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A Litterman BVAR approach for production forecasting of technology industries

机译:Litterman BVAR方法用于技术行业的产量预测

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Forecasting the production of technology industries is important to entrepreneurs and governments, but usually suffers from market fluctuation and explosion. This paper aims to propose a Litterman Bayesian vector autoregression (LBVAR) model for production prediction based on the interaction of industrial clusters. Related industries within industrial clusters are included into the LBVAR model to provide more accurate predictions. The LBVAR model possesses the superiority of Bayesian statistics in small sample forecasting and holds the dynamic property of the vector autoregression (VAR) model. Two technology industries in Taiwan, the photonics industry and semiconductor industry are used to examine the LBVAR model using a rolling forecasting procedure. As a result, the LBVAR model was found to be capable of providing outstanding predictions for these two technology industries in comparison to the autoregression (AR) model and VAR model.
机译:预测技术产业的生产对企业家和政府来说很重要,但通常会遭受市场波动和爆炸的影响。本文旨在基于产业集群的相互作用,提出一个Litterman贝叶斯向量自回归(LBVAR)模型用于生产预测。 LBVAR模型中包含了产业集群内的相关产业,以提供更准确的预测。 LBVAR模型在小样本预测中具有贝叶斯统计的优势,并具有向量自回归(VAR)模型的动态特性。台湾的两个技术产业,即光子产业和半导体产业,都采用滚动预测程序来检验LBVAR模型。结果,与自回归(AR)模型和VAR模型相比,发现LBVAR模型能够为这两个技术行业提供出色的预测。

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