GDP nowcasting with ragged-edge data: a semi-parametric modeling

Journal article: This paper formalizes the process of forecasting unbalanced monthly datasets in order to obtain robust nowcasts and forecasts of quarterly gross domestic product (GDP) growth rate through a semi-parametric modeling. This innovative approach lies in the use of non-parametric methods, based on nearest neighbors and on radial basis function approaches, to forecast the monthly variables involved in the parametric modeling of GDP using bridge equations. A real-time experience is carried out on euro area vintage data in order to anticipate, with an advance ranging from 6 to 1 months, the GDP flash estimate for the whole zone.

Author(s)

Laurent Ferrara, Dominique Guegan, Patrick Rakotomarolahy

Journal
  • Journal of Forecasting
Date of publication
  • 2010
Keywords JEL
C22 C53 E32
Keywords
  • Euro area GDP • real-time nowcasting • forecasting • non-parametric methods
Pages
  • 186-199
Version
  • 1
Volume
  • 29