Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights
AbstractMany contemporaneously aggregated variables have stochasticaggregation 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 European University Institute in its series Economics Working Papers with number ECO2011/17.
Date of creation: 2011
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Aggregation; autoregressive process; mean squared error;
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 Brüggemann & Helmut Lütkepohl, 2011. "Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights," Working Paper Series of the Department of Economics, University of Konstanz 2011-23, Department of Economics, University of Konstanz.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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