在迴歸模型之降維問題中，Li(1991)提出sliced inverse regression(SIR)方法。唯此法僅適用於彼此獨立之數據，且有誤差須服從常態分配的限制。鑒於此，Bura and Cook(2001)提出parametric inverse regression(PIR)方法，並以模擬法展示其在小樣本下，相對於SIR法之優越性。Becker et al.(2000)提出dynamical sliced inverse regression(DSIR)作為SIR在處理時間相關數據之推廣，模擬顯示在真實維度K=1及K=2時，估計到正確維度佔滿高的比例。
本論文溶合PIR和DSIR的想法，探討PIR降維法在時間序列數據之適用性，討論可能遭遇之困難及解決之道，提出修正的方法，並以模擬法展示此法在維度之估計，及真實空間之解釋上均有不錯的表現。最後以實證探討影響台灣消費者物價指數與台積電股價投資報酬率之因素。

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