Title: A Litterman BVAR approach for production forecasting of technology industries
Authors: Hsu, PH
Wang, CH
Shyu, JZ
Yu, HC
統計學研究所
科技管理研究所
Institute of Statistics
Institute of Management of Technology
Keywords: production forecasting;autoregression (AR);vector autoregression (VAR);Bayesian vector autoregression (BVAR);industrial clusters
Issue Date: 1-Jan-2003
Abstract: 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. (C) 2002 Elsevier Science Inc. All rights reserved.
URI: http://dx.doi.org/10.1016/S0040-1625(01)00142-1
http://hdl.handle.net/11536/14467
ISSN: 0040-1625
DOI: 10.1016/S0040-1625(01)00142-1
Journal: TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
Volume: 70
Issue: 1
Begin Page: 67
End Page: 82
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