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Pooling-Based Data Interpolation and Backdating

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  • Massimiliano Marcellino

Abstract

Pooling forecasts obtained from different procedures typically reduces the mean square forecast error and more generally improve 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 in this context also. Copyright 2007 The Author Journal compilation 2007 Blackwell Publishing Ltd.

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Article provided by Wiley Blackwell in its journal Journal of Time Series Analysis.

Volume (Year): 28 (2007)
Issue (Month): 1 (01)
Pages: 53-71

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Handle: RePEc:bla:jtsera:v:28:y:2007:i:1:p:53-71

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Cited by:
  1. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.

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