Root-n consistent estimation in partly linear regression models

Anton Schick

Statistics & Probability Letters, 1996, vol. 28, issue 4, 353-358

Abstract: This paper deals with root-n consistent estimation of the parameter [beta] in the partly linear regression model Y = [beta]T U + [gamma](X) + [var epsilon], where , [gamma] is a function on [0, 1]q, the error variable [var epsilon] satisfies E([var epsilon] / U, X) = 0 and E([var epsilon]2 / U, X) is bounded, and the random vector (UT, XT)T is . Under an identifiability condition, least squares type estimates of [beta] are shown to be root-n consistent under mild smoothness assumptions on [gamma], h or both, where h(X) = E(U X). No assumption on the distribution of X are imposed. This result improves on a result of Chen (1988).

Keywords: Least; dispersed; regular; estimator; Least; squares; spline; estimator (search for similar items in EconPapers)
Date: 1996
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Citations: View citations in EconPapers (7)

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