Adaptive Estimation in Time Serise Regression Models With Heteroskedasticity of Unknown Form
Adaptive Estimation in Time Serise Regression Models With Heteroskedasticity of Unknown Form
复制标题
未知形式异方差时间序列回归模型的自适应估计
DOI：
10.1017/s0266466600012743
复制
发表时间：
1992
影响因子：
0.8
通讯作者：
J. Hidalgo
中科院分区：
文献类型：
--
作者：
J. Hidalgo
在多元时间序列回归模型中，残差是异方差的，并且是未知形式的序列相关。GLS估计的回归系数使用核回归和谱方法被证明是自适应的，在这个意义上具有相同的渐近分布，到第一阶，作为GLS估计的基础上的知识的实际异方差和序列相关性。我们的估计性能的Monte Carlo实验。
In a multiple time series regression model the residuals are heteroskedastic and serially correlated of unknown form. GLS estimates of the regression coefficients using kernel regression and spectral methods are shown to be adaptive, in the sense of having the same asymptotic distribution, to the first order, as GLS estimates based on knowledge of the actual heteroskedasticity and serial correlation. A Monte Carlo experiment about the performance of our estimator is described.