Asymptotic theory for partly linear models
This paper considers a partially linear model of the form y = x beta + g(t) + e, where beta is an unknown parameter vector, g(.) is an unknown function, and e is an error term. Based on a nonparametric estimate of g(.), the parameter beta is estimated by a semiparametric weighted least squares estimator. An asymptotic theory is established for the consistency of the estimators.
|Date of creation:||01 Jul 1994|
|Date of revision:||02 Dec 1994|
|Publication status:||Published in Communications in Statistics: Theory and Methods 8.24(1995): pp. 1985-2009|
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- Donald W.K. Andrews, 1988.
"Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models,"
Cowles Foundation Discussion Papers
874R, Cowles Foundation for Research in Economics, Yale University, revised May 1989.
- Andrews, Donald W K, 1991. "Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models," Econometrica, Econometric Society, vol. 59(2), pages 307-45, March.
- Rice, John, 1986. "Convergence rates for partially splined models," Statistics & Probability Letters, Elsevier, vol. 4(4), pages 203-208, June.
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