Building of combined forecasts for seasonal causal-descriptive models
Języki publikacji
PL
Abstrakty
EN
In the article author considers the situation in which several forecasts of the same variable are available. The forecasts was marked on basis of the causal-descriptive models for economic variable having the form of time series with seasonal fluctuations. Author creates new forecast of the same variable – the combined forecast which should be burdened with the smallest error. The author analyses four methods of creating combined forecasts as a weighted average and examines the efficiency of combined forecasts in comparison with individual forecasts. In the majority of the examination cases combined forecasts marked two methods: artificial neural networks and variance-covariance have smaller prediction errors than their component forecasts. It appears that the results of empirical research confirmed the higher efficiency of combined forecast in comparison with individual forecasts.
Katedra Zastosowań Matematyki w Ekonomii, Zachodniopomorski Uniwersytet Technologiczny w Szczecinie, ul.K.Janickiego 31, 71-270 Szczecin
Bibliografia
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