Asymptotic theory for certain regression models with long memory errors
Asymptotic theory for certain regression models with long memory errors
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某些具有长记忆误差的回归模型的渐近理论
DOI：
10.1111/1467-9892.00057
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
R. Deo
中科院分区：
文献类型：
--
作者：
R. Deo
线性长记忆序列的加权线性组合的渐近分布对于某些权重来说是正态的。该结果可用于导出多项式趋势的最小二乘估计量和固定傅立叶频率下的周期图的极限分布。还获得了固定傅立叶频率下锥形周期图渐近相对偏差的封闭形式表达式。加权最小二乘估计量对于多项式趋势回归量是渐近有效的，并且被证明是渐近正态的。
The asymptotic distribution of a weighted linear combination of a linear long memory series is shown to be normal for certain weights. This result can be used to derive the limiting distribution of the least squares estimators for polynomial trends and of the periodogram at fixed Fourier frequencies. A closed form expression for the asymptotic relative bias of the tapered periodogram at fixed Fourier frequencies is also obtained. A weighted least squares estimator, which is asymptotically efficient for polynomial trend regressors, is shown to be asymptotically normal.