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Forecasting Macroeconomic Variables for the Acceding Countries

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  • Anindya Banerjee
  • Massimiliano Marcellino
  • Igor Masten

Abstract

The accession of ten countries into the European Union makes the forecasting of their key macroeconomic indicators such as GDP growth, inflation and interest rates an exercise of some importance. Because of the transition period, only short spans of reliable time series are available which suggests the adoption of simple time series models as forecasting tools, because of their parsimonious specification and good performance. Nevertheless, despite this constraint on the span of data, a large number of macroeconomic variables (for a given time span) are available which are of potential use in forecasting, making the class of dynamic factor models a reasonable alternative forecasting tool. We compare the relative performance of the two forecasting approaches, first by means of simulation experiments and then by using data for five Acceding countries. We also evaluate the role of Euro-area information for forecasting, and the usefulness of robustifying techniques such as intercept corrections and second differencing. We find that factor models work well in general, even though there are marked differences across countries. Robustifying techniques are useful in a few cases, while Euro-area information is virtually irrelevant.

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Bibliographic Info

Paper provided by IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University in its series Working Papers with number 260.

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Date of creation: 2004
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Handle: RePEc:igi:igierp:260

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References

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  1. Fagan, Gabriel & Henry, Jérôme & Mestre, Ricardo, 2001. "An area-wide model (AWM) for the euro area," Working Paper Series 0042, European Central Bank.
  2. Artis, M. & Marcellino, M., 1999. "Fiscal Forecasting: the Track Record of the IMF, OECD and EC," Economics Working Papers eco99/22, European University Institute.
  3. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, December.
  4. Massimiliano Marcellino & James H. Stock & Mark W. Watson, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  5. Angelini, Elena & Henry, Jérôme & Marcellino, Massimiliano, 2003. "Interpolation and backdating with a large information set," Working Paper Series 0252, European Central Bank.
  6. James H. Stock & Mark W. Watson, 1988. "A Probability Model of The Coincident Economic Indicators," NBER Working Papers 2772, National Bureau of Economic Research, Inc.
  7. Artis, Michael J & Marcellino, Massimiliano & Proietti, Tommaso, 2004. "Characterizing the Business Cycle for Accession Countries," CEPR Discussion Papers 4457, C.E.P.R. Discussion Papers.
  8. Ben S. Bernanke & Jean Boivin, 2001. "Monetary Policy in a Data-Rich Environment," NBER Working Papers 8379, National Bureau of Economic Research, Inc.
  9. Marcellino, Massimiliano, 2000. " Forecast Bias and MSFE Encompassing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(4), pages 533-42, September.
  10. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
  11. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  12. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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Cited by:
  1. Angelini, Elena & Henry, Jérôme & Marcellino, Massimiliano, 2004. "Interpolation and Backdating with A Large Information Set," CEPR Discussion Papers 4533, C.E.P.R. Discussion Papers.
  2. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Working Papers 334, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  3. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate pass-through in the major European economies - the role of expectations," Discussion Papers 10-07, Department of Economics, University of Birmingham.
  4. Victor Bystrov, 2006. "Forecasting Emerging Market Indicators: Brazil and Russia," Economics Working Papers ECO2006/12, European University Institute.
  5. O. De Bandt & E. Michaux & C. Bruneau & A. Flageollet, 2007. "Forecasting inflation using economic indicators: the case of France," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 1-22.
  6. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.
  7. Harm Bandholz, 2005. "New Composite Leading Indicators for Hungary and Poland," Ifo Working Paper Series Ifo Working Paper No. 3, Ifo Institute for Economic Research at the University of Munich.

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