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Nowcasting

Listed author(s):
  • Bańbura, Marta
  • Giannone, Domenico
  • Reichlin, Lucrezia

We define nowcasting as the prediction of the present, the very near future and the very recent past. Crucial in this process is to use timely monthly information in order to nowcast key economic variables, such as e.g. GDP, that are typically collected at low frequency and published with long delays. Until recently, nowcasting had received very little attention by the academic literature, although it was routinely conducted in policy institutions either through a judgemental process or on the basis of simple models. We argue that the nowcasting process goes beyond the simple production of an early estimate as it essentially requires the assessment of the impact of new data on the subsequent forecast revisions for the target variable. We design a statistical model which produces a sequence of nowcasts in relation to the real time releases of various economic data. The methodology allows to process a large amount of information, as it is traditionally done by practitioners using judgement, but it does it in a fully automatic way. In particular, it provides an explicit link between the news in consecutive data releases and the resulting forecast revisions. To illustrate our ideas, we study the nowcast of euro area GDP in the fourth quarter of 2008. JEL Classification: E52, C53, C33

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Paper provided by European Central Bank in its series Working Paper Series with number 1275.

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Date of creation: Dec 2010
Handle: RePEc:ecb:ecbwps:20101275
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  1. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, 01.
  2. Martha Banbura & Domenico Giannone & Lucrezia Reichlin, 2008. "Large Bayesian VARs," Working Papers ECARES 2008_033, ULB -- Universite Libre de Bruxelles.
  3. 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.
  4. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
  5. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  6. Angelini, Elena & Camba-Mendez, Gonzalo & Giannone, Domenico & Reichlin, Lucrezia & Rünstler, Gerhard, 2008. "Short-term Forecasts of Euro Area GDP Growth," CEPR Discussion Papers 6746, C.E.P.R. Discussion Papers.
  7. Troy Matheson, 2007. "An analysis of the informational content of New Zealand data releases: the importance of business opinion surveys," Reserve Bank of New Zealand Discussion Paper Series DP2007/13, Reserve Bank of New Zealand.
  8. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
  9. Banbura, Marta & Rünstler, Gerhard, 2007. "A look into the factor model black box: publication lags and the role of hard and soft data in forecasting GDP," Working Paper Series 0751, European Central Bank.
  10. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, December.
  11. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "A quasi maximum likelihood approach for large approximate dynamic factor models," Working Paper Series 0674, European Central Bank.
  12. 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.
  13. Knut Are Aastveit & Tørres G. Trovik, 2008. "Nowcasting Norwegian GDP: The role of asset prices in a small open economy," Working Paper 2007/09, Norges Bank.
  14. Maximo Camacho & Gabriel Perez-Quiros, 2008. "Introducing the EURO-STING: Short Term INdicator of Euro Area Growth," Working Papers 0807, Banco de España;Working Papers Homepage.
  15. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-Time Measurement of Business Conditions," PIER Working Paper Archive 07-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  16. Domenico Giannone & Lucrezia Reichlin & Saverio Simonelli, 2009. "Nowcasting Euro Area Economic Activity in Real-Time: The Role of Confidence Indicator," Working Papers ECARES 2009_021, ULB -- Universite Libre de Bruxelles.
  17. D'Agostino, Antonello & McQuinn, Kieran & O'Brien, Derry, 2011. "Nowcasting Irish GDP," MPRA Paper 32941, University Library of Munich, Germany.
  18. Marie Diron, 2008. "Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 371-390.
  19. K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze & G. Rünstler, 2008. "Short-term forecasting of GDP using large monthly datasets – A pseudo real-time forecast evaluation exercise," Working Paper Research 133, National Bank of Belgium.
  20. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
  21. 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 and International Relations Area.
  22. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
  23. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
  24. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
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