Asymptotic properties of estimators for autoregressive models with errors in variables
Asymptotic properties of estimators for autoregressive models with errors in variables
复制标题
变量误差自回归模型估计量的渐近性质
DOI：
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发表时间：
1996
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通讯作者：
K. Chanda
中科院分区：
文献类型：
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作者：
K. Chanda
设{X t， t∈Z}是随机变量的可观测严格平稳序列，设X t = U t + et，其中{U t}是AR (p)， {e t}是表示{X t}测量误差的严格平稳序列，其中e {e 1} = 0。在{e t}的广义假设下，我们建立了由一组修正Yule-Walker方程计算的自回归参数的标准估计量的相合性和收敛率。
Let {X t , t ∈ Z} be an observable strictly stationary sequence of random variables and let X t = U t + e t , where {U t } is an AR (p) and {e t } is a strictly stationary sequence representing errors of measurement in {X t }, with E{e 1 } = 0. Under some broad assumptions on {e t } we establish the consistency properties as well as the rates of convergence for the standard estimators for the autoregressive parameters computed from a set of modified Yule-Walker equations.