Parametrically and Semiparametrically Efficient Detection of Random Regression Coefficients
Amal Mellouk and
Marc Hallin ()
No ECARES 2017-14, Working Papers ECARES from ULB -- Universite Libre de Bruxelles
Locally asymptotically optimal (in the Hajek-Le Cam sense) pseudo-Gaussian and rank-based procedures for detecting randomness of coefficients in linear regression models are proposed. The parametric and semiparametric efficiency properties of those procedures are investigated. Their asymptotic relative efficiencies (with respect to the pseudo-Gaussian procedure) turns out to be be huge under heavy-tailed and skewed densities, stressing the importance of an adequate choice of scores. Simulations demonstrate the excellent finite-sample performances of a class of rank-based procedures based on data-driven scores.
Keywords: local Asymptotic normality; optimal tests; pseudo-gaussian test; semiparametric efficiency; rank tests; random coefficient regression model (search for similar items in EconPapers)
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