IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/40452.html
   My bibliography  Save this paper

Asymptotic theory for partly linear models

Author

Listed:
  • Gao, Jiti

Abstract

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.

Suggested Citation

  • Gao, Jiti, 1994. "Asymptotic theory for partly linear models," MPRA Paper 40452, University Library of Munich, Germany, revised 02 Dec 1994.
    • Handle: RePEc:pra:mprapa:40452
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/40452/1/MPRA_paper_40452.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrews, Donald W K, 1991. "Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models," Econometrica, Econometric Society, vol. 59(2), pages 307-345, March.
    2. Rice, John, 1986. "Convergence rates for partially splined models," Statistics & Probability Letters, Elsevier, vol. 4(4), pages 203-208, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Aneiros-Perez, G. & Vilar-Fernandez, J.M., 2008. "Local polynomial estimation in partial linear regression models under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2757-2777, January.
    2. You, Jinhong & Zhou, Xian, 2006. "Statistical inference in a panel data semiparametric regression model with serially correlated errors," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 844-873, April.
    3. Zhensheng Huang, 2012. "Empirical likelihood for varying-coefficient single-index model with right-censored data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(1), pages 55-71, January.
    4. 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, vol. 53(9), pages 3466-3477, July.
    5. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
    6. Huang, Tzee-Ming & Chen, Hung, 2008. "Estimating the parametric component of nonlinear partial spline model," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1665-1680, September.
    7. Xiaohui Liu & Zhizhong Wang & Xuemei Hu, 2011. "Testing heteroscedasticity in partially linear models with missing covariates," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 321-337.
    8. Q. Shao, 2009. "Seasonality analysis of time series in partial linear models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(7), pages 827-837.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Linton, Oliver, 1995. "Second Order Approximation in the Partially Linear Regression Model," Econometrica, Econometric Society, vol. 63(5), pages 1079-1112, September.
    2. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
    3. Ai, Chunrong & McFadden, Daniel, 1997. "Estimation of some partially specified nonlinear models," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 1-37.
    4. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    5. Li, Qi, 1996. "On the root-N-consistent semiparametric estimation of partially linear models," Economics Letters, Elsevier, vol. 51(3), pages 277-285, June.
    6. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339, Elsevier.
    7. Delgado, Miguel A & Robinson, Peter M, 1992. "Nonparametric and Semiparametric Methods for Economic Research," Journal of Economic Surveys, Wiley Blackwell, vol. 6(3), pages 201-249.
    8. Mustafa Koroglu & Yiguo Sun, 2016. "Functional-Coefficient Spatial Durbin Models with Nonparametric Spatial Weights: An Application to Economic Growth," Econometrics, MDPI, vol. 4(1), pages 1-16, February.
    9. Abhimanyu Gupta & Myung Hwan Seo, 2023. "Robust Inference on Infinite and Growing Dimensional Time‐Series Regression," Econometrica, Econometric Society, vol. 91(4), pages 1333-1361, July.
    10. Alberto Abadie, 2000. "Semiparametric Estimation of Instrumental Variable Models for Causal Effects," NBER Technical Working Papers 0260, National Bureau of Economic Research, Inc.
    11. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2021. "A unified framework for efficient estimation of general treatment models," Quantitative Economics, Econometric Society, vol. 12(3), pages 779-816, July.
    12. Jim Levinsohn & Wendy Petropoulos, 2001. "Creative Destruction or Just Plain Destruction?: The U.S. Textile and Apparel Industries since 1972," NBER Working Papers 8348, National Bureau of Economic Research, Inc.
    13. Lee, Jungyoon & Robinson, Peter M., 2016. "Series estimation under cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 190(1), pages 1-17.
    14. Lewbel, Arthur & Linton, Oliver, 2003. "Nonparametric estimation of homothetic and homothetically separable functions," LSE Research Online Documents on Economics 2066, London School of Economics and Political Science, LSE Library.
    15. repec:cdl:ucsdec:qt9qz123ng is not listed on IDEAS
    16. Jungyoon Lee & Peter Robinson, 2016. "Series estimation under cross-sectional dependence," LSE Research Online Documents on Economics 63380, London School of Economics and Political Science, LSE Library.
    17. Kourtellos, Andros & Stengos, Thanasis & Sun, Yiguo, 2022. "Endogeneity In Semiparametric Threshold Regression," Econometric Theory, Cambridge University Press, vol. 38(3), pages 562-595, June.
    18. Gordon B. Dahl, 2002. "Mobility and the Return to Education: Testing a Roy Model with Multiple Markets," Econometrica, Econometric Society, vol. 70(6), pages 2367-2420, November.
    19. Wang Q. & Linton O. & Hardle W., 2004. "Semiparametric Regression Analysis With Missing Response at Random," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 334-345, January.
    20. Chen, Haiqiang & Choi, Paul Moon Sub & Hong, Yongmiao, 2013. "How smooth is price discovery? Evidence from cross-listed stock trading," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 668-699.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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; Diffusion Processes

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:40452. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.