Pooling-based Data Interpolation and Backdating

Massimiliano Marcellino

No 299, Working Papers from IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University

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.

Date: 2005
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Related works:
Journal Article: Pooling‐Based Data Interpolation and Backdating (2007)
Working Paper: Pooling-based data interpolation and backdating (2005)
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