Asymptotic theory for partly linear models
AbstractThis 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 40452.
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
Asymptotic normality; linear process; partly linear model; strong consistency;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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.:
- Andrews, Donald W K, 1991.
"Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models,"
Econometrica, Econometric Society,
Econometric Society, vol. 59(2), pages 307-45, March.
- Donald W.K. Andrews, 1988. "Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 874R, Cowles Foundation for Research in Economics, Yale University, revised May 1989.
- Rice, John, 1986. "Convergence rates for partially splined models," Statistics & Probability Letters, Elsevier, Elsevier, vol. 4(4), pages 203-208, June.
- Aneiros-Perez, G. & Vilar-Fernandez, J.M., 2008. "Local polynomial estimation in partial linear regression models under dependence," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(5), pages 2757-2777, January.
- Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper, University Library of Munich, Germany 39562, University Library of Munich, Germany, revised 01 Sep 2000.
- Wong, Heung & Liu, Feng & Chen, Min & Ip, Wai Cheung, 2009. "Empirical likelihood based diagnostics for heteroscedasticity in partial linear models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 53(9), pages 3466-3477, July.
- Zhensheng Huang, 2012. "Empirical likelihood for varying-coefficient single-index model with right-censored data," Metrika, Springer, Springer, vol. 75(1), pages 55-71, January.
- You, Jinhong & Zhou, Xian, 2006. "Statistical inference in a panel data semiparametric regression model with serially correlated errors," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 97(4), pages 844-873, April.
- Huang, Tzee-Ming & Chen, Hung, 2008. "Estimating the parametric component of nonlinear partial spline model," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 99(8), pages 1665-1680, September.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht).
If references are entirely missing, you can add them using this form.