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Forecasting Dutch GDP using Large Scale Factor Models

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A.H.J. den Reijer

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Abstract

This paper applies large scale factor models to Dutch quarterly data in order to generate forecasts of GDP growth rates for an horizon up to 8 quarters ahead. The data set consists of the series underlying the cen- tral bank´s macroeconomic structural model for the Netherlands sup- plemented with leading indicator variables. In a pseudo out-of-sample forecasting context, we select optimal models in the time dimension and the optimal size of the ordered data set in the cross-sectional dimension. The main empirical ?ndings of this paper are that the cross-sectional opti- mization substantially improves the forecasting performance of the factor models. However, only the dynamic factor model systematically outper- forms and encompasses the autoregressive benchmark model with an op- timal subset of the data of around 110 series. The forecasting gains in terms of mean squared errors range from 10% to 30% for forecast horizons up to 6 quarters ahead.

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Paper provided by Netherlands Central Bank, Research Department in its series DNB Working Papers with number 028.

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Date of creation: Feb 2005
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Handle: RePEc:dnb:dnbwpp:028

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Related research
Keywords: Factor models; Forecasting; Leading Indicators.;

Find related papers by JEL classification:
C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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  2. Connor, Gregory & Korajczyk, Robert A., 1988. "Risk and return in an equilibrium APT : Application of a new test methodology," Journal of Financial Economics, Elsevier, vol. 21(2), pages 255-289, September. [Downloadable!] (restricted)
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  4. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(06), pages 1113-1141, December. [Downloadable!]
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  5. 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. [Downloadable!] (restricted)
  6. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  7. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June. [Downloadable!] (restricted)
  8. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2002. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," CEPR Discussion Papers 3432, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  9. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis. [Downloadable!]
  10. Forni, Mario & Lippi, Marco & Reichlin, Lucrezia, 2003. "Opening the Black Box: Structural Factor Models versus Structural VARs," CEPR Discussion Papers 4133, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  11. Filippo Altissimo & Antonio Bassanetti & Riccardo Cristadoro & Mario Forni & Marco Lippi & Lucrezia Reichlin & Giovanni Veronese, 2001. "A real time coincident indicator of the euro area business cycle," Temi di discussione (Economic working papers) 436, Bank of Italy, Economic Research Department. [Downloadable!]
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  13. Altissimo, Filippo & Bassanetti, Antonio & Cristadoro, Riccardo & Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia & Veronese, Giovanni, 2001. "EuroCOIN: A Real Time Coincident Indicator of the Euro Area Business Cycle," CEPR Discussion Papers 3108, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  14. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  15. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  16. Artis, Michael J & Banerjee, Anindya & Marcellino, Massimiliano, 2002. "Factor Forecasts for the UK," CEPR Discussion Papers 3119, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  17. Elena Angelini & Jerome Henry & Ricardo Mestre, 2001. "Diffusion index-based inflation forecasts for the euro area," Working Paper Series 061, European Central Bank. [Downloadable!]
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Adam Elbourne & Henk Kranendonk & Rob Luginbuhl & Bert Smid & Martin Vromans, 2008. "Evaluating CPB’s published GDP growth forecasts," CPB Documents 172, CPB Netherlands Bureau for Economic Policy Analysis. [Downloadable!]
  2. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute. [Downloadable!]
    Other versions:
  3. Marc Hallin & Roman Liska, 2008. "Dynamic Factors in the Presence of Block Structure," ECARES Working Papers 2008_012, Université Libre de Bruxelles, Ecares. [Downloadable!]
    Other versions:
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