Efficient Robust Estimation of Time-Series Regression Models

CentER Discussion Paper No. 2007-95

12 Pages Posted: 10 Dec 2007

See all articles by Pavel Cizek

Pavel Cizek

Tilburg University - Department of Econometrics & Operations Research

Date Written: October 2007

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.

Keywords: Asymptotic efficiency, least weighted squares, robust regression, time series

JEL Classification: C13, C21, C22

Suggested Citation

Cizek, Pavel, Efficient Robust Estimation of Time-Series Regression Models (October 2007). CentER Discussion Paper No. 2007-95, Available at SSRN: https://ssrn.com/abstract=1068624 or http://dx.doi.org/10.2139/ssrn.1068624

Pavel Cizek (Contact Author)

Tilburg University - Department of Econometrics & Operations Research ( email )

Tilburg, 5000 LE
Netherlands

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