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Working Paper

299.
Pooling-Based Data Interpolation and Backdating
by Massimiliano Marcellino

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.

Key words: Pooling, Interpolation, Factor Model, Kalman Filter, Spline

JEL Classification: C32, C43, C82. 

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Last updated February 27, 2007