论文标题
用于分数本地与统一根误差的回归模型的渐近理论
Asymptotic theory for regression models with fractional local to unity root errors
论文作者
论文摘要
本文在误差具有分数局部到统一根（FLUR）模型结构时，为参数和非参数回归模型开发了渐近理论。 Flur模型是静止的时间序列，具有半范围依赖性属性，其协方差函数类似于中度滞后的长存储器模型，但最终根据由非中心参数控制的衰减因子的存在迅速减少。当此参数取决于样本大小时，这些回归模型的渐近正态性允许具有包括长，半长的和短记忆过程的行为的广泛随机过程。
This paper develops the asymptotic theory for parametric and nonparametric regression models when the errors have a fractional local to unity root (FLUR) model structure. FLUR models are stationary time series with semi-long range dependence property in the sense that their covariance function resembles that of a long memory model for moderate lags but eventually diminishes exponentially fast according to the presence of a decay factor governed by a noncentrality parameter. When this parameter is sample size dependent, the asymptotic normality for these regression models admit a wide range of stochastic processes with behavior that includes long, semi-long, and short memory processes.
