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Partially Adaptive Estimation of Regression Models via the Generalized T Distribution

Listed author(s):
  • McDonald, James B.
  • Newey, Whitney K.

This paper considers M-estimators of regression parameters that make use of a generalized functional form for the disturbance distribution. The family of distributions considered is the generalized t (GT), which includes the power exponential or Box-Tiao, normal, Laplace, and t distributions as special cases. The corresponding influence function is bounded and redescending for finite “degrees of freedom.” The regression estimators considered are those that maximize the GT quasi-likelihood, as well as one-step versions. Estimators of the parameters of the GT distribution and the effect of such estimates on the asymptotic efficiency of the regression estimates are discussed. We give a minimum-distance interpretation of the choice of GT parameter estimate that minimizes the asymptotic variance of the regression parameters.

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Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 4 (1988)
Issue (Month): 03 (December)
Pages: 428-457

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Handle: RePEc:cup:etheor:v:4:y:1988:i:03:p:428-457_01
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