Efficient robust estimation of time-series regression models |
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Authors: | Pavel ?í?ek |
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Institution: | (1) Department of Econometrics & OR, Tilburg University, P.O. Box 90153, 5000LE Tilburg, The Netherlands |
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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. |
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Keywords: | asymptotic efficiency least weighted squares robust regression time series |
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