政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/85895

政大典藏 > College of Commerce > Department of Statistics > Theses > Item 140.119/85895

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/85895

Title:	時間數列模式之拔靴模擬法研究
Authors:	郭玉麟
Contributors:	鄭天澤郭玉麟
Keywords:	拔靴法非常態ARMA(p,q) 最小平方法 bootstrap non-normal ARMA(p,q) least-square
Date:	1998
Issue Date:	2016-04-21 09:55:15 (UTC+8)
Abstract:	本篇文章之主要目的是將Efron於1979年所提出之拔靴法(Bootstrap method)應用在非常態ARMA(p,q)模式上。我們考慮的結構包括AR(1)，AR(2)，MA(1)，MA(2)，ARMA(1,1)，而考慮的非常態干擾分配則包括對數常態分配，均勻分配，迦瑪分配以及指數分配，對每一個模式，所設定之參數值組合涵蓋了使模式平穩及/或可逆之參數組合中的重要區域。我們比較傳統的最小平方法與利用500個拔靴重複子(Bootstrap repetitions)的拔靴法在參數估計上的差異。進一步我們也以MAE及MAPE等準則比較了這兩種估計模式在預測上的優劣。模擬結果顯示，在參數估計方面，當所設定的參數範圍接近非平穩或非可逆條件時，拔靴法所獲得的參數估計值表現會較佳。然而在預測效益方面，利用往前一期預測法，並配合拔靴法重新改造的數列，在具有非常態干擾項AR(1)，AR(2)以及ARMA(1,1)模式的預測效益上的確有較佳的效果。 In this thesis, the Bootstrap technique proposed by Efron in 1979 is applied to parameter estimation and forecasting of non-normal ARMA(p,q) time series models. A simulation study is conducted where artificial time series are generated from various ARMA structures with different non-normal noise distributions. The ARMA structures considered in the simulation are AR(1), AR(2), MA(1), MA(2) and ARMA(1,1), while the non-normal noise distributions include Log-normal, Uniform, Gamma, and Exponential distributions. For each structure, the parameter values used cover important regions of the stationary and/or invertible parameter space . The conventional least-square estimators of the parameters are compared with the corresponding non-parametric Bootstrap estimator, obtained by using 500 Bootstrap repetitions for each series. Furthermore, forecasts based on these estimated model are also compared by using such criteria as MAE and MAPE .
Reference:	1. Aczel, A. D. and Josephy, N. H. (1992), "Using the bootstrap for Improved ARIMA Model Identification," Journal of Forecasting, 11, 71-80. 2. Box, G. E. P., Jenkins, G. M., and Reinsel, G. C., (1994), Time Series Analysis: Forecasting and Control, U. S. A.: Prentice Hall, Inc. 3. Chatterjee, S. (1986), "Bootstrap ARMA models: Some Simulations,"IEEE Transactions. on Systems, Man and Cybernetics; U. SMC 16, 2, 294-297. 4. Efron, B. (1979), "Bootstrap Methods: Another Look at the Jacknife," Annals of Statistics, 7, 1-26. 5. Efron, B. (1982), The Jacknife, the Bootstrap and Other Resampling Plans, SIAM Monograph, No. 38. 6. Efron, B. and Tibshirani, R. J. (1986) "Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy," Statistical Science, 1, 54-77. 7. Efron, B. and Tibshirani, R. J. (1993), An Introduction to the Bootstrap, New York.: Chapman&Hall. 8. Freedman, D. A.,(1981) "Bootstraping regression model", The Annals of Statistics, 9, 1218-28. 9. Freedman, D. A. and Peters, S. C.,(1984)"Bootstraping a regression equation: some empirical results", Journal of the American Statistical Association , 79, 97-106. 10. Holbert, D. and Son, M. S. (1986), "Bootstraping a Time Series Model: Some Empirical Results," Communication in Statistics Theory and Methods, 15(12), 3669-3691. 11.Jeremy Berkowitz and Lutz Kilian(1996), "Recent Developments in Bootstrapping Time Series" . 12. Masarotto, G.(1990), "Bootstrap Prediction Intervals for Autoregressions," International Journal of Forcasting, 6, 229-239. 13. Souza, R. C. and Neto, A. C. (1996), "A Bootstrap Simulation Study in ARMA(p,q) Structure," Journal of Forecasting, 15, 343-53. 14. Stine, R. A. (1987), "Estimating Properties of Autoregressive Forecasts," Journal of the American Statistical Association, 82, 1072-1078.
Description:	碩士國立政治大學統計學系 86354011
Source URI:	http://thesis.lib.nccu.edu.tw/record/#B2002001567
Data Type:	thesis
Appears in Collections:	[Department of Statistics] Theses

Files in This Item:

There are no files associated with this item.

社群 sharing

Loading...