IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v55y2011i2p1137-1149.html
   My bibliography  Save this article

On nonlinear regression estimator with denoised variables

Author

Listed:
  • Cui, Hengjian
  • Hu, Tao

Abstract

In this paper, a class of denoised nonlinear regression estimators is suggested for a nonlinear measurement error model where the variables in error are observed together with an auxiliary variable. The programming involved in this denoised nonlinear regression estimation is relatively simple and it can be modified with a little effort from the existing programs for nonlinear regression estimation. We establish the consistency and asymptotic normality of such denoised estimators based on the least squares and M-methods. A simulation study is carried out to illustrate the performance of these estimates. An empirical application of the model to production models in economics further demonstrates the potential of the proposed modeling procedures.

Suggested Citation

  • Cui, Hengjian & Hu, Tao, 2011. "On nonlinear regression estimator with denoised variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1137-1149, February.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:2:p:1137-1149
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00354-3
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jinhong You & Haibo Zhou, 2007. "On Semiparametric EV Models with Serially Correlated Errors in Both Regression Models and Mismeasured Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(2), pages 365-383, June.
    2. Susanne M. Schennach, 2004. "Estimation of Nonlinear Models with Measurement Error," Econometrica, Econometric Society, vol. 72(1), pages 33-75, January.
    3. Gallant, A. Ronald, 1975. "Seemingly unrelated nonlinear regressions," Journal of Econometrics, Elsevier, vol. 3(1), pages 35-50, February.
    4. Hengjian Cui & Efang Kong, 2006. "Empirical Likelihood Confidence Region for Parameters in Semi‐linear Errors‐in‐Variables Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(1), pages 153-168, March.
    5. Pollard, David & Radchenko, Peter, 2006. "Nonlinear least-squares estimation," Journal of Multivariate Analysis, Elsevier, vol. 97(2), pages 548-562, February.
    6. You, Jinhong & Zhou, Xian & Zhu, Li-Xing, 2009. "Inference on a regression model with noised variables and serially correlated errors," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1182-1197, July.
    Full references (including those not matched with items on IDEAS)

    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. Amjad D. Al-Nasser, 2014. "Two steps generalized maximum entropy estimation procedure for fitting linear regression when both covariates are subject to error," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1708-1720, August.
    2. Orazio Attanasio & Sarah Cattan & Emla Fitzsimons & Costas Meghir & Marta Rubio-Codina, 2020. "Estimating the Production Function for Human Capital: Results from a Randomized Controlled Trial in Colombia," American Economic Review, American Economic Association, vol. 110(1), pages 48-85, January.
    3. Aslanidis, Nektarios, 2007. "Business Cycle Regimes in CEECs Production: A Threshold SURE Approach," Working Papers 2072/5318, Universitat Rovira i Virgili, Department of Economics.
    4. Huijun Guo & Youming Liu, 2019. "Regression estimation under strong mixing data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 553-576, June.
    5. Mittag, Nikolas, 2016. "Correcting for Misreporting of Government Benefits," IZA Discussion Papers 10266, Institute of Labor Economics (IZA).
    6. Gary King, 1989. "A Seemingly Unrelated Poisson Regression Model," Sociological Methods & Research, , vol. 17(3), pages 235-255, February.
    7. Perry Warjiyo & Wallace E. Huffman, 1997. "Dynamic input demand functions and resource adjustment for US agriculture: state evidence," Agricultural Economics, International Association of Agricultural Economists, vol. 17(2-3), pages 223-237, December.
    8. Delaigle, Aurore & Fan, Jianqing & Carroll, Raymond J., 2009. "A Design-Adaptive Local Polynomial Estimator for the Errors-in-Variables Problem," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 348-359.
    9. Stoker, Thomas M. & Berndt, Ernst R. & Denny Ellerman, A. & Schennach, Susanne M., 2005. "Panel data analysis of U.S. coal productivity," Journal of Econometrics, Elsevier, vol. 127(2), pages 131-164, August.
    10. Xia Chen & Liyue Mao, 2020. "Penalized empirical likelihood for partially linear errors-in-variables models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 597-623, December.
    11. D’Haultfoeuille, Xavier, 2011. "On The Completeness Condition In Nonparametric Instrumental Problems," Econometric Theory, Cambridge University Press, vol. 27(3), pages 460-471, June.
    12. Battistin, Erich & Chesher, Andrew, 2014. "Treatment effect estimation with covariate measurement error," Journal of Econometrics, Elsevier, vol. 178(2), pages 707-715.
    13. Christoph T. Weiss, 2012. "Persistent Attitudes and Behaviors," "Marco Fanno" Working Papers 0143, Dipartimento di Scienze Economiche "Marco Fanno".
    14. Susanne M. Schennach & Yingyao Hu & Arthur Lewbel, 2007. "Nonparametric identification of the classical errors-in-variables model without side information," Boston College Working Papers in Economics 674, Boston College Department of Economics.
    15. David Besanko & Sachin Gupta & Dipak Jain, 1998. "Logit Demand Estimation Under Competitive Pricing Behavior: An Equilibrium Framework," Management Science, INFORMS, vol. 44(11-Part-1), pages 1533-1547, November.
    16. Attanasio, Orazio & Cattan, Sarah & Fitzsimons, Emla & Meghir, Costas & Rubio-Codina, Marta, 2015. "Estimating the Production Function for Human Capital: Results from a Randomized Control Trial in Colombia," IZA Discussion Papers 8856, Institute of Labor Economics (IZA).
    17. Ben-Moshe, Dan, 2018. "Identification Of Joint Distributions In Dependent Factor Models," Econometric Theory, Cambridge University Press, vol. 34(1), pages 134-165, February.
    18. Francis J. DiTraglia & Camilo García-Jimeno, 2017. "Mis-classified, Binary, Endogenous Regressors: Identification and Inference," NBER Working Papers 23814, National Bureau of Economic Research, Inc.
    19. Yuquan Chen & Xiaohua Yu, 2022. "Estimating market power for the Chinese fluid milk market with imported products," Agribusiness, John Wiley & Sons, Ltd., vol. 38(2), pages 386-401, April.
    20. Marcela Munizaga & Sergio Jara-Díaz & Paulina Greeven & Chandra Bhat, 2008. "Econometric Calibration of the Joint Time Assignment--Mode Choice Model," Transportation Science, INFORMS, vol. 42(2), pages 208-219, May.

    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:eee:csdana:v:55:y:2011:i:2:p:1137-1149. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.