Convergence Forecasting by Best Selection of VARIMA Parameters: (Case Study: Forecasting the Alpha Company's Provincial Market Share of IRAN's Butter Market)

Forecasting is one of the most attractive and widely research areas. Forecasting is the basis of planning and budgeting in firms. There are many forecasting methods that enterprise firm selected one or more from them, but these methods generally have a weakness. In previous studies, judgmental methods mainly focus on expert judgments, which are waste of resources in some extent and may lead to lower accuracy, while statistical methods in terms of statistical models can achieve better performance, but when the unpredictable events appear, these models are useless and ineffective sometimes. The paper presents how a new forecasting approach combined forecasting to reduce significantly the error resulting from that, by working with an entire assortment at a time instead of producing a forecast individually. The main purpose of this paper is introduction a convergence method of growth rate for all provinces. To identify how apply combined forecasting in which they are operating; In this paper, by incorporating province's market share, two novel paradigms are proposed for forecasting, which may overcome the aforementioned shortcoming. In the first paradigm, the market shares of provinces are extracted, and then their trend are integrated to forecast market shares in subsequent years. Moreover, these paradigms are validated and compared using real data. The empirical results show that our proposed paradigms are useful and feasible for forecasting, and furthermore, the combined model out performs the traditional forecasting by rectifying the Solution of Non-Linear Programming to filling the gap between VARIMA and convergence regression.
نشریه نظریه های نوین اقتصادی, Volume:2 Issue: 3, 2014
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