摘要

由於Johansen的Gaussian VAR model的大部分實證研究結果都無法同時通過常態性與獨立性檢定，因此我們在應用共整合模型時，似乎有放寬 Gaussian VAR 之假設的必要。Saikkonen (1992), Saikkonen and Luukkonen (1997) 的共整合方法將有限期次向量自我迴歸模型 (Finite-Order VAR) 延伸為無限期次，並將殘差項為常態的假設摒除，除了更一般化外，也消除了 Johansen and Juselius (1990) 方法中殘差項經常不能通過常態性檢定的困擾。Saikkonen (1992), Saikkonen and Luukkonen ( 1997 ) 稱此為無限期次非常態的向量自我迴歸模型(Infinite Order Non-Gaussian Vector Autoregressive Process )。
本研究所採用的模型以Saikkonen (1992)、Saikkonen and Luukkonen (1997) 的無限期次非常態的向量自我迴歸模型為主，對於應用廣泛的 Johansen 之最大概似估計法和其他相關知識，則在文獻回顧中加以說明。此模式的估計，依順序會有5個步驟，其分別為：
(1)單根檢定。
(2) 決定落後期數k，以VAR(k) model 來漸近無限期次 VAR model。
(3) 檢定共整合數目。
(4) 估計共整合向量的係數，常數矩陣。
(5) 對步驟(4)的估計式做假設檢定。
本研究最主要的目的是：導出檢定共整合向量的統計式，並對共整合向量估計式做假設檢定，其結論是所推導的共整合向量的Wald Test形態的檢定，其分配將會是Chi-Square分配。

Nil

目次


第一章 緒論 1
第一節 動機與目的 1
第二節 方法及架構 3
第二章 文獻回顧與理論模型 4
第一節 相關文獻回顧 4
第二節 理論模型 17
第三章 估計步驟介紹 21
第一節 單根檢定 21
第二節 決定落後期數k 25
第三節 檢定共整合數目 26
第四節 估計共整合向量的係數及常數矩陣 30
第五節 對 和 做假設檢定 30
第四章 推導過程與分析 32
第五章 結論與建議 38
第一節 結論 38
第二節 建議 39
參考文獻 40

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