本研究使用要素模型 (factor model) 預測我國產業GDP 成長趨勢，並與傳統時間序列模型比較何者具有預測優勢。要素模型利用主成份分析法 (principle component analysis)，從眾多資訊萃取要素來代表複雜的經濟體系。我們發現要素模型比傳統自我迴歸 (autoregressive, AR) 模型與向量自我迴歸 (vector autoregressive, VAR) 模型更具產業成長預測優勢，其中大幅改善製造業之成長預測準確度。我們採取不同模型設定方式，發現要素模型仍具有產業成長預測優勢。

This paper compares the forecast performances between the factor and conventional time series models for Taiwan’s industries. The factor model adopts the principle component analysis, generating factors from abundant information to represent the complicated economy. We find that the factor model has forecasting advantages over the autoregressive and vector autoregressive models. The factor model, in particular, greatly improves the forecasting performance on Taiwan’s manufacturing sector relative to conventional models. These results are robust to alternative model specifications.