IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v26y2001i1p307-324.html
   My bibliography  Save this article

Integrated Conditional Moment testing of quantile regression models

Author

Listed:
  • Herman J. Bierens

    (Department of Economics, Pennsylvania State University, 608 Kern Graduate Building, University Park, PA16802, and Tilburg University, The Netherlands Research Department, Federal Reserve Bank of Atlanta, 104 Marietta Street, NW, Atlanta, GA 30303, USA)

  • Donna K. Ginther

    (Department of Economics, Pennsylvania State University, 608 Kern Graduate Building, University Park, PA16802, and Tilburg University, The Netherlands Research Department, Federal Reserve Bank of Atlanta, 104 Marietta Street, NW, Atlanta, GA 30303, USA)

Abstract

In this paper we propose a consistent test of the linearity of quantile regression models, similar to the Integrated Conditional Moment (ICM) test of Bierens (1982) and Bierens and Ploberger (1997). This test requires re-estimation of the quantile regression model by minimizing the ICM test statistic with respect to the parameters. We apply this ICM test to examine the correctness of the functional form of three median regression wage equations.

Suggested Citation

  • Herman J. Bierens & Donna K. Ginther, 2001. "Integrated Conditional Moment testing of quantile regression models," Empirical Economics, Springer, vol. 26(1), pages 307-324.
    • Handle: RePEc:spr:empeco:v:26:y:2001:i:1:p:307-324
    as

    Download full text from publisher

    File URL: http://link.springer.de/link/service/journals/00181/papers/1026001/10260307.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. Giacomini, Raffaella & Komunjer, Ivana, 2002. "Evaluation and Combination of Conditional Quantile Forecasts," University of California at San Diego, Economics Working Paper Series qt4n99t4wz, Department of Economics, UC San Diego.
    2. Fertő, Imre & Bakucs, Lajos Zoltán, 2008. "Érvényes-e a Gibrat-törvény a magyar mezőgazdaságban? [Is Gibrat s law valid for Hungarian agriculture?]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(1), pages 25-38.
    3. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    4. Ana Pérez-González & Tomás R. Cotos-Yáñez & Wenceslao González-Manteiga & Rosa M. Crujeiras-Casais, 2021. "Goodness-of-fit tests for quantile regression with missing responses," Statistical Papers, Springer, vol. 62(3), pages 1231-1264, June.
    5. Lawrence Dacuycuy, 2006. "Explaining male wage inequality in the Philippines: non-parametric and semiparametric approaches," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2497-2511.
    6. Kostov, Philip & Patton, Myles & Moss, Joan E. & McErlean, Seamus, 2005. "Does Gibrat's Law Hold Amongst Dairy Farmers in Northern Ireland?," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24775, European Association of Agricultural Economists.
    7. Latruffe, Laure & Desjeux, Yann & Fogarasi, Jozsef & Bakucs, Lajos Zoltan & Ferto, Imre, 2010. "Technical efficiency and environmental pressures of pig farms in Hungary," 120th Seminar, September 2-4, 2010, Chania, Crete 109385, European Association of Agricultural Economists.
    8. Horowitz, Joel L. & Spokoiny, Vladimir G., 2000. "An Adaptive, Rate-Optimal Test of Linearity for Median Regression Models," Working Papers 00-04, University of Iowa, Department of Economics.
    9. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.
    10. Escanciano, Juan Carlos & Velasco, Carlos, 2010. "Specification tests of parametric dynamic conditional quantiles," Journal of Econometrics, Elsevier, vol. 159(1), pages 209-221, November.
    11. Komunjer, Ivana & Vuong, Quang, 2010. "Efficient estimation in dynamic conditional quantile models," Journal of Econometrics, Elsevier, vol. 157(2), pages 272-285, August.
    12. Giacomini, Raffaella & Komunjer, Ivana, 2005. "Evaluation and Combination of Conditional Quantile Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 416-431, October.
    13. repec:hal:journl:peer-00732534 is not listed on IDEAS
    14. Jhon James Mora, 2003. "Sheepskin effects and screening in Colombia," Colombian Economic Journal, Universidad Nacional de Colombia, FCE, CID, April.
      • Jhon James Mora, 2003. "Sheepskin effects and screening in Colombia," Colombian Economic Journal, Academia Colombiana de Ciencias Economicas, Colegio Mayor de Nuestra Senora del Rosario, Pontificia Universidad Javeriana, Universidad de Antioquia, Universidad de los Andes, Universidad del Valle, Universidad Externado de Colombia, Universidad Nacional de Colombia, vol. 1(1), pages 95-108, December.
    15. Sungwon Lee & Joon H. Ro, 2020. "Nonparametric Tests for Conditional Quantile Independence with Duration Outcomes," Working Papers 2013, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    16. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.
    17. Lajos Zoltán Bakucs & Imre Fertő, 2009. "The growth of family farms in Hungary," Agricultural Economics, International Association of Agricultural Economists, vol. 40(s1), pages 789-795, November.
    18. Sun, Yiguo, 2006. "A Consistent Nonparametric Equality Test Of Conditional Quantile Functions," Econometric Theory, Cambridge University Press, vol. 22(4), pages 614-632, August.
    19. Escanciano, Juan Carlos & Velasco, Carlos, 2010. "Specification tests of parametric dynamic conditional quantiles," Journal of Econometrics, Elsevier, vol. 159(1), pages 209-221, November.
    20. Peter Horvath & Jia Li & Zhipeng Liao & Andrew J. Patton, 2022. "A consistent specification test for dynamic quantile models," Quantitative Economics, Econometric Society, vol. 13(1), pages 125-151, January.
    21. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    22. Juan Carlos Escanciano & Chuan Goh, 2010. "Specification Analysis of Structural Quantile Regression Models," Working Papers tecipa-415, University of Toronto, Department of Economics.
    23. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    24. Meng-Shiuh Chang & Teng-Yuan Hu & Ching-Yuan Lin, 2016. "Variation in Engel's law across quantiles in Taiwan: toward an alternative concept of near poverty line," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 21(1), pages 103-115, January.

    More about this item

    Keywords

    Quantile regression; Test for linearity; Integrated conditional moment test; Wage equations;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

    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:spr:empeco:v:26:y:2001:i:1:p:307-324. 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.