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| 担当者 | 吉田朋広、増田弘毅、荻原哲平、小池祐太 |
|---|---|
| 目的 | 確率統計学およびその関連領域に関する研究発表, 研究紹介を行う. |
2007年01月17日(水)
16:20-17:30 数理科学研究科棟(駒場) 128号室
玉置 健一郎 氏 (早稲田大学)
Second order optimality for estimators in time series regression models
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2006/17.html
玉置 健一郎 氏 (早稲田大学)
Second order optimality for estimators in time series regression models
[ 講演概要 ]
We consider the second order asymptotic properties of an efficient frequency domain regression coefficient estimator $\\hat{\\beta}$ proposed by Hannan (1963). This estimator is a semiparametric estimator based on nonparametric spectral estimators. We derive the second order Edgeworth expansion of the distribution of $\\hat{\\beta}$. Then it is shown that the second order asymptotic properties are independent of the bandwidth choice for residual spectral estimator, which implies that $\\hat{\\beta}$ has the same rate of convergence as in regular parametric estimation. This is a sharp contrast with the general semiparametric estimation theory. We also examine the second order Gaussian efficiency of $\\hat{\\beta}$. Numerical studies are given to confirm the theoretical results.
[ 参考URL ]We consider the second order asymptotic properties of an efficient frequency domain regression coefficient estimator $\\hat{\\beta}$ proposed by Hannan (1963). This estimator is a semiparametric estimator based on nonparametric spectral estimators. We derive the second order Edgeworth expansion of the distribution of $\\hat{\\beta}$. Then it is shown that the second order asymptotic properties are independent of the bandwidth choice for residual spectral estimator, which implies that $\\hat{\\beta}$ has the same rate of convergence as in regular parametric estimation. This is a sharp contrast with the general semiparametric estimation theory. We also examine the second order Gaussian efficiency of $\\hat{\\beta}$. Numerical studies are given to confirm the theoretical results.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2006/17.html
