IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v105y2009i2p181-182.html
   My bibliography  Save this article

The incidental parameter problem in a non-differentiable panel data model

Author

Listed:
  • Graham, Bryan S.
  • Hahn, Jinyong
  • Powell, James L.

Abstract

We consider a panel quantile model with fixed effects. It is shown that the maximum likelihood estimator is numerically equivalent to the least absolute deviations estimator of the differenced model, and as a consequence, there is no incidental parameter problem.

Suggested Citation

  • Graham, Bryan S. & Hahn, Jinyong & Powell, James L., 2009. "The incidental parameter problem in a non-differentiable panel data model," Economics Letters, Elsevier, vol. 105(2), pages 181-182, November.
  • Handle: RePEc:eee:ecolet:v:105:y:2009:i:2:p:181-182
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165-1765(09)00244-4
    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.

    Citations

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


    Cited by:

    1. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    2. Hernández Martínez, Pedro Jesús, 2016. "Reassessing the link between firm size and exports," Economics Discussion Papers 2016-25, Kiel Institute for the World Economy (IfW).
    3. Rosen, Adam M., 2012. "Set identification via quantile restrictions in short panels," Journal of Econometrics, Elsevier, vol. 166(1), pages 127-137.
    4. Chen, Liang & Gonzalo, Jesús & Dolado, Juan José, 2017. "Quantile Factor Models," UC3M Working papers. Economics 25299, Universidad Carlos III de Madrid. Departamento de Economía.
    5. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
    6. Pedro J. Hernández, 2020. "Reassessing the link between firm size and exports," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 10(2), pages 207-223, June.
    7. Matthew Harding & Carlos Lamarche, 2017. "Penalized Quantile Regression with Semiparametric Correlated Effects: An Application with Heterogeneous Preferences," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 342-358, March.
    8. Castagnetti, Carolina & Giorgetti, Maria Letizia, 2019. "Understanding the gender wage-gap differential between the public and private sectors in Italy: A quantile approach," Economic Modelling, Elsevier, vol. 78(C), pages 240-261.
    9. Victor Chernozhukov & Ivan Fernandez-Val & Martin Weidner, 2018. "Network and panel quantile effects via distribution regression," CeMMAP working papers CWP21/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. David Powell, 2020. "Does Labor Supply Respond to Transitory Income? Evidence from the Economic Stimulus Payments of 2008," Journal of Labor Economics, University of Chicago Press, vol. 38(1), pages 1-38.
    11. repec:ran:wpaper:710-1 is not listed on IDEAS
    12. Carolina Castagnetti & Luisa Rosti & Marina Töpfer, 2019. "The Public-Private Sector Wage Differential Across Gender in Italy: a New Quantile-Based Decomposition Approach," Economics Bulletin, AccessEcon, vol. 39(4), pages 2533-2539.
    13. Carolina Castagnetti, 2015. "The Analysis of the Gender Wage Gap in the Italian Public Sector: a Quantile Approach for Panel Data," DEM Working Papers Series 109, University of Pavia, Department of Economics and Management.
    14. Tansel, Aysit & Keskin, Halil Ibrahim & Ozdemir, Zeynel Abidin, 2020. "Public-private sector wage gap by gender in Egypt: Evidence from quantile regression on panel data, 1998–2018," World Development, Elsevier, vol. 135(C).
    15. Harding, Matthew & Lamarche, Carlos, 2014. "Estimating and testing a quantile regression model with interactive effects," Journal of Econometrics, Elsevier, vol. 178(P1), pages 101-113.
    16. Sherrilyn Billger & Carlos Lamarche, 2015. "A panel data quantile regression analysis of the immigrant earnings distribution in the United Kingdom and United States," Empirical Economics, Springer, vol. 49(2), pages 705-750, September.
    17. Li, Tong & Oka, Tatsushi, 2015. "Set identification of the censored quantile regression model for short panels with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 363-377.

    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:ecolet:v:105:y:2009:i:2:p:181-182. 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: (Haili He). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.