unemployment. It turns out that this is often the case. Hence, overall, our results indicate that there is a substantial amount of instability and non-linearity in the EMU, and suggest that it can be worth going beyond linear models for several EMU macroeconomic variables.

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  • Massimiliano Marcellino, "undated". "Instability and non-linearity in the EMU," Working Papers 211, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  • Handle: RePEc:igi:igierp:211

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    Cited by:

    1. Marcellino, Massimliano, 2004. "Forecasting EMU macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 20(2), pages 359-372.
    2. D van Dijk & D R Osborn & M Sensier, 2002. "Changes in variability of the business cycle in the G7 countries," The School of Economics Discussion Paper Series 0204, Economics, The University of Manchester.
    3. repec:emu:wpaper:dp15-01.pdf is not listed on IDEAS
    4. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen Miller, 2013. "Forecasting Nevada gross gaming revenue and taxable sales using coincident and leading employment indexes," Empirical Economics, Springer, vol. 44(2), pages 387-417, April.
    5. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    6. Clark, Todd E. & McCracken, Michael W., 2006. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1127-1148, August.
    7. McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
    8. Massimiliano Marcellino, "undated". "Forecast pooling for short time series of macroeconomic variables," Working Papers 212, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
    10. Habrov, Vladimir, 2012. "Optimization of portfolio management based on vector autoregression models and multivariate volatility models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 28(4), pages 35-62.
    11. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.
    12. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, Elsevier.
    13. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    14. Carlo A. Favero & Massimiliano Marcellino, 2005. "Modelling and Forecasting Fiscal Variables for the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 755-783, December.
    15. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City.
    16. Moosa, Imad & Burns, Kelly, 2014. "The unbeatable random walk in exchange rate forecasting: Reality or myth?," Journal of Macroeconomics, Elsevier, vol. 40(C), pages 69-81.
    17. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Clark, Todd E. & McCracken, Michael W., 2005. "The power of tests of predictive ability in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 124(1), pages 1-31, January.
    19. Burns, Kelly & Moosa, Imad A., 2015. "Enhancing the forecasting power of exchange rate models by introducing nonlinearity: Does it work?," Economic Modelling, Elsevier, vol. 50(C), pages 27-39.
    20. Enders, Walter & Pascalau, Razvan, 2015. "Pretesting for multi-step-ahead exchange rate forecasts with STAR models," International Journal of Forecasting, Elsevier, vol. 31(2), pages 473-487.
    21. A.H.J. den Reijer & P.J.G. Vlaar, 2003. "Forecasting Inflation in the Netherlands and the Euro Area," WO Research Memoranda (discontinued) 723, Netherlands Central Bank, Research Department.
    22. Agostino Consolo, 2006. "Forecasting measures of inflation for the Estonian economy," Bank of Estonia Working Papers 2006-03, Bank of Estonia, revised 12 Nov 2006.
    23. Ullrich Heilemann & Herman Stekler, 2010. "Perspectives on Evaluating Macroeconomic Forecasts," Working Papers 2010-002, The George Washington University, Department of Economics, Research Program on Forecasting.
    24. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, EconWPA.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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