Browsing by Author "Hsu, PH"

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A Litterman BVAR approach for production forecasting of technology industries
(2003-01-01)
Hsu, PH
;
Wang, CH
;
Shyu, JZ
;
Yu, HC
;
統計學研究所
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.
Production forecasting of Taiwan's technology industrial cluster: A Bayesian autoregression approach
(2005-06-01)
Lee, JC
;
Hsu, PH
;
Wang, CH
;
Lai, HC
;
統計學研究所
This study proposes a forecasting method that combines the clustering effect and non-informative diffiuse-prior Bayesian vector autoregression (NDBVAR) model to forecast the productions of technology industries. Two empirical cases are examined to verify the proposed method: the semiconductor industry and computer manufacturing industry in Taiwan. It is found that the NDBVAR model outperforms the other three conventional time series models including the autoregression (AR), vector autoregression (VAR), and Litterman Bayesian VAR (LBVAR) models. Moreover the NDBVAR model also outperforms the forecast reports from leading market information providers over the past several years. The forecasting method proposed is therefore concluded to be a feasible approach for production prediction, especially for technology, industries in volatile environments.