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Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise

  • Barhoumi, K.
  • Rünstler, G.
  • Cristadoro, R.
  • Den Reijer, A.
  • Jakaitiene, A.
  • Jelonek, P.
  • Rua, A.
  • Ruth, K.
  • Benk, S.
  • Van Nieuwenhuyze, C.

This paper evaluates different models for the short-term forecasting of real GDP growth in ten selected European countries and the euro area as a whole. Purely quarterly models are compared with models designed to exploit early releases of monthly indicators for the nowcast and forecast of quarterly GDP growth. Amongst the latter, we consider small bridge equations and forecast equations in which the bridging between monthly and quarterly data is achieved through a regression on factors extracted from large monthly datasets. The forecasting exercise is performed in a simulated real-time context, which takes account of publication lags in the individual series. In general, we find that models that exploit monthly information outperform models that use purely quarterly data and, amongst the former, factor models perform best.

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Paper provided by Banque de France in its series Working papers with number 215.

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Length: 25 pages
Date of creation: 2008
Date of revision:
Handle: RePEc:bfr:banfra:215
Contact details of provider: Postal: Banque de France 31 Rue Croix des Petits Champs LABOLOG - 49-1404 75049 PARIS
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  1. 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.
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  5. 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, vol. 27(2), pages 333-346.
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