Nonparametric transformation to white noise
Nonparametric transformation to white noise
复制标题
非参数变换为白噪声
DOI：
10.1016/j.jeconom.2007.05.018
复制
发表时间：
2008
影响因子：
6.3
通讯作者：
Linton O
中科院分区：
文献类型：
--
作者：
Linton O
本文考虑了一个半参数分布滞后模型，其中“新闻影响曲线”m是非参数的，但通过一些线性滤波器的响应是动态的。一个特殊的例子是具有序列相关误差的非参数回归。我们提出了一个估计的新闻影响曲线的基础上产生白色噪声误差的动态变换。这产生了m的估计方程，该估计方程是第二类线性积分方程。我们调查固定的情况下，错误的情况下，有一个单位根。在平稳的情况下，我们建立逐点渐近正态性。在非参数回归服从时间序列误差的特殊情况下，我们的估计量比通常的估计量实现了效率改进，参见Xiao et al. [2003]。误差自相关非参数回归中局部多项式估计的改进。美国统计协会杂志98，980-992]。在单位根的情况下，我们的程序是一致的，渐近正态的，不像标准的回归平滑。我们还提出了分布理论的参数估计，这是非标准的单位根的情况下。我们还通过仿真实验研究了它的有限样本性能。
We consider a semiparametric distributed lag model in which the “news impact curve”m is nonparametric but the response is dynamic through some linear filters. A special case of this is a nonparametric regression with serially correlated errors. We propose an estimator of the news impact curve based on a dynamic transformation that produces white noise errors. This yields an estimating equation for m that is a type two linear integral equation. We investigate both the stationary case and the case where the error has a unit root. In the stationary case we establish the pointwise asymptotic normality. In the special case of a nonparametric regression subject to time series errors our estimator achieves efficiency improvements over the usual estimators, see Xiao et al. [2003. More efficient local polynomial estimation in nonparametric regression with autocorrelated errors. Journal of the American Statistical Association 98, 980–992]. In the unit root case our procedure is consistent and asymptotically normal unlike the standard regression smoother. We also present the distribution theory for the parameter estimates, which is nonstandard in the unit root case. We also investigate its finite sample performance through simulation experiments.