通貨膨脹 (亦簡稱通膨) 作為一個長期存在且影響深遠的經濟現象，一直以來受到產官學各界之關注，各國央行竭盡所能控制通膨，各方研究不遺餘力證明通膨指標之預測價值，然而結論卻仍眾說紛紜。細究其因不免發現指標間可能因不同國家、採樣期間、資料頻率、計算公式或計量模型…等眾多因素而產生預測上之差異。為此，本文參考多方學者的研究成果，聚焦於分解通膨 (disaggregation) 及跨通膨指數細項間之交互影響。首先，利用由上而下法篩選出各通膨成分中之高相關性因子共計 21 組，接著導入 ADF 檢定確保數據平穩性，透過 Granger 因果關係檢定以及改良式迴歸檢定模型，再加入實驗組及對照組交互比對，以求尋找出最具前瞻性之預測指引。根據本文實證結果顯示，CPI 項下之食物項目對 PPI 具有預測價值，然而對於能源項目卻缺乏顯著預測力；其次，該住房項目僅對 PPI 之服務類成本產生預測價值，對於建築類成本則呈現出季節性效應而缺乏預測能力；另外，建築項目在不同模型中亦出現反向之預測效果，顯示除了季節性干擾因子外，其他暫時性干擾因子的存續狀態將比預期來得更長久。除此之外，其他關鍵的研究發現還包括，相對於 CPI，PCE 對於各種模型檢定皆展現出高度一致性，且 PCE 及其細項對核心 PPI 的解釋力甚至可達 CPI 所未及之處。本文特別提醒，除了需注意能源及建築相關項目之干擾因子外，地緣政治所造成的體制改變 (regime change)，以及動態數據本身之結構性變化，皆可能造成模型的無效率，期以後續研究者能因此避免或降低設定偏誤，並嘗試以創新思維來解讀這些通膨數據。

As a long-standing and far-reaching economic phenomenon, inflation has always attracted the attention of various circles of society. Central banks are doing their best to control inflation. Various literatures have also tried to prove the predictive value of inflation indices. However, the conclusions are still controversial. These gaps may come from different countries, sampling periods, data frequencies, formulas, and models. Therefore, this paper focuses on the interactions of US inflation indices and their components and aims to separate possible biases in aggregated inflation. The author investigates the dynamic behavior across the indices’ components to gather four empirical conclusions: (1) food under CPI has a forecasting value for PPI but lacks predictive power for energy. (2) Housing only has predictive value for service costs under PPI, yet it lacks predictive capabilities and shows seasonality for construction costs. (3) Construction shows reverse conclusion in different models, in addition to seasonality factor, which indicates that other transient disruptive factors will persist longer than expected. (4) Compared with CPI, PCE shows higher consistency across various model tests. The estimating power of PCE and its components on core PPI can even reach fields that CPI cannot. In addition to observing the transient disruptive factors of energy and construction, regime changes caused by geopolitics and structural changes in the dynamic data may also cause the inefficiency of the models.

目錄
1. 前言...............1
2. 文獻回顧............3
3. 研究方法............5
4. 實證結果...........15
5. 結論...............44
參考文獻..............46
附錄..................50

中文部分
1. 呂捷與王高望 (2015)，「CPI 與 PPI “背離”的結構性解釋」，《經濟研究》，50，136-149。

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