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Two-stage quantile regression when the first stage is based on quantile regression

โœ Scribed by Tae-Hwan Kim; Christophe Muller


Book ID
110879973
Publisher
John Wiley and Sons
Year
2004
Tongue
English
Weight
130 KB
Volume
7
Category
Article
ISSN
1368-4221

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The ordinary least-squares-based estimator of the disturbance variance is shown to be asymptotically unbiased and weakly consistent irrespective of restrictions on the nonstochastic regressor matrix, when a regression model uses the data collected by a two-stage sampling.