Developing a new approach to estimate the parameters of the damped trend exponential smoothing

Exponential smoothing models are known as an important prediction tool both in business and supply chain management. The aim of this paper is to provide the analytical forecasting properties of the damped trend exponential smoothing model in the class of the “single source of error” framework. Also, in recent years, the damped trend exponential smoothing has developed in the majority of applicable studies. Nowadays, it is applied as a precise approach in forecasting. This paper develops a novel approach by the algebraic mapping between the state-space parameters and the reduced form ARIM parameters. Moreover, it is showed that the new estimation approach can circumvent the difficulties that are possible to appear during estimating directly the damped state-space parameters. Finally, it has considered US energy commodities data to analyze the performance of the damped trend model state space mode estimated with a developed novel approach. The results illustrate that the novel approach works properly, besides, this method is very viable and it can be used in any empirical applications.