Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
Forecasting the Semiconductor Industry Growth: A Bayesian
Model Averaging Approach
Wen-Hsien Liu＊ and Shu-Shih Weng#
It is estimated that a new modern semiconductor fabrication plant may cost US$5
billion today, as the semiconductor industry has experienced huge fluctuations,
ranging from a positive 70% to a negative 40% annual growth during the past 40
years. Hence, how to precisely forecast the semiconductor industry growth in
order to secure the enormous investment turns out to be a concern and a
challenge in the industry. Recently, the forecast of the semiconductor industry
growth has garnered much attention of academic researchers than ever (Liu,
2005; Liu and Chyi, 2006, Liu, 2007; Tan and Mattews, 2010a; Tan and Mattews,
2010b; Tan, 2011; Liu et al. 2013; Aubry and Renou-Maissant, 2013; Aubry and
Renou-Maissant, 2014,). This paper aims to provide a new forecasting framework,
a Bayesian Model Averaging (BMA) approach, in addition to the existing literature
on this issue. By incorporating 70 potential macroeconomic, financial and
industry-level explanatory variables and using the monthly data from 1995:05 to
2009:02, we are able to provide the in-sample posterior inclusion probability (PIP)
of each potential explanatory variable and various h-step-ahead out-of-sample
forecasts for the period 2009:03-2012:10. The out-of-sample forecast
performance of the BMA model is then compared to the Random Walk model and
the best-fitted ARMA(3,1) model with a seasonal autocorrelation adjustment.
Based on the Diebold-Mariano (DM) test, we find that the ARMA model
outperforms the Random Walk model and the BMA model in the real time, 0-stepahead, forecast, but the BMA model beats the Random Walk and the ARMA
models for other longer term forecasts (6-, 12-, 18- and 24-step-ahead forecasts)
by 21-66% (in terms of the root-mean-squared error). Therefore, we conclude that
the BMA approach proposed in this study in general provides a better forecasting
tool than the pure guesswork or the conventional, univariate, time-series model for
the long-term forecasts of the semiconductor industry growth.
JEL Codes: L16, L63
＊
Dr. Wen-Hsien Liu, Department of Economics and Institute of International Economics, Min-Hsiung, Chiayi
62102, National Chung Cheng University, Taiwan. E-mail: whl@ccu.edu.tw; Phone: +886-5-2720411 ext.
34124; Fax: +886-5-2720816.
#
Mr. Shuh-Shih Weng, Master’s student, Institute of International Economics, National Chung Cheng
University, Min-Hsiung, Chiayi 62102, Taiwan.