- 期刊
探索預測台灣通膨隱而未現的重要因子－監督式降維模型的實證
Disentangling Latent Factors for Inflation Forecasting in Taiwan via Supervised Dimension-Reduction Approach
摘要
研究目的：根據傳統經濟理論認定與通膨相關的變數，侷限了研究者發現其他重要變數的可能性。本文探討高維度資料集於通膨預測之應用，嘗試由下而上利用改良後之擴散指數預測法（diffusion index forecasting）預測台灣通膨。研究設計／方法：本研究以2000年至2021年間，近百個對於台灣通膨具有潛在影響力變數，採納Stock and Watson（2002b）之建議，事先將變數分為11大類後再進行預測，探討不同降維方法所萃取之潛在因子（latent factor）對模型預測力的影響。研究結果：本文發現使用監督式的降維方法有助於提升模型整體預測能力。研究限制／啟發：發現在分類前預測力最好的偏分量迴歸（PQR）於分類後建模之模型預測力有了更進一步提升。理論／實務／社會意涵：分析架構中可以探討預測通膨的關鍵變數，在不同的時空背景下11大類別相對重要性之消長與經濟意義，解構其中與後續政策干預攸關的變數說明。創見／價值：本研究也建構溫和與極端通膨（縮）預警模型，做為台灣央行制定貨幣政策時的參考依據。
並列摘要
Purpose - Past literature on Taiwan's inflation forecasting mostly confines to only few theory-specific variables, which limits the possibility of other potential important variables. In view of the superior forecasts from the diffusion index method via incorporating large dimension information. Design/methodology/approach - We generalize the framework to allow for a richer spectrum that encompass and compare various linear/nonlinear, supervised/unsupervised dimensionality reduction methods. We collected nearly 100 potential variables, from the period of 2000 to 2021, in order to extract the hidden common factors for inflation forecasting. Findings - Among the examined 4 approaches, our results indicate that the supervised partial quantile regression (PQR) dominate the other 3 approaches in anticipating inflation. Research limitations/implications - Once we further divide variables into 11 categories and extract category-specific factors for the subsequent forecasting as in Stock and Watson (2002b), we found that the predictability of PQR became even better. Practical implications/Social implications - We can not only visualize the importance of each category in 1-step ahead inflation projection across time. Originality/value - We can establish an early warning model for monitoring the arrival of radical inflation/deflation and promptly adjusting for policy interventions.