Efficient robust estimation of time-series regression models
Applications of Mathematics, Tome 53 (2008) no. 3, pp. 267-279
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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.
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.
DOI :
10.1007/s10492-008-0009-x
Classification :
62F10, 62F12, 62F35, 62J05, 62L12, 62M10, 65C60
Keywords: asymptotic efficiency; least weighted squares; robust regression; time series
Keywords: asymptotic efficiency; least weighted squares; robust regression; time series
@article{10_1007_s10492_008_0009_x,
author = {\v{C}{\'\i}\v{z}ek, Pavel},
title = {Efficient robust estimation of time-series regression models},
journal = {Applications of Mathematics},
pages = {267--279},
year = {2008},
volume = {53},
number = {3},
doi = {10.1007/s10492-008-0009-x},
mrnumber = {2411129},
zbl = {1189.62140},
language = {en},
url = {http://geodesic.mathdoc.fr/articles/10.1007/s10492-008-0009-x/}
}
TY - JOUR AU - Čížek, Pavel TI - Efficient robust estimation of time-series regression models JO - Applications of Mathematics PY - 2008 SP - 267 EP - 279 VL - 53 IS - 3 UR - http://geodesic.mathdoc.fr/articles/10.1007/s10492-008-0009-x/ DO - 10.1007/s10492-008-0009-x LA - en ID - 10_1007_s10492_008_0009_x ER -
Čížek, Pavel. Efficient robust estimation of time-series regression models. Applications of Mathematics, Tome 53 (2008) no. 3, pp. 267-279. doi: 10.1007/s10492-008-0009-x
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