IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v71y2014icp743-757.html

LOL selection in high dimension

Author

Listed:
  • Mougeot, M.
  • Picard, D.
  • Tribouley, K.

Abstract

A selection procedure with no optimization step called LOLA, for Learning Out of Leaders with Adaptation is proposed. LOLA is an auto-driven algorithm with two thresholding steps. The consistency of the LOL procedure (the non adaptive version of LOLA) is proved under sparsity conditions and simulations are conducted to illustrate the practical good performances of LOLA. The behavior of the algorithm is studied when instrumental variables are artificially added without a priori significant connection to the model. Finally, the problem of empirically verifying the conditional convergence hypothesis used in economics concerning the growth rate is studied. To avoid unnecessary discussion about the choice and the pertinence of instrumental variables, the model is embedded in a very high dimensional setting. Using the LOLA algorithm, a solution for modeling the link between the growth rate and the initial level of the gross domestic product is provided and the convergence hypothesis is empirically proved.

Suggested Citation

  • Mougeot, M. & Picard, D. & Tribouley, K., 2014. "LOL selection in high dimension," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 743-757.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:743-757
    DOI: 10.1016/j.csda.2012.04.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947312001764
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2012.04.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. S. Darolles & Y. Fan & J. P. Florens & E. Renault, 2011. "Nonparametric Instrumental Regression," Econometrica, Econometric Society, vol. 79(5), pages 1541-1565, September.
    2. Mathilde Mougeot & Dominique Picard & Karine Tribouley, 2012. "Learning out of leaders," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(3), pages 475-513, June.
    3. Jean-Pierre Florens & James Heckman & Costas Meghir & Edward Vytlacil, 2002. "Instrumental variables, local instrumental variables and control functions," CeMMAP working papers CWP15/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    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. D’Haultfoeuille, Xavier, 2011. "On The Completeness Condition In Nonparametric Instrumental Problems," Econometric Theory, Cambridge University Press, vol. 27(3), pages 460-471, June.
    2. Frolich, Markus, 2007. "Nonparametric IV estimation of local average treatment effects with covariates," Journal of Econometrics, Elsevier, vol. 139(1), pages 35-75, July.
    3. Severini, Thomas A. & Tripathi, Gautam, 2006. "Some Identification Issues In Nonparametric Linear Models With Endogenous Regressors," Econometric Theory, Cambridge University Press, vol. 22(2), pages 258-278, April.
    4. Richard Blundell & Xiaohong Chen & Dennis Kristensen, 2003. "Nonparametric IV estimation of shape-invariant Engel curves," CeMMAP working papers CWP15/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. d'Haultfoeuille, Xavier, 2010. "A new instrumental method for dealing with endogenous selection," Journal of Econometrics, Elsevier, vol. 154(1), pages 1-15, January.
    6. Christoph Breunig, 2019. "Goodness-of-Fit Tests based on Series Estimators in Nonparametric Instrumental Regression," Papers 1909.10133, arXiv.org.
    7. Grégory Jolivet & Bruno Jullien & Fabien Postel-Vinay, 2014. "Reputation and Pricing on the e-Market: Evidence from a Major French Platform," Sciences Po Economics Publications (main) hal-03460312, HAL.
    8. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    9. Chiappori, Pierre-Andre & Komunjer, Ivana, 2008. "Correct Specification and Identification of Nonparametric Transformation Models," University of California at San Diego, Economics Working Paper Series qt4v12m2rg, Department of Economics, UC San Diego.
    10. Nir Billfeld & Moshe Kim, 2024. "Context-dependent Causality (the Non-Nonotonic Case)," Papers 2404.05021, arXiv.org.
    11. Michal Kolesár, 2013. "Estimation in an Instrumental Variables Model With Treatment Effect Heterogeneity," Working Papers 2013-2, Princeton University. Economics Department..
    12. Xiaohong Chen & Timothy M. Christensen, 2015. "Optimal sup-norm rates, adaptivity and inference in nonparametric instrumental variables estimation," CeMMAP working papers 32/15, Institute for Fiscal Studies.
    13. Jing Nie & Juliana Malagon & Julian Williams, 2022. "The impact of high speed quoting on execution risk dynamics: Evidence from interest rate futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1434-1465, August.
    14. Jean-Pierre Florens & James Heckman & Costas Meghir & Edward Vytlacil, 2002. "Instrumental variables, local instrumental variables and control functions," CeMMAP working papers CWP15/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2018. "Nonparametric estimation in case of endogenous selection," Journal of Econometrics, Elsevier, vol. 202(2), pages 268-285.
    16. Babii, Andrii, 2020. "Honest Confidence Sets In Nonparametric Iv Regression And Other Ill-Posed Models," Econometric Theory, Cambridge University Press, vol. 36(4), pages 658-706, August.
    17. Zongwu Cai & Huaiyu Xiong, 2013. "Effient Estimation of Partially Varying Coefficient Instrumental Variables Models," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    18. Daniel J. Henderson & Anne-Charlotte Souto & Le Wang, 2020. "Higher-Order Risk–Returns to Education," JRFM, MDPI, vol. 13(11), pages 1-25, October.
    19. Samuele Centorrino & Jean-Pierre Florens & Jean-Michel Loubes, 2022. "Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables," Papers 2202.08977, arXiv.org.
    20. Halbert White & Karim Chalak, 2013. "Identification and Identification Failure for Treatment Effects Using Structural Systems," Econometric Reviews, Taylor & Francis Journals, vol. 32(3), pages 273-317, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:csdana:v:71:y:2014:i:c:p:743-757. 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.