預測半導體產業景氣循環: 貝氏模型平均法的應用

Forecasting the Semiconductor Industry Cycles by Bayesian Model Averaging

翁書適(Shu-Shih Weng)

指導教授 ： 劉文獻

國立中正大學/管理學院/經濟系國際經濟研究所/碩士(2013年)

摘要

本文旨在以75個美國產業及總體層級變數之月資料研究全球半導體產業景氣循環，樣本期間為1997年1月至2012年10月，除以貝氏模型平均法進行變數選取外，並比較貝氏模型平均法、ARMA模型及隨機漫步模型之樣本外預測能力。實證結果顯示，存貨量是預測半導體產業景氣循環的主要變數，而貝氏模型平均法之樣本外預測能力不論在均方根誤差及DM檢定的表現上皆顯著優於ARMA模型及隨機漫步模型，並在預測轉折點有較好的表現。

關鍵字

貝氏模型平均法 ； 半導體產業景氣循環 ； 存貨量

並列摘要

This paper aims to use the Bayesian Model Averaging (BMA) to identify the main cause of the global semiconductor industry cycle from a list of 75 U.S. macro and industry-level variables. By using the data from January 1997 to October 2012, we compare the out-of-sample forecasting performance accuracy BMA, ARMA and random walk models. The empirical results show that inventories are the most important variables in explaining the semiconductor industry cycle. Besides, BMA outperforms the other two models in terms of out-of-sample forecasts in both RMSE and DM tests. Nonetheless, BMA also provides better turning-point forecasts.

並列關鍵字

Bayesian Model Averaging ； semiconductor industry cycle ； inventories

參考文獻

iSuppli (2012), “IHS iSuppli Semiconductor Preliminary Rankings for 2012,” available at: http://www.isuppli.com/Pages/Home.aspx.

Aubry, M. and P. Renou-Maissant (2011), “Semiconductor Industry Cycles: Explanatory Factors and Forecasting,” Working Paper. Department of Economics, University of Caen, France.

Aubry, M. and P. Renou-Maissant (2013), “Investigating the Semiconductor Industry Cycles,” Applied Economics, 45, 3058-3067.

Barnard, G. A. (1963), “New Methods of Quality Control,” Journal of the Royal Statistical Society, Series A, 126, 255-266.

Chow, H. K. and K. M. Choy (2006), “Forecasting the Global Electronics Cycle with Leading Indicators: a Bayesian VAR Approach,” International Journal of Forecasting, 22, 301-315.