### Abstract

Original language | English |
---|---|

Pages (from-to) | 267-279 |

Journal | Applications of Mathematics |

Volume | 53 |

Issue number | 3 |

Publication status | Published - 2008 |

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### Cite this

*Applications of Mathematics*,

*53*(3), 267-279.

}

*Applications of Mathematics*, vol. 53, no. 3, pp. 267-279.

**Efficient robust estimation of time-series regression models.** / Cizek, P.

Research output: Contribution to journal › Article › Scientific › peer-review

TY - JOUR

T1 - Efficient robust estimation of time-series regression models

AU - Cizek, P.

N1 - Appeared previously as CentER DP 2007-95

PY - 2008

Y1 - 2008

N2 - The paper studies a new class of robust regression estimators based on the two-step least weighted squares (2S-LWS) estimator which employs data-adaptive weights determined from the empirical distribution or quantile functions of regression residuals obtained from an initial robust fit. Just like many existing two-step robust methods, the proposed 2S-LWS estimator preserves robust properties of the initial robust estimate. However, contrary to the existing methods, the first-order asymptotic behavior of 2S-LWS is fully independent of the initial estimate under mild conditions. We propose data-adaptive weighting schemes that perform well both in the cross-section and time-series data and prove the asymptotic normality and efficiency of the resulting procedure. A simulation study documents these theoretical properties in finite samples.

AB - The paper studies a new class of robust regression estimators based on the two-step least weighted squares (2S-LWS) estimator which employs data-adaptive weights determined from the empirical distribution or quantile functions of regression residuals obtained from an initial robust fit. Just like many existing two-step robust methods, the proposed 2S-LWS estimator preserves robust properties of the initial robust estimate. However, contrary to the existing methods, the first-order asymptotic behavior of 2S-LWS is fully independent of the initial estimate under mild conditions. We propose data-adaptive weighting schemes that perform well both in the cross-section and time-series data and prove the asymptotic normality and efficiency of the resulting procedure. A simulation study documents these theoretical properties in finite samples.

M3 - Article

VL - 53

SP - 267

EP - 279

JO - Applications of Mathematics

JF - Applications of Mathematics

SN - 0862-7940

IS - 3

ER -