IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v134y2006i2p471-506.html
   My bibliography  Save this article

Quantile regression methods for recursive structural equation models

Author

Listed:
  • Ma, Lingjie
  • Koenker, Roger

Abstract

Two classes of quantile regression estimation methods for the recursive structural equation models of Chesher (2003) are investigated. A class of weighted average derivative estimators based directly on the identification strategy of Chesher is contrasted with a new control variate estimation method. The latter imposes stronger restrictions achieving an asymptotic efficiency bound with respect to the former class. An application of the methods to the study of the effect of class size on the performance of Dutch primary school students shows that (i.) reductions in class size are beneficial for good students in language and for weaker students in mathematics, (ii) larger classes appear bene cial for weaker language students, and (iii.) the impact of class size on both mean and median performance is negligible.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Ma, Lingjie & Koenker, Roger, 2006. "Quantile regression methods for recursive structural equation models," Journal of Econometrics, Elsevier, vol. 134(2), pages 471-506, October.
  • Handle: RePEc:eee:econom:v:134:y:2006:i:2:p:471-506
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304-4076(05)00153-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Amemiya, Takeshi, 1982. "Two Stage Least Absolute Deviations Estimators," Econometrica, Econometric Society, vol. 50(3), pages 689-711, May.
    2. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    3. Alan B. Krueger, 1999. "Experimental Estimates of Education Production Functions," The Quarterly Journal of Economics, Oxford University Press, vol. 114(2), pages 497-532.
    4. Newey, Whitney K. & Powell, James L., 1990. "Efficient Estimation of Linear and Type I Censored Regression Models Under Conditional Quantile Restrictions," Econometric Theory, Cambridge University Press, vol. 6(3), pages 295-317, September.
    5. Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
    6. Guido W. Imbens & Whitney K. Newey, 2009. "Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity," Econometrica, Econometric Society, vol. 77(5), pages 1481-1512, September.
    7. Andrew Chesher, 2001. "Quantile driven identification of structural derivatives," CeMMAP working papers 08/01, Institute for Fiscal Studies.
    8. Alberto Abadie & Joshua Angrist & Guido Imbens, 2002. "Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings," Econometrica, Econometric Society, vol. 70(1), pages 91-117, January.
    9. Zhao, Quanshui, 2001. "Asymptotically Efficient Median Regression In The Presence Of Heteroskedasticity Of Unknown Form," Econometric Theory, Cambridge University Press, vol. 17(4), pages 765-784, August.
    10. repec:fth:prinin:379 is not listed on IDEAS
    11. Eric A. Hanushek, "undated". "The Evidence on Class Size," Wallis Working Papers WP10, University of Rochester - Wallis Institute of Political Economy.
    12. Caroline M. Hoxby, 2000. "The Effects of Class Size on Student Achievement: New Evidence from Population Variation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(4), pages 1239-1285.
    13. Sakata, S., 1998. "Instrumental Variable Estimation Based on Mean Absolute Deviation," Papers 98-08, Michigan - Center for Research on Economic & Social Theory.
    14. Jesse Levin, 2001. "For whom the reductions count: A quantile regression analysis of class size and peer effects on scholastic achievement," Empirical Economics, Springer, vol. 26(1), pages 221-246.
    15. Hanushek, Eric A., 2006. "School Resources," Handbook of the Economics of Education, in: Erik Hanushek & F. Welch (ed.), Handbook of the Economics of Education, edition 1, volume 2, chapter 14, pages 865-908, Elsevier.
    16. Edward P. Lazear, 2001. "Educational Production," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(3), pages 777-803.
    17. Hanushek, Eric A, 1986. "The Economics of Schooling: Production and Efficiency in Public Schools," Journal of Economic Literature, American Economic Association, vol. 24(3), pages 1141-1177, September.
    18. Alan B. Krueger, 2003. "Economic Considerations and Class Size," Economic Journal, Royal Economic Society, vol. 113(485), pages 34-63, February.
    19. Andrew Chesher, 2001. "Exogenous impact and conditional quantile functions," CeMMAP working papers 01/01, Institute for Fiscal Studies.
    20. Andrew Chesher, 2003. "Identification in Nonseparable Models," Econometrica, Econometric Society, vol. 71(5), pages 1405-1441, September.
    21. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    22. Richard Blundell & James L. Powell, 2001. "Endogeneity in nonparametric and semiparametric regression models," CeMMAP working papers 09/01, Institute for Fiscal Studies.
    23. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    24. Powell, James L, 1983. "The Asymptotic Normality of Two-Stage Least Absolute Deviations Estimators," Econometrica, Econometric Society, vol. 51(5), pages 1569-1575, September.
    25. Maria Iacovou, 2002. "Class Size in the Early Years: Is Smaller Really Better?," Education Economics, Taylor & Francis Journals, vol. 10(3), pages 261-290.
    26. Simone Dobbelsteen & Jesse Levin & Hessel Oosterbeek, 2002. "The causal effect of class size on scholastic achievement: distinguishing the pure class size effect from the effect of changes in class composition," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(1), pages 17-38, February.
