分整阶数半参数估计的有限样本性质研究  

Finite Sample Properties of Semi-parametric Estimation of Fractional Integration

作　　者：吴亮[1] 邓明[2] 

机构地区：[1]阜阳师范学院经济学院 [2]厦门大学经济学院

出　　处：《数量经济技术经济研究》2014年第12期142-158,共17页Journal of Quantitative & Technological Economics

基　　金：国家自然科学基金重点项目(71131008/G0113);教育部人文社会科学青年项目(13YJC910003);福建省自然科学基金项目(2014J01270);全国统计科研计划项目(2012LY015;2013LY044)的资助

摘　　要：随着对经济和金融时间序列长记忆性的研究,分整阶数估计已成为当前理论研究的焦点问题。以对数周期图回归和局部Whittle方法为代表的半参数分整阶数估计方法在实践中得到广泛应用,但对这两类半参数估计方法的有限样本性质的比较则鲜有涉及,影响了在实践中对估计方法的选择。利用蒙特卡洛模拟方法,在不同数据产生的过程下,对这两类半参数估计方法有限样本性质的研究结果表明:在ARFIMA(0,d,0)过程下,LW类估计量具有较好的小样本性质;在平稳ARFIMA(1,d,0)过程下,本文建议的QGPH估计量的有限样本性质要优于其他对数周期图估计量;在非平稳过程下,MGPH的偏差最小。With the study of long memory in economic and estimation of fractional integration order has become financial time series, a focus in theory research. Semi-parametric estimation with log periodogram regression and local Whittle method as its representatives is widely used in practice, but comparison of finite sample properties of two classes of semi-parametric estimation methods is the least studied and it influences choice of estimation methods in practice. This article gives a comprehensive overview about these two classes of semi-parametric estimation methods in theory and uses Monte Carlo method to study finite sample properties of two classes of semi-parametric estimation under different data generation processes. The simulation reveals that： in ARFIMA （0, d, 0） process, the LW group estimators have better small sample properties; in stationary ARFIMA （1, d, 0） process, the finite sample properties of QGPH estimator is better than other log periodogram estimators; in nonstationary process, the bias of MGPH estimators is minimum in all estimators.

关 键 词：长记忆性 分整阶数 半参数估计 蒙特卡洛模拟 

分 类 号：O212[理学—概率论与数理统计]