Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights
AbstractMany 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.
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Bibliographic InfoPaper provided by Department of Economics, University of Konstanz in its series Working Paper Series of the Department of Economics, University of Konstanz with number 2011-23.
Length: 21 pages
Date of creation: 21 Apr 2011
Date of revision:
Other versions of this item:
- Brüggemann, Ralf & Lütkepohl, Helmut, 2013. "Forecasting contemporaneous aggregates with stochastic aggregation weights," International Journal of Forecasting, Elsevier, vol. 29(1), pages 60-68.
- Ralf Brueggemann & Helmut Luetkepohl, 2011. "Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights," Economics Working Papers ECO2011/17, European University Institute.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-07-02 (All new papers)
- NEP-CBA-2011-07-02 (Central Banking)
- NEP-ECM-2011-07-02 (Econometrics)
- NEP-ETS-2011-07-02 (Econometric Time Series)
- NEP-FOR-2011-07-02 (Forecasting)
- NEP-ORE-2011-07-02 (Operations Research)
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