IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v27y2002i1p131-148.html
   My bibliography  Save this article

Nonlinearity in dynamic adjustment: Semiparametric estimation of panel labor supply

Author

Listed:
  • Qi Li

    (Department of Economics, Texas A&M University, College Station, TX 77843, U.S.A.)

  • Thomas J. Kniesner

    (Center for Policy Research, 426 Eggers Hall, Syracuse University, Syracuse, NY 13244-1020, U.S.A.)

Abstract

We estimate a semiparametric dynamic panel data model by the local linear kernel method and we interpret the slope of the nonparametric component function as a varying slope coefficient. Thus, the slope coefficient is a smooth, but otherwise unknown, function of some of the regressors. A Monte Carlo experiment is reported to examine the finite sample performance of the local linear estimator. We apply the estimation method to a labor supply equation for men from the triannual Survey of Income and Program Participation (SIPP). Specification tests based on the estimated labor supply elasticities, partial adjustment coefficients, and residuals demonstrate the improvements from a semiparametric partially linear model. Our empirical results point to a need by economists to revisit the issue of the speed of labor market adjustment to policy induced shifts in labor demand and to take more formal econometric account of heterogeneity in wage effects when studying the distributional consequences of tax reforms for labor supply earnings.

Suggested Citation

  • Qi Li & Thomas J. Kniesner, 2002. "Nonlinearity in dynamic adjustment: Semiparametric estimation of panel labor supply," Empirical Economics, Springer, vol. 27(1), pages 131-148.
    • Handle: RePEc:spr:empeco:v:27:y:2002:i:1:p:131-148
    Note: received: July 2000/Final version received: January 2001
    as

    Download full text from publisher

    File URL: http://link.springer.de/link/service/journals/00181/papers/2027001/20270131.pdf
    Download Restriction: Access to the full text of the articles in this series is restricted
    ---><---

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

    Citations

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


    Cited by:

    1. Li, Kui-Wai & Zhou, Xianbo, 2010. "Openness, domestic performance and growth," Economics Letters, Elsevier, vol. 107(1), pages 13-16, April.
    2. Roy, Nilanjana & Cornelis van Kooten, G., 2004. "Another look at the income elasticity of non-point source air pollutants: a semiparametric approach," Economics Letters, Elsevier, vol. 85(1), pages 17-22, October.
    3. Anil Kumar, 2012. "Nonparametric estimation of the impact of taxes on female labor supply," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 415-439, April.
    4. Francisca Antman & David McKenzie, 2007. "Poverty traps and nonlinear income dynamics with measurement error and individual heterogeneity," Journal of Development Studies, Taylor & Francis Journals, vol. 43(6), pages 1057-1083.
    5. Cai, Zongwu & Li, Qi, 2008. "Nonparametric Estimation Of Varying Coefficient Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 24(5), pages 1321-1342, October.
    6. Cai, Zongwu & Das, Mitali & Xiong, Huaiyu & Wu, Xizhi, 2006. "Functional coefficient instrumental variables models," Journal of Econometrics, Elsevier, vol. 133(1), pages 207-241, July.
    7. Ozkan Eren & Daniel J. Henderson, 2008. "The impact of homework on student achievement," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 326-348, July.
    8. Daniel J. Henderson & Alexandre Olbrecht & Solomon W. Polachek, 2006. "Do Former College Athletes Earn More at Work?: A Nonparametric Assessment," Journal of Human Resources, University of Wisconsin Press, vol. 41(3).
    9. Elizabeth Schroeder, 2016. "Dynamic labor supply adjustment with bias correction," Empirical Economics, Springer, vol. 51(4), pages 1623-1640, December.
    10. Tran, Kien C. & Tsionas, Efthymios G., 2009. "Estimation of nonparametric inefficiency effects stochastic frontier models with an application to British manufacturing," Economic Modelling, Elsevier, vol. 26(5), pages 904-909, September.
    11. Kien Tran & Efthymios Tsionas, 2010. "Local GMM Estimation of Semiparametric Panel Data with Smooth Coefficient Models," Econometric Reviews, Taylor & Francis Journals, vol. 29(1), pages 39-61.
    12. Henderson, Daniel J. & Ullah, Aman, 2005. "A nonparametric random effects estimator," Economics Letters, Elsevier, vol. 88(3), pages 403-407, September.
    13. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    14. Kusum Mundra, 2005. "Nonparametric Slope Estimators for Fixed-Effect Panel Data," Econometrics 0502008, University Library of Munich, Germany.
    15. Polemis, Michael L. & Tsionas, Mike G., 2016. "An alternative semiparametric approach to the modelling of asymmetric gasoline price adjustment," Energy Economics, Elsevier, vol. 56(C), pages 384-388.
    16. Qi Gao & Long Liu & Jeffrey S. Racine, 2015. "A Partially Linear Kernel Estimator for Categorical Data," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 959-978, December.
    17. Zhang, Yuanqing & Sun, Yanqing, 2015. "Estimation of partially specified dynamic spatial panel data models with fixed-effects," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 37-46.
    18. Kumar, Anil, 2008. "Labor supply, deadweight loss and tax reform act of 1986: A nonparametric evaluation using panel data," Journal of Public Economics, Elsevier, vol. 92(1-2), pages 236-253, February.

    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:spr:empeco:v:27:y:2002:i:1:p:131-148. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.