Discussion Paper Details

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Title: Pooling-based data interpolation and backdating

Author(s): Massimiliano Marcellino

Publication Date: October 2005

Keyword(s): factor Model, interpolation, Kalman Filter, pooling and spline

Programme Area(s): International Macroeconomics

Abstract: Pooling forecasts obtained from different procedures typically reduces the mean square forecast error and more generally improves the quality of the forecast. In this paper we evaluate whether pooling interpolated or backdated time series obtained from different procedures can also improve the quality of the generated data. Both simulation results and empirical analyses with macroeconomic time series indicate that pooling plays a positive and important role also in this context.

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Bibliographic Reference

Marcellino, M. 2005. 'Pooling-based data interpolation and backdating'. London, Centre for Economic Policy Research.