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Improving Standard Moment Estimators of Beta Random Variables

改進Beta隨機變數之標準動差估計式

摘要


The beta distribution of the first kind, including two shape parameters, is a flexible curve specification in studying the classical moment method of statistical principles. The research of this paper, originating with a need similar to that in econometrics, further finds a sequence of explicit high-order moment estimators for the beta distribution. In addition to the trials of weighting different moment estimators, this research also examines a deserving-emphasis condition for estimating the classic four-parameter beta distribution, and permitting moment-equation substitution.

並列摘要


第一類型貝塔分佈,包括兩個形狀參數,是學習統計學原理中古典動差方法的一種易於伸縮曲線設定。本文的研究源自計量經濟學之相似需要,進一步發現貝塔分佈具有一系列外顯高階動差估計式。除了對不同估計式進行加權試驗外,這項研究還考察了一個值得重視的情況,估計經典的四參數貝塔分佈,並允許動差方程替換。

參考文獻


Andrews, D. W. K. (2000), "Inconsistency of the Bootstrap When a Parameter Is on the Bound- ary of the Parameter Space," Econometrica, 68, 399–405.
Carnahan, J. V. (1989), “Maximum Likelihood Estimation for the 4-Parameter Beta Distribu- tion," Communications in Statistics – Simulation and Computation, 18, 513–536. 99
Carrasco, M. and J.-P. Florens (2014), "On the Asymptotic Efficiency of GMM," Econometric 66 Theory, 30, 372–406.
Chang, S.-K. (2011), “A Computationally Practical Simulation Estimation for Dynamic Panel Tobit Models," Academia Economic Papers, 39, 1–32.
Chen, L.-Y. and S. Lee (2018), “Exact Computation of GMM Estimators for Instrumental Variable Quantile Regression Models," Journal of Applied Econometrics, 33, 553–567. 99

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