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We investigate the estimation methods of the multivariate non-stationary errors-in-variables models when there are non-stationary trend components and the measurement errorsMeasurement errors or noise components. We compare the maximum likelihood (ML)Maximum likelihood estimation estimation and the separating information maximum likelihood (SIML) estimation. The Gaussian likelihood function can have non-concave shape in some cases and the ML method works only when the Gaussianity of the non-stationary and stationary components holds with some restrictions in the parameter space. The SIML estimator has the asymptotic robust properties in more general situations. We study the finite sample and asymptotic properties of the ML and SIML methods for the non-stationary errors-in-variables modelsNon-stationary errors-in-variables models.

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Comparing Estimation Methods of Non-stationary Errors-in-Variables Models

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  • 发表日期：

    2025年

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  • Naoto Kunitomo,
  • Seisho Sato

摘要

We investigate the estimation methods of the multivariate non-stationary errors-in-variables models when there are non-stationary trend components and the measurement errorsMeasurement errors or noise components. We compare the maximum likelihood (ML)Maximum likelihood estimation estimation and the separating information maximum likelihood (SIML) estimation. The Gaussian likelihood function can have non-concave shape in some cases and the ML method works only when the Gaussianity of the non-stationary and stationary components holds with some restrictions in the parameter space. The SIML estimator has the asymptotic robust properties in more general situations. We study the finite sample and asymptotic properties of the ML and SIML methods for the non-stationary errors-in-variables modelsNon-stationary errors-in-variables models.