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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

央行貨幣政策操作對短期利率的影響

文淑芬 Unknown Date (has links)
本研究分為兩個部分,第一個部分為探討1990年來英美等國央行貨幣政策操作改革方向,期望貨幣市場的金融同業隔夜拆款利率,沿著隔夜拆款目標利率微幅波動。 英國央行原採零準備率制度,不易估測貨幣市場資金,其隔夜拆款利率波動幅度較美國為劇,為有效控制操作目標, 2006年5月起實施「自願準備金制度」,有利英國央行進行公開市場操作,達成穩定利率的效果。 第二部分參考Nadja(2006)一文,探討我國央行貨幣政策操作對短期利率之影響,係以隔夜拆款利率與目標利率的利差為利率函數模型之因變數,其中以重貼現率為目標利率,並以超額準備為主要的操作變數。 本文以最小平方估計法(OLS)實證結果發現,央行貨幣政策操作有效地影響隔夜拆款利率;惟2003年起央行不以重貼現率為隔夜拆款利率的底限,貨幣政策操作對隔夜拆款利率與重貼現率之間的利率變動並無顯著性的影響,亦即央行已放棄重貼現率為隔夜拆款利率之目標利率,而係積極地進行之公開市場操作,穩定短期利率。
2

群集樣本具巢狀誤差結構之迴歸分析 / Regression analysis for cluster samples with nested-error structure

賴昭如 Unknown Date (has links)
分析具有巢狀誤差結構的迴歸模式時,惹忽略隨機誤差項之間的相關性,而採用最小平方(OLS)估計量所導出的標準 F 統計量(以 F<sup>S</sup>表之)進行檢定,會導致過大的型 I 錯誤機率;若將隨機誤差項之間的相關性納入考量,而採用廣義最小平方(GLS)估計量所導出的 F 統計量 (以 F<sup>GLS</sup>表之),則計算上會較為繁雜。因此我們藉由轉換方式,將模式轉換成隨機誤差項之間彼此獨立的新模式後,再以 F<sup>S</sup> 進行檢定,其結果與直接以 F<sup>GLS</sup> 檢定相同,且可使計算較為方便。由於模式轉換所需的轉換矩陣為母體變異數的函數,因此當母體變異數未知時,我們以 Henderson 的常數配適 (fitting-of-constants)方法來估計之。藉由模擬結果得知,若各段的觀察個數相等,則不論巢狀誤差結構為二段式(two-stage)或三段式(three-stage),廣義最小平方估計量(GLS)均較最小平方估計量(OLS)表現穩定,且 F<sup>GLS</sup> 在檢定力及實際顯著水準方面的表現也都比 F<sup>S</sup> 好。 / When analyzing the regression model with nested-error structure, if the correlations between errors are ignored, and conduting the model adequacy test by the standard F statistic (F<sup>S</sup>) led from the ordinary leastsquares estimator (OLSE) , then the type I error rate will be inflated. However, if the corrlated structure is considered and the model is tested by F<sup>GLS</sup> led from the general least-squares estimator (GLSE) , the calculation will be more complicate. The model can be transformed to a new model with independent random errors and then, tested by F<sup>S</sup> . The result is the same as the one by F<sup>GLS</sup> , also it is more convenient for calculation. Since the transformation matrix is a function of variance components, we estimate variance components by Henderson's fitting-of-constants when they are unknown. Through simulation, it is concluded that if the observations in each stage of nested-error structure are the same, the GLSE is more stable than the OLSE in both two-stage and tree-stage structures. Also, the power and the sizes of F<sup>GLS</sup> will perform better than those of F<sup>S</sup> .

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