Comparing estimation methods of non-stationary errors-in-variables models
Comparing estimation methods of non-stationary errors-in-variables models
复制标题
非平稳变量误差模型的估计方法比较
DOI：
10.1007/s42081-019-00051-1
复制
发表时间：
2020
影响因子：
1.3
通讯作者：
Naoki Awaya and Daisuke Kurisu
中科院分区：
文献类型：
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作者：
Naoto Kunitomo;Naoki Awaya and Daisuke Kurisu
本文研究了当存在非平稳趋势分量和测量误差或噪声分量时，多元非平稳变量含误差模型的估计方法。本文比较了最大似然估计和分离信息最大似然估计。后者是由Kunitomo和Sato提出的（趋势、季节性和经济时间序列：非平稳变量误差模型。MIMS-RBP-SDS-3，MIMS，明治大学。 http://www.mims.meiji.ac.jp/ ，2017）和Kunitomo等人（高频金融数据的分离信息最大似然方法。Springer，柏林，2018）。我们发现高斯似然函数在某些情况下可以具有非凹形状，并且ML方法仅在非平稳和平稳分量的高斯性在参数空间中具有一些限制（例如信噪比）时才有效。在更一般的情况下，SIML估计具有渐近稳健性。我们研究了非平稳误差变量模型的ML和SIML方法的有限样本和渐近性质。
We investigate the estimation methods of the multivariate non-stationary errors-in-variables models when there are non-stationary trend components and the measurement errors or noise components. We compare the maximum likelihood (ML) estimation and the separating information maximum likelihood (SIML) estimation. The latter was proposed by Kunitomo and Sato (Trend, seasonality and economic time series: the nonstationary errors-in-variables models. MIMS-RBP-SDS-3, MIMS, Meiji University. http://www.mims.meiji.ac.jp/ , 2017) and Kunitomo et al. (Separating information maximum likelihood method for high-frequency financial data. Springer, Berlin, 2018). We have found that the Gaussian likelihood function can have non-concave shape in some cases and the ML method does work only when the Gaussianity of non-stationary and stationary components holds with some restrictions such as the signal–noise variance ratio in the parameter space. The SIML estimation has the asymptotic robust properties in more general situations. We explore the finite sample and asymptotic properties of the ML and SIML methods for the non-stationary errors-in variables models.