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A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle

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  • Buss, Ginters

Abstract

We build a small-scale factor model for the GDP of one of the hardest hit economies during the latest recession to study the exact dynamic versus static factor model performance along a business cycle, with an emphasis placing on nowcasting performance during a pronounced switch of business cycle phases due to the latest recession. We compare the factor models' nowcasting performance to a random walk, autoregressive and the best-performing nowcasting models at our hands, which are vector autoregressive (VAR) models. It is shown that a small-scale static factor-augmented VAR (FAVAR) model tends to improve upon the nowcasting performance of the VAR models when the model span and the nowcasting period stretch beyond a single business cycle phase, while exact dynamic factor models tend to fail to detect the timing and depth of the recession regardless of ARMA specifications. As regards the case when the model span and the nowcasting period are contained within a single business cycle phase, static and dynamic factor models appear to show similar performance with potentially slight superiority of dynamic factor models if the factor-forming set of variables and factor dynamics are carefully selected.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 22147.

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Date of creation: 16 Apr 2010
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Handle: RePEc:pra:mprapa:22147

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Keywords: nowcasting; business cycle; static versus dynamic factors; small-scale FAVAR; VAR; GDP;

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  1. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," NBER Working Papers 11285, National Bureau of Economic Research, Inc.
  2. Rangan Gupta & Alain Kabundi, 2009. "A Large Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 137, Economic Research Southern Africa.
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  7. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-96, November.
  8. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  9. Maximo Camacho & Gabriel Perez-Quiros & Pilar Poncela, 2012. "Markov-switching dynamic factor models in real time," Banco de Espa�a Working Papers 1205, Banco de Espa�a.
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  12. Anindya Banerjee & Massimiliano Marcellino, 2008. "Factor-augmented Error Correction Models," Economics Working Papers ECO2008/15, European University Institute.
  13. Marcellino, Massimiliano & Schumacher, Christian, 2008. "Factor-MIDAS for now- and forecasting with ragged-edge data: A model comparison for German GDP," CEPR Discussion Papers 6708, C.E.P.R. Discussion Papers.
  14. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
  15. D'Agostino, Antonello & Giannone, Domenico, 2006. "Comparing Alternative Predictors Based on Large-Panel Factor Models," Research Technical Papers 14/RT/06, Central Bank of Ireland.
  16. Boriss Siliverstovs & Konstantin A. Kholodilin, 2010. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," Discussion Papers of DIW Berlin 970, DIW Berlin, German Institute for Economic Research.
  17. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
  18. Marcelle Chauvet & James D. Hamilton, 2005. "Dating Business Cycle Turning Points," NBER Working Papers 11422, National Bureau of Economic Research, Inc.
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