Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/152686
Authors: 
Angelini, Elena
Henry, Jérôme
Marcellino, Massimiliano
Year of Publication: 
2003
Series/Report no.: 
ECB Working Paper 252
Abstract: 
Existing methods for data interpolation or backdating are either univariate or based on a very limited number of series, due to data and computing constraints that were binding until the recent past. Nowadays large datasets are readily available, and models with hundreds of parameters are fastly estimated. We model these large datasets with a factor model, and develop an interpolation method that exploits the estimated factors as an efficient summary of all the available information. The method is compared with existing standard approaches from a theoretical point of view, by means of Monte Carlo simulations, and also when applied to actual macroeconomic series. The results indicate that our method is more robust to model misspecification, although traditional multivariate methods also work well while univariate approaches are systematically outperformed. When interpolated series are subsequently used in econometric analyses, biases can emerge, depending on the type of interpolation but again be reduced with multivariate approaches, including factor-based ones.
Subjects: 
factor model
Interpolation
Kalman filter
spline
JEL: 
C32
C43
C82
Document Type: 
Working Paper

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