Geodesic
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.
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 
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     title = {Efficient robust estimation of time-series regression models},
     journal = {Applications of Mathematics},
     pages = {267--279},
     publisher = {mathdoc},
     volume = {53},
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     year = {2008},
     doi = {10.1007/s10492-008-0009-x},
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     zbl = {1189.62140},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1007/s10492-008-0009-x/}
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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. http://geodesic.mathdoc.fr/articles/10.1007/s10492-008-0009-x/

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