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Forecasting Aggregated Time Series Variables: A Survey

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  • Helmut Luetkepohl

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 first and then these forecasts may be aggregated. A number of forecasts are presented and compared. Classical theoretical results on the relative efficiencies of different forecasts are reviewed and some complications are discussed which invalidate the theoretical results. Contemporaneous as well as temporal aggregation are considered.

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Bibliographic Info

Paper provided by European University Institute in its series Economics Working Papers with number ECO2009/17.

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Date of creation: 2009
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Handle: RePEc:eui:euiwps:eco2009/17

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Keywords: Autoregressive moving-average process; temporal aggregation; contemporaneous aggregation; vector autoregressive moving-average process;

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Cited by:
  1. Ralf Brueggemann & Helmut Luetkepohl, 2011. "Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights," Economics Working Papers ECO2011/17, European University Institute.
  2. Uwe Hassler, 2011. "Estimation of fractional integration under temporal aggregation," Post-Print hal-00815563, HAL.
  3. Helmut Luetkepohl, 2011. "Forecasting Nonlinear Aggregates and Aggregates with Time-varying Weights," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 107-133, February.
  4. 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, vol. 146(1), pages 185-198.
  5. Ferrara, L. & Marsilli, C. & Ortega, J-P., 2013. "Forecasting growth during the Great Recession: is financial volatility the missing ingredient?," Working papers 454, Banque de France.
  6. Asimakopoulos, Stylianos & Paredes, Joan & Warmedinger, Thomas, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.

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