Efficient robust estimation of time-series regression models

@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 -