Efficient Robust Estimation of Time-Series Regression Models
CentER Discussion Paper No. 2007-95
12 Pages Posted: 10 Dec 2007
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: Suggested Citation