IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v211y2019i1p294-307.html
   My bibliography  Save this article

On the structure of IV estimands

Author

Listed:
  • Andrews, Isaiah

Abstract

When the overidentifying restrictions of the constant-effect linear instrumental variables model fail, common IV estimators converge to different probability limits. I characterize the estimands of two stage least squares, two step GMM, and limited information maximum likelihood as functions of the single-instrument estimands from the just-identified IV regressions which consider each instrument separately. The limited information maximum likelihood estimand is found to be discontinuous on a set of dimension equal to the number of instruments minus one, and to equal the full parameter space on a set of dimension equal to the number of instruments minus two.

Suggested Citation

  • Andrews, Isaiah, 2019. "On the structure of IV estimands," Journal of Econometrics, Elsevier, vol. 211(1), pages 294-307.
    • Handle: RePEc:eee:econom:v:211:y:2019:i:1:p:294-307
    DOI: 10.1016/j.jeconom.2018.12.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407618302537
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconom.2018.12.017?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Motohiro Yogo, 2004. "Estimating the Elasticity of Intertemporal Substitution When Instruments Are Weak," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 797-810, August.
    2. Hall, Alastair R. & Inoue, Atsushi, 2003. "The large sample behaviour of the generalized method of moments estimator in misspecified models," Journal of Econometrics, Elsevier, vol. 114(2), pages 361-394, June.
    3. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2018. "Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters," Econometrica, Econometric Society, vol. 86(5), pages 1589-1619, September.
    4. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    5. James H. Stock & Jonathan Wright, 2000. "GMM with Weak Identification," Econometrica, Econometric Society, vol. 68(5), pages 1055-1096, September.
    6. Gary Chamberlain, 2007. "Decision Theory Applied to an Instrumental Variables Model," Econometrica, Econometric Society, vol. 75(3), pages 609-652, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Hwang, Jungbin & Kang, Byunghoon & Lee, Seojeong, 2022. "A doubly corrected robust variance estimator for linear GMM," Journal of Econometrics, Elsevier, vol. 229(2), pages 276-298.
    2. Isaiah Andrews & Anna Mikusheva, 2022. "GMM is Inadmissible Under Weak Identification," Papers 2204.12462, arXiv.org, revised May 2023.
    3. Frank Kleibergen & Zhaoguo Zhan, 2025. "Double robust inference for continuous updating GMM," Quantitative Economics, Econometric Society, vol. 16(1), pages 295-327, January.

    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. Timothy B. Armstrong & Michal Kolesár, 2021. "Sensitivity analysis using approximate moment condition models," Quantitative Economics, Econometric Society, vol. 12(1), pages 77-108, January.
    2. Frank Kleibergen & Zhaoguo Zhan, 2025. "Double robust inference for continuous updating GMM," Quantitative Economics, Econometric Society, vol. 16(1), pages 295-327, January.
    3. Molinari, Francesca, 2020. "Microeconometrics with partial identification," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 355-486, Elsevier.
    4. Marcelo Moreira & Geert Ridder, 2019. "Efficiency loss of asymptotