Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights

Ralf Brüggemann and Helmut Lütkepohl

No 2011-23, Working Paper Series of the Department of Economics, University of Konstanz from Department of Economics, University of Konstanz

Abstract: Many contemporaneously aggregated variables have stochastic aggregation weights. We compare different forecasts for such variables including univariate forecasts of the aggregate, a multivariate forecast of the aggregate that uses information from the disaggregate components, a forecast which aggregates a multivariate forecast of the disaggregate components and the aggregation weights, and a forecast which aggregates univariate forecasts for individual disaggregate components and the aggregation weights. In empirical illustrations based on aggregate GDP and money growth rates, we find forecast efficiency gains from using the information in the stochastic aggregation weights. A Monte Carlo study confirms that using the information on stochastic aggregation weights explicitly may result in forecast mean squared error reductions.

Keywords: Aggregation; autoregressive process; mean squared error (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2011-04-21
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets, nep-for and nep-ore
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Related works:
Journal Article: Forecasting contemporaneous aggregates with stochastic aggregation weights (2013)
Working Paper: Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights (2011)
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