# Efficient robust estimation of time-series regression models

Applications of Mathematics (2008)

- Volume: 53, Issue: 3, page 267-279
- ISSN: 0862-7940

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topČížek, Pavel. "Efficient robust estimation of time-series regression models." Applications of Mathematics 53.3 (2008): 267-279. <http://eudml.org/doc/37783>.

@article{Čížek2008,

abstract = {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.},

author = {Čížek, Pavel},

journal = {Applications of Mathematics},

keywords = {asymptotic efficiency; least weighted squares; robust regression; time series; asymptotic efficiency; least weighted squares; robust regression; time series},

language = {eng},

number = {3},

pages = {267-279},

publisher = {Institute of Mathematics, Academy of Sciences of the Czech Republic},

title = {Efficient robust estimation of time-series regression models},

url = {http://eudml.org/doc/37783},

volume = {53},

year = {2008},

}

TY - JOUR

AU - Čížek, Pavel

TI - Efficient robust estimation of time-series regression models

JO - Applications of Mathematics

PY - 2008

PB - Institute of Mathematics, Academy of Sciences of the Czech Republic

VL - 53

IS - 3

SP - 267

EP - 279

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.

LA - eng

KW - asymptotic efficiency; least weighted squares; robust regression; time series; asymptotic efficiency; least weighted squares; robust regression; time series

UR - http://eudml.org/doc/37783

ER -

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