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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/58668
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/58668


    Title: 運用向量自我迴歸模型與最大交叉相關預測時間序列
    Forecasting of Time Series based on Vector Autoregression Model and Maximum Cross-correlation
    Authors: 陳寬旻
    Contributors: 洪英超
    陳寬旻
    Keywords: 領先關係
    自我向量迴歸
    交叉相關
    Wald檢定
    預測平方誤差
    Granger causality
    Vector autoregressive (VAR) model
    Cross-correlation
    Wald test
    Mean prediction squared errors (MPSE)
    Date: 2012
    Issue Date: 2013-07-01 17:01:31 (UTC+8)
    Abstract: 對具時間序列型態的多變量資料進行預測時,模型的選取至關重要。長年以來,文獻中多以向量自我迴歸模型(VAR 模型)進行預測。其缺點是:(i)模型選取複雜;(ii)參數估計不易;(iii)模型假設常不符;(iv)估計模型所需資料量較大。本文提供了一個新的多變量時間序列預測方法,此方法主要建構在最大交叉相關性之上,資料僅需在短期時間內滿足相當程度的線性關係。本文並與時間序列應用相當廣泛的向量自我迴歸模型預測方法做比較,希望提供使用者實務分析上的預測方法選取準則。藉由台灣國內各股票型基金淨值以及各基金所含之股票型投資組合資料,本文比較此二種方法對於基金淨值的波動所提供之預測效果。以各預測方法之預測平方誤差作為評量標準,本文發現利用最大交叉相關的方法之預測效果較向量自我迴歸模型更佳。
    The selection of methods plays an important role in the prediction based on time-series data. In most literature reviews, the vector autoregression model(VAR) has been a popular choice for prediction for many years. There are some disadvantages of this method: (i) the model selection procedure can be really complex; (ii) the model assumptions are difficult to validate; (iii) it requires a large amount of data for model building. The objective of this thesis is to provide an new multivariate-time series prediction method based on the concept of maximum cross-correlation. It requires merely the assumption of “fair linearity” between two time series under investigation. This thesis also compares the proposed method to the vector autoregressive (VAR) model which is widely used in time series analysis with the expectation to provide a new prediction method in practical data analysis. We use data from the Taiwan equity funds and the portfolio of those funds to compare the prediction performances of these two methods. Using the mean prediction squared errors (MPSE) as assessment criterion, the prediction method based on the maximum cross-correlation best performs under all prediction periods.
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    Description: 碩士
    國立政治大學
    統計研究所
    100354017
    101
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0100354017
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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