    27. Joshua Angrist & Alan Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Working Papers 834, Princeton University, Department of Economics, Industrial Relations Section..
    28. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-1160, September.
    29. Summers, Anita A & Wolfe, Barbara L, 1977. "Do Schools Make a Difference?," American Economic Review, American Economic Association, vol. 67(4), pages 639-652, September.
    30. repec:cup:etheor:v:6:y:1990:i:3:p:295-317 is not listed on IDEAS
    31. Koenker, Roger & Park, Beum J., 1996. "An interior point algorithm for nonlinear quantile regression," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 265-283.
    32. Akerhielm, Karen, 1995. "Does class size matter?," Economics of Education Review, Elsevier, vol. 14(3), pages 229-241, September.
    33. Joshua D. Angrist & Victor Lavy, 1999. "Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(2), pages 533-575.
    34. Alan Krueger, 1997. "Experimental Estimates of Education Production Functions," Working Papers 758, Princeton University, Department of Economics, Industrial Relations Section..
    35. Hanushek, E.A.omson, W., 1996. "Assessing the Effects of School Resources on Student Performance : An Update," RCER Working Papers 424, University of Rochester - Center for Economic Research (RCER).
    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. Lingjie Ma & Roger Koenker, 2004. "Quantile regression methods for recursive structural equation models," CeMMAP working papers 01/04, Institute for Fiscal Studies.
    2. Ludger Wößmann, 2003. "European education production functions: what makes a difference for student achievement in Europe?," European Economy - Economic Papers 2008 - 2015 190, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    3. Margaret Stevens & Kathryn Graddy, 2003. "The Impact of School Inputs on Student Performance: An Empirical Study of Private Schools in the United Kingdom," Economics Series Working Papers 146, University of Oxford, Department of Economics.
    4. Stevens, Margaret & Graddy, Kathryn, 2003. "The Impact of School Inputs on Student Performance: An Empirical Study of Private Schools in the UK," CEPR Discussion Papers 3776, C.E.P.R. Discussion Papers.
    5. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    6. Corak, Miles & Lauzon, Darren, 2009. "Differences in the distribution of high school achievement: The role of class-size and time-in-term," Economics of Education Review, Elsevier, vol. 28(2), pages 189-198, April.
    7. Corak, Miles & Lauzon, Darren, 2009. "Differences in the distribution of high school achievement: The role of class-size and time-in-term," Economics of Education Review, Elsevier, vol. 28(2), pages 189-198, April.
    8. Dinand Webbink, 2005. "Causal Effects in Education," Journal of Economic Surveys, Wiley Blackwell, vol. 19(4), pages 535-560, September.
    9. Denny, Kevin & Oppedisano, Veruska, 2013. "The surprising effect of larger class sizes: Evidence using two identification strategies," Labour Economics, Elsevier, vol. 23(C), pages 57-65.
    10. Alan B. Krueger, 2003. "Economic Considerations and Class Size," Economic Journal, Royal Economic Society, vol. 113(485), pages 34-63, February.
    11. Lauzon, Darren & Corak, Miles, 2005. "Differences entre les distributions du rendement scolaire au secondaire : le role de la taille de la classe et du temps d'enseignement," Direction des études analytiques : documents de recherche 2005270f, Statistics Canada, Direction des études analytiques.
    12. Wo[ss]mann, Ludger & West, Martin, 2006. "Class-size effects in school systems around the world: Evidence from between-grade variation in TIMSS," European Economic Review, Elsevier, vol. 50(3), pages 695-736, April.
    13. Hægeland, Torbjørn & Raaum, Oddbjørn & Salvanes, Kjell G., 2012. "Pennies from heaven? Using exogenous tax variation to identify effects of school resources on pupil achievement," Economics of Education Review, Elsevier, vol. 31(5), pages 601-614.
    14. West, Martin R. & Woessmann, Ludger, 2006. "Which school systems sort weaker students into smaller classes? International evidence," European Journal of Political Economy, Elsevier, vol. 22(4), pages 944-968, December.
    15. Chesher, Andrew, 2007. "Instrumental values," Journal of Econometrics, Elsevier, vol. 139(1), pages 15-34, July.
    16. Rosalind Levacic & Stephen Machin & David Reynolds & Anna Vignoles & James Walker, 2000. "The Relationship between Resource Allocation and Pupil Attainment: A Review," CEE Discussion Papers 0002, Centre for the Economics of Education, LSE.
    17. Cohen-Zada, Danny & Gradstein, Mark & Reuven, Ehud, 2013. "Allocation of students in public schools: Theory and new evidence," Economics of Education Review, Elsevier, vol. 34(C), pages 96-106.
    18. Wößmann, Ludger, 2001. "New Evidence on the Missing Resource-Performance Link in Education," Kiel Working Papers 1051, Kiel Institute for the World Economy (IfW Kiel).
    19. Fiona Steele & Anna Vignoles & Andrew Jenkins, 2007. "The effect of school resources on pupil attainment: a multilevel simultaneous equation modelling approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 801-824, July.
    20. Maximilian Bach & Stephan Sievert, 2019. "Birth Cohort Size Variation and the Estimation of Class Size Effects," Discussion Papers of DIW Berlin 1817, DIW Berlin, German Institute for Economic Research.

    More about this item

    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:eee:econom:v:134:y:2006:i:2:p:471-506. 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/jeconom .

    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.