Efficient robust estimation of time-series regression models
Cizek,P.
Cizek,P.
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.
Description
Appeared previously as CentER DP 2007-95
Date
2008
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Publisher
Research Projects
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Citation
Cizek, P 2008, 'Efficient robust estimation of time-series regression models', Applications of Mathematics, vol. 53, no. 3, pp. 267-279.
License
info:eu-repo/semantics/restrictedAccess