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研究生:張雅梅
研究生(外文):Chang, Ya-Mei
論文名稱:ARFIMA模式中長距相關參數的估計方法:ARMA近似模型
論文名稱(外文):Estimation of Long-Memory Parameter in ARFIMA Models: ARMA Approximation Approach
指導教授:徐南蓉徐南蓉引用關係
指導教授(外文):Hsu, Nan-Jung
學位類別:碩士
校院名稱:國立清華大學
系所名稱:統計學研究所
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:38
中文關鍵詞:ARMA長記憶模式參數估計Kullback-Leibler discrepancy
外文關鍵詞:ARMAlong memoryestimationKullback-Leibler discrepancy
相關次數:
  • 被引用被引用:1
  • 點閱點閱:335
  • 評分評分:
  • 下載下載:48
  • 收藏至我的研究室書目清單書目收藏:0
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.
1 Introduction
2 ARFIMA Model and its ARMA Approximation
2.1 ARFIMA Models
2.2 ARMA Approximations for FI(d) models
2.3 ARMA Approximations for ARFIMA models
3 Estimation Methods for Long-Memory Parameter
3.1 Classical Estimation Methods
3.2 Proposed Method
4 Numerical Simulation
5 Application
6 Conclusion
[1]Aydogan, Kursat and Booth, G. Geoffrey (1988), Are there long cycles in common stock returns?, Southern Economic Journal, 141-149.
[2]Basak, G.K., Chan, N.H. and Palma, W. (2001), The approximation of long-memory process by an ARMA model, Journal of Forecasting, 367-389.
[3]Beran, J. (1994), Statistics for long-memory process, Chapman and Hall, New York.
[4]Beran, J. and Terrin, N. (1996), Testing for a change of the long-memory parameter, Biometrika}, 83, No. 3, 627-638.
[5]Brockwell, P.J. and Davis, R.A. (1987), Time series: Theory and Methods (first edition), Springer, New York.
[6]Chan, N.H. and Palma, W. (1998), State space modelling for long-memory processes, Ann. Statist., 26, 719-740.
[7]Diebold, F.X. and Rudebusch, Glenn D. (1989), Long memory and persistence in aggregate output, Journal of Monetary Economics, 24, 189-209.
[8]Fox, Robert and Taqqu, Murad S. (1986), Large-Sample properties of parameter estimates for strongly dependent stationary Gaussian time series, Ann. Statist., 14, 517-532.
[9]Geweke, John F. and Porter-Hudak, Susan (1982), The estimation and application of long memory time series models, Journal of Time Series Analysis, 4, 221-238.
[10]Graf, H.P. (1983), Long-range correlations and estimation of the self-similarity parameter, PhD thesis, ETH Zurich.
[11]Granger, C.W.J and Joyeux, R. (1980), An introduction to long-range time series models and fractional differencing, J. Time Ser. Anal., 1, 15-30.
[12]Hosking, J.R.M. (1981), Fractional differencing, Biometrika, 68, 165-176.
[13]Hosking, J.R.M. (1984), Modelling persistence in hidrological time series using fractional differencing, Water Resources Reserch, 20, 1898-1908.
[14]Hsu, N.J. and Breidt, F.J. (2002), Bayesian analysis of fractionally integrated ARMA with additive noise, Journal of Forecasting, in press.
[15]Whittle, P. (1953), Estimation and information in stationary time series, Ark. Mat., 2, 423-434.
[16]Tsay, Wen-Jen (2000), Long memory story of the real interest rate, Economics Letters, 67, 325-330.
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