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Pooling-based Data Interpolation and Backdating

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Author Info
Massimiliano Marcellino

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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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Paper provided by IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University in its series Working Papers with number 299.

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Date of creation: 2005
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Handle: RePEc:igi:igierp:299

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  3. Tommaso Proietti, 2004. "Temporal Disaggregation by State Space Methods: Dynamic Regression Methods Revisited," Econometrics 0411011, EconWPA. [Downloadable!]
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  4. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December. [Downloadable!] (restricted)
  5. Santos Silva, J. M. C. & Cardoso, F. N., 2001. "The Chow-Lin method using dynamic models," Economic Modelling, Elsevier, vol. 18(2), pages 269-280, April. [Downloadable!] (restricted)
  6. Timmermann, Allan G, 2005. "Forecast Combinations," CEPR Discussion Papers 5361, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  7. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, 06. [Downloadable!] (restricted)
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  8. Graham Elliott & Allan Timmermann, 2008. "Economic Forecasting," Journal of Economic Literature, American Economic Association, vol. 46(1), pages 3-56, March.
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  9. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-36, January.
  10. Jushan Bai & Serena Ng, 2004. "Confidence Intervals for Diffusion Index Forecasts with a Large Number of Predictor," Econometrics 0408006, EconWPA. [Downloadable!]
  11. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  12. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January. [Downloadable!] (restricted)
  13. Gabriel Fagan & Jérôme Henry & Ricardo Mestre, 2001. "An area-wide model (AWM) for the euro area," Working Paper Series 42, European Central Bank. [Downloadable!]
  14. Elena Angelini & Jerome Henry & Ricardo Mestre, 2001. "Diffusion index-based inflation forecasts for the euro area," Working Paper Series 061, European Central Bank. [Downloadable!]
  15. Angelini, Henry, Marcellino, 2002. "interpolation with a large information set," Computing in Economics and Finance 2002 72, Society for Computational Economics. [Downloadable!]
  16. Massimiliano Marcellino & James H. Stock & Mark W. Watson, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
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