Efficient Robust Estimation of Time-Series Regression Models

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Abstract

Abstract. This 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 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.
Original languageEnglish
Place of PublicationTilburg
PublisherEconometrics
Number of pages12
Volume2007-95
Publication statusPublished - 2007

Publication series

NameCentER Discussion Paper
Volume2007-95

Keywords

  • Asymptotic efficiency
  • least weighted squares
  • robust regression
  • time series

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  • Cite this

    Cizek, P. (2007). Efficient Robust Estimation of Time-Series Regression Models. (CentER Discussion Paper; Vol. 2007-95). Econometrics.