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|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:40452. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter)
If references are entirely missing, you can add them using this form.