### Abstract

For a class of time series regression models with long-memory disturbance, we are interested in estimation of a subset of the regression coefficient vector and spectral parameter of the residual process when the complementary subset is suspected to be close to 0. In this situation, we evaluate the mean square errors of the restricted and unrestricted MLE and a preliminary test estimator when the complementary parameters are contiguous to zero vector. The results are expressed in terms of the regression spectra and the residual spectra. Since we assume long-memory dependence for the disturbance, the asymptotics are much different from the case of i.i.d. disturbance. Numerical studies elucidate some interesting features of regression and long-memory structures.

Original language | English |
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Pages (from-to) | 3213-3224 |

Number of pages | 12 |

Journal | Communications in Statistics - Theory and Methods |

Volume | 38 |

Issue number | 16-17 |

DOIs | |

Publication status | Published - 2009 Jan |

Externally published | Yes |

### Fingerprint

### Keywords

- Fractional spectral density
- LAN theorem
- Long-memory process
- Preliminary test estimator
- Restricted MLE
- Time regression model
- Unrestricted MLE

### ASJC Scopus subject areas

- Statistics and Probability

### Cite this

*Communications in Statistics - Theory and Methods*,

*38*(16-17), 3213-3224. https://doi.org/10.1080/03610920902947741

**Preliminary test estimation for regression models with long-memory disturbance.** / Taniguchi, Masanobu; Ogata, Hiroaki; Shiraishi, Hiroshi.

Research output: Contribution to journal › Article

*Communications in Statistics - Theory and Methods*, vol. 38, no. 16-17, pp. 3213-3224. https://doi.org/10.1080/03610920902947741

}

TY - JOUR

T1 - Preliminary test estimation for regression models with long-memory disturbance

AU - Taniguchi, Masanobu

AU - Ogata, Hiroaki

AU - Shiraishi, Hiroshi

PY - 2009/1

Y1 - 2009/1

N2 - For a class of time series regression models with long-memory disturbance, we are interested in estimation of a subset of the regression coefficient vector and spectral parameter of the residual process when the complementary subset is suspected to be close to 0. In this situation, we evaluate the mean square errors of the restricted and unrestricted MLE and a preliminary test estimator when the complementary parameters are contiguous to zero vector. The results are expressed in terms of the regression spectra and the residual spectra. Since we assume long-memory dependence for the disturbance, the asymptotics are much different from the case of i.i.d. disturbance. Numerical studies elucidate some interesting features of regression and long-memory structures.

AB - For a class of time series regression models with long-memory disturbance, we are interested in estimation of a subset of the regression coefficient vector and spectral parameter of the residual process when the complementary subset is suspected to be close to 0. In this situation, we evaluate the mean square errors of the restricted and unrestricted MLE and a preliminary test estimator when the complementary parameters are contiguous to zero vector. The results are expressed in terms of the regression spectra and the residual spectra. Since we assume long-memory dependence for the disturbance, the asymptotics are much different from the case of i.i.d. disturbance. Numerical studies elucidate some interesting features of regression and long-memory structures.

KW - Fractional spectral density

KW - LAN theorem

KW - Long-memory process

KW - Preliminary test estimator

KW - Restricted MLE

KW - Time regression model

KW - Unrestricted MLE

UR - http://www.scopus.com/inward/record.url?scp=70249143971&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70249143971&partnerID=8YFLogxK

U2 - 10.1080/03610920902947741

DO - 10.1080/03610920902947741

M3 - Article

AN - SCOPUS:70249143971

VL - 38

SP - 3213

EP - 3224

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

IS - 16-17

ER -