Article
MSC:
62F10,
62F12,
62F35,
62J05,
62L12,
62M10,
65C60 | MR 2411129 | Zbl 1189.62140 | DOI: 10.1007/s10492-008-0009-x
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Keywords:
asymptotic efficiency; least weighted squares; robust regression; time series
asymptotic efficiency; least weighted squares; robust regression; time series
Summary:
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.
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