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| Title: | 長期資料之隨機效果模型分析－公司每股盈餘與財務比率之關聯性研究
Random effect model in longitudinal data--the empirical study of the relationship among EPS & financial ratios |
| Authors: | 楊慧怡
Yang, Hui-Yi |
| Contributors: | 鄭宗記
Cheng, Tsung-Chi
楊慧怡
Yang, Hui-Yi |
| Keywords: | 長期性資料
隨機效果模型
每股盈餘
Longitudinal data
Random effect model
Earnings per share
AR(1) |
| Date: | 2000 |
| Issue Date: | 2009-09-18 19:07:59 (UTC+8) |
| Abstract: | 長期性資料(longitudinal data)，是指對同一個觀察個體(subject)或實驗單位(experiment unit)，在不同時間點上重複觀察或測量一個或多個變數。雖然觀察個體之間互相獨立，但就同一個個體而言，不同時間的觀察或測量常常是有相關性的。且觀察的個體之間可能由於一些無法測量的環境因素造成個體之間有差異，因此在傳統橫斷面分析中，假設其有相同迴歸係數的邊際模型可能不合理。隨機效果模型可以解決長期資料分析的相關，並假設每個個體的迴歸係數不同；此模型不但可以說明橫斷面資料的cohort效果，也可直接解釋長期資料的age效果；更可以區分個體之間與個體之內的變異。
本研究以1995年至2000年台灣11個產業中的100家公司之每股盈餘與各財務比率，作為實證分析的資料；分別配適每股盈餘與時間、產業別、時間產業別交互作用及財務比率及排除每股盈餘有異常值後之邊際效果模型(一般迴歸分析)及隨機效果模型，並比較其參數估計之異同。實證結果顯示，一般迴歸分析與假設誤差不相關且等變異下的隨機效果模型參數估計相似，但後者能區分變異為個體之間(between-subjects)與個體之內(within-subject)的變異。而假設誤差不相關且不等變異與假設誤差服從AR(1)且不等變異下的隨機效果模型估計相近。實證結果並顯示，在排除異常值後的模型參數估計，一般迴歸分析不論是估計值及顯著性大多沒有很大差別；而隨機效果模型的估計在排除異常值前後較有差別。特別是現金流量比率(CFR)原本為不顯著變數，在排除異常值後的模型配適全部變顯著性變數。
The defining characteristic of a longitudinal study is that individuals are measured repeatedly through time. Although it is independent between subjects, the set of observations on one subject tends to be inter-correlated. Because there is some natural heterogeneity due to unmeasured factors between subjects, it is not corrected to assume they have the same regression coefficients. A random effect model is a reasonable description about the different regression coefficients, and it can resolve the inter-correlation of the observations on one subject. The major advantages of the random effect model are its capacity to separate what in the context of population studies are called cohort and age effects, and it can distinguish the variations between subjects and within subjects.
This study describes the marginal model and random effect model, and shows their difference by real data analysis. We apply these models to the earnings per share (EPS) and other financial ratios of one hundred companies in Taiwan, which are distributed in eleven industries. The results show that the parameter estimates of the marginal model and random effect model are similar when error structure is independent and of equal variance. Furthermore, the latter can distinguish the variations between subjects and within subjects. However, the residual analysis reveals that the error structure may not be constant. Therefore, we consider heteroscedasticity error in random effect model. We also assume that error follows an autoregressive process (e.g. AR(1) model), which leads to the optimum among our results in terms of residual analysis.
There are some observations that appear to be outlying from the majority of data. The results show little difference in the marginal models no matter whether those outliers are included. However, we obtain different results in the random effect models. Especially, the variable of “cash flow ratio” becomes significant once those potential outliers have been excluded, while it is not significant when all cases are fitted in the model. |
| Reference: | 一、中文部分
簡銘宏，「運用財務比率預測每股盈餘之研究」，國立政治大學會計學研究所，民國七十九年未出版論文。
吳瑞源，「財務比率預測每股盈餘之研究」，國立政治大學會計學研究所，民國八十一年未出版論文。
潘志青，「財務比率對未來會計盈餘及市場報酬變化之研究」，國立政治大學會計學研究所，民國八十三年未出版論文。
邱維正，「台灣地區股票上市公司資本結構再研究」，國立中正大學財務金融研究所，民國八十年未出版論文。
臺灣經濟經報資料庫，台灣經濟新報，民國七十九年出版。
陳隆麒，「現代財務管理-理論與應用」，華泰書局，民國八十二年一月修訂版。
陳毓家，「長期資料迴歸分析離群值診斷」，國立成功大學，民國八十六年未出版論文。
簡莉珠，「長期資料迴歸分析影響值診斷」，國立成功大學，民國八十八年未出版論文。
劉美足，「長期資料迴歸影響值的偵測-疊代診斷法」，國立成功大學，民國八十九年未出版論文。
二、英文部分
Azzalini, A. (1984). Estimation and hypothesis testing for collections of autoregressive time serial. Biometrika, 1, 85-90.
Box, G. P. and Jenkins, G. M. (1970). Time series analysis: forecasting and control. Jolden-Day, San Francisco, California.
Diggle, P. J.,Liang, K.-Y. and Zeger, S.L. (1994). Analysis of Longitudinal Data. Oxford, New York.
Draper, N. R. and Smith, H. (1998). Applied Regression Analysis.
Fearn, T. (1975). A two-stage model for growth curves which leads to Rao’s covariance-adjusted estimates. Biometrika , 64, 141-43.
Grizzle, J. E. and Allen, D. M. (1969). Analysis of growth and dose response curves. Biometrics, 2, 357-81.
Harville, D. (1977). Maximum likelihood estimation of variance components and related problems. Journal of the American Statistical Association, 72, 320-40.
Hui, S. L. (1984). Curve fitting for repeated measurements made at irregular time points. Biometrics , 40, 691-97.
Jones, R. M. (1993). Longitudinal data with serial correlation: a state-space approach. Chapman and Hall, London.
Korn, E. L. and Whittemore, A. S. (1979). Methods for analyzing panel studies of acute health effects of air pollution. Biometrics, 35, 795-802.
Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics , 38, 963-74.
Liang, K.-Y. (2000). Longitudinal data analysis. Summer Institute of Biostatistical Science(SIBS).
Liang, K.-Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika , 73, 13-22.
Pinheiro, J. C. and Bates, D. M. (2000). Mixed-Effects Models in S and S-PLUS. Springer-Verlag, New York.
Rao, C. R. (1965). The theory of least squares when the parameters are stochastic and its application to the analysis of growth curves. Biometrika, 52, 447-58.
Rao, C. R. (1975). Simultaneous estimation of parameters in different linear models and applications to biometric problems. Biometrics, 31, 545-54.
Stiratelli, R., Laird, N. and Ware, J. H. (1984). Random effects models for serial observations with binary responses. Biometrics, 40, 961-71.
Ware, J. H. (1985). Linear models for the analysis of longitudinal studies. The American Statistician, 39, 95-101.
Zeger, S. L. and Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcome. Biometrics, 42, 121-30.
Zeger, S. L., Liang, K.-Y., and Albert, P. S. (1988). Models for longitudinal data: a generalized estimating equation approach. Biometrics, 44, 1049-60.
Zeger, S. L. and Karim, M. R. (1991). Generalized linear models with random effects: a Gibbs sampling approach. Journal of the American Statistical Association, 86, 79-86. |
| Description: | 碩士
國立政治大學
統計研究所
88354020
89 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#A2002001346 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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