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GDP nowcasting with ragged‐edge data: a semi‐parametric modeling
Author(s) -
Ferrara Laurent,
Guégan Dominique,
Rakotomarolahy Patrick
Publication year - 2009
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.1159
Subject(s) - nowcasting , parametric statistics , gross domestic product , econometrics , real gross domestic product , parametric model , semiparametric model , computer science , economics , nonparametric statistics , mathematics , statistics , macroeconomics , meteorology , geography
Abstract 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. Copyright © 2009 John Wiley & Sons, Ltd.