摘要翻译：
本文提出了一种用于时变ARCH过程参数估计的递推在线算法。该估计是通过在时间点$t-1$用关于时间点$t$的观测更新估计量来完成的，以产生在时间点$t$的参数估计量。在非平稳情况下研究了该估计量的抽样性质，特别是建立了渐近正态性和非平稳性偏差的表达式。通过并行运行两种不同步长的递归在线算法，并对估计量进行线性组合，可以提高1~2阶H\\{o}级参数曲线的收敛速度。
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英文标题：
《A recursive online algorithm for the estimation of time-varying ARCH
  parameters》
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作者：
Rainer Dahlhaus, Suhasini Subba Rao
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最新提交年份：
2007
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分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  In this paper we propose a recursive online algorithm for estimating the parameters of a time-varying ARCH process. The estimation is done by updating the estimator at time point $t-1$ with observations about the time point $t$ to yield an estimator of the parameter at time point $t$. The sampling properties of this estimator are studied in a non-stationary context -- in particular, asymptotic normality and an expression for the bias due to non-stationarity are established. By running two recursive online algorithms in parallel with different step sizes and taking a linear combination of the estimators, the rate of convergence can be improved for parameter curves from H\"{o}lder classes of order between 1 and 2.
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PDF链接：
https://arxiv.org/pdf/708.4081