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Forecasting Aggregated Time Series Variables: A Survey

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  • Helmut Lütkepohl

Abstract

Aggregated times series variables can be forecasted in different ways. For example, they may be forecasted on the basis of the aggregate series or forecasts of disaggregated variables may be obtained fi rst and then these forecasts may be aggregated. A number of forecasts are presented and compared. Classical theoretical results on the relative effi ciencies of different forecasts are reviewed and some complications are discussed which invalidate the theoretical results. Contemporaneous as well as temporal aggregation are considered. JEL classifi cation : C22, C32 Key Words : Autoregressive moving-average process, contemporaneous aggregation, temporal aggregation, vector autoregressive moving-average process

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File URL: http://dx.doi.org/10.1787/jbcma-2010-5km399r2jz9n
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Article provided by OECD Publishing,CIRET in its journal OECD Journal: Journal of Business Cycle Measurement and Analysis.

Volume (Year): 2010 (2010)
Issue (Month): 2 ()
Pages: 1-26

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Handle: RePEc:oec:stdkab:5km399r2jz9n

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Cited by:
  1. Ralf Brueggemann & Helmut Luetkepohl, 2011. "Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights," Economics Working Papers, European University Institute ECO2011/17, European University Institute.
  2. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Forecasting aggregate demand: Analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework," International Journal of Production Economics, Elsevier, Elsevier, vol. 146(1), pages 185-198.
  3. Ferrara, Laurent & Marsilli, Clément & Ortega, Juan-Pablo, 2014. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Economic Modelling, Elsevier, Elsevier, vol. 36(C), pages 44-50.
  4. Helmut Luetkepohl, 2010. "Forecasting Nonlinear Aggregates and Aggregates with Time-varying Weights," Economics Working Papers, European University Institute ECO2010/11, European University Institute.
  5. Uwe Hassler, 2011. "Estimation of fractional integration under temporal aggregation," Post-Print, HAL peer-00815563, HAL.
  6. Asimakopoulos, Stylianos & Paredes, Joan & Warmedinger, Thomas, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series, European Central Bank 1550, European Central Bank.

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