本文提出一個新的ARFIMA模型之長距相關參數估計方法。研究中利用Kullback-Leibler discrepancy找出與FI(d)模型最近似的ARMA(1,1)與ARMA(2,2)模型，並利用cubic spline決定出ARMA近似模型中的各個參數與d之關係式。再以此近似模式的概似函數作為長距相關參數d的估計目的函數。我們推導出此新估計量的大樣本性質。且經模擬生成的資料，評估此估計方法在小樣本之下的表現，並與先前的參數估計方法做比較。在實證分析上，以尼羅河水位資料作實例探討。

A new method for estimating long-memory parameter in ARFIMA Models is proposed based on ARMA approximation. The Kullback-Leibler discrepancy is used to find a best ARMA approximation for a FI(d) model. The performance of the new estimator is investigated and compared to previous methods in finite sample via simulations. The Nile River data are used for illustration.

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