臺灣博碩士論文加值系統

在描述多變量時間序列資料的變動性上，多變量自我迴歸模型是文獻上被廣泛使用的方法之一，而常見之配適模型方法為AIC或BIC準則。這兩種準則雖然能夠容易的選取模式，但因為受到不顯著異於零的參數影響而導致參數估計不準確或預測誤差偏大。本研究即利用Lütkepohl（1991）提出之Top-Down準則和Bottom-Up準則及Tibshirani（1996）提出之「Least absolute shrinkage and selection operator」方法來縮減這些不顯著異於零的參數，而在不同情況下比較這些方法是否能更為準確的估計參數或降低預測誤差。

第一章 文獻探討與問題敘述 1
第二章 多變量自我迴歸模型 3
第三章 參數估計及配適模型準則 5
3.1 多變量最小平方法和最適模型選取準則 5
3.2 線性限制式下之多變量最小平方法和最適模型選取準則 6
3.3 Bottom-Up準則 8
3.4 LASSO 9
第四章 模擬分析 10
4.1 在各種模型設定下比較不同配適模型方法之優劣 13
4.1.1 真實模型為VAR 13
4.1.2 真實模型為VMA 24
4.2 探討樣本數如何影響各配適模型方法之優劣 29
第五章 實證分析 36
第六章 結論與後續研究 42
參考文獻 43

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