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Real-Time Nowcasting of GDP: Factor Model versus Professional Forecasters

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  • Liebermann, Joelle

    (Central Bank of Ireland)

Abstract

This paper performs a fully real-time nowcasting (forecasting) exercise of US real gross domestic product (GDP) growth using Giannone, Reichlin and Small (2008) factor model framework which enables one to handle unbalanced datasets as available in real-time. To this end, we have constructed a novel real-time database of vintages from October 2000 to June 2010 for a panel of US variables, and can hence reproduce, for any given day in that range, the exact information that was available to a real-time forecaster. We track the daily evolution throughout the current and next quarter of the model nowcasting performance. Similarly to Giannone et al. pseudo realtime results, we find that the precision of the nowcasts increases with information releases. Moreover, the Survey of Professional Forecasters (SPF) does not carry additional information with respect to the model best specification, suggesting that the often cited superiority of the SPF, attributable to judgment, is weak over our sample. Then, as one moves forward along the real-time data flow, the continuous updating of the model provides a more precise estimate of current quarter GDP growth and the SPF becomes stale compared to all the model specifications. These results are robust to the recent recession period.

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Bibliographic Info

Paper provided by Central Bank of Ireland in its series Research Technical Papers with number 3/RT/11.

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Date of creation: Mar 2011
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Handle: RePEc:cbi:wpaper:3/rt/11

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Keywords: Real-time data; Nowcasting; Forecasting; Factor model.;

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References

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  1. D’Agostino, Antonello & Giannone, Domenico & Surico, Paolo, 2006. "(Un)Predictability and macroeconomic stability," Working Paper Series, European Central Bank 0605, European Central Bank.
  2. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 27(4), pages 468-479.
  3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, Elsevier, vol. 55(4), pages 665-676, May.
  4. Boivin, Jean & Ng, Serena, 2005. "Understanding and Comparing Factor-Based Forecasts," MPRA Paper 836, University Library of Munich, Germany.
  5. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 25, pages 52-60, January.
  6. Domenico Giannone & Lucrezia Reichlin & Luca Sala, 2005. "Monetary Policy in Real Time," NBER Chapters, National Bureau of Economic Research, Inc, in: NBER Macroeconomics Annual 2004, Volume 19, pages 161-224 National Bureau of Economic Research, Inc.
  7. Forni M. & Hallin M., 2003. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Computing in Economics and Finance 2003, Society for Computational Economics 143, Society for Computational Economics.
  8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 20(1), pages 134-44, January.
  9. Karim Barhoumi & Szilard Benk & Riccardo Cristadoro & Ard Den Reijer & Audrone Jakaitiene & Piotr Jelonek & António Rua & Gerhard Rünstler & Karsten Ruth & Christophe Van Nieuwenhuyze, 2008. "Short-term forecasting of GDP using large monthly datasets - a pseudo real-time forecast evaluation exercise," Occasional Paper Series 84, European Central Bank.
  10. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers, Econometric Society 1504, Econometric Society.
  11. Catherine Doz & Lucrezia Reichlin, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Post-Print, HAL hal-00844811, HAL.
  12. Jon Faust & Jonathan H. Wright, 2007. "Comparing Greenbook and Reduced Form Forecasts using a Large Realtime Dataset," NBER Working Papers 13397, National Bureau of Economic Research, Inc.
  13. Ben S. Bernanke & Jean Boivin, 2001. "Monetary Policy in a Data-Rich Environment," NBER Working Papers 8379, National Bureau of Economic Research, Inc.
  14. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198523543, October.
  15. Antonello D'Agostino & Kieran McQuinn & Derry O’Brien, 2012. "Nowcasting Irish GDP," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, OECD Publishing,CIRET, vol. 2012(2), pages 21-31.
  16. Vincenzo Verardi & Catherine Dehon & Marjorie Gassner, 2009. "Beware of "good" outliers and overoptimistic conclusions," ULB Institutional Repository 2013/9907, ULB -- Universite Libre de Bruxelles.
  17. repec:cbi:wpaper:14/rt/06 is not listed on IDEAS
  18. Elena Angelini & Gonzalo Camba‐Mendez & Domenico Giannone & Lucrezia Reichlin & Gerhard Rünstler, 2011. "Short‐term forecasts of euro area GDP growth," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 14(1), pages C25-C44, February.
  19. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, American Economic Association, vol. 80(3), pages 375-89, June.
  20. Antonello D’ Agostino & Domenico Giannone, 2012. "Comparing Alternative Predictors Based on Large‐Panel Factor Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, Department of Economics, University of Oxford, vol. 74(2), pages 306-326, 04.
  21. Bańbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(2), pages 333-346.
  22. Catherine Dehon & Marjorie Gassner & Vincenzo Verardi, 2009. "Beware of 'Good' Outliers and Overoptimistic Conclusions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, Department of Economics, University of Oxford, vol. 71(3), pages 437-452, 06.
  23. Bańbura, Marta & Modugno, Michele, 2010. "Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data," Working Paper Series, European Central Bank 1189, European Central Bank.
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Cited by:
  1. D’Agostino, Antonello & Schnatz, Bernd, 2012. "Survey-based nowcasting of US growth: a real-time forecast comparison over more than 40 years," Working Paper Series, European Central Bank 1455, European Central Bank.
  2. Liebermann, Joelle, 2012. "Real-time forecasting in a data-rich environment," MPRA Paper 39452, University Library of Munich, Germany.
  3. Alessandro Beber & Michael W. Brandt & Maurizio Luisi, 2013. "Distilling the Macroeconomic News Flow," NBER Working Papers 19650, National Bureau of Economic Research, Inc.
  4. repec:cbi:wpaper:9/rt/08 is not listed on IDEAS
  5. Antonello D'Agostino & Kieran McQuinn & Derry O’Brien, 2012. "Nowcasting Irish GDP," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing,CIRET, OECD Publishing,CIRET, vol. 2012(2), pages 21-31.

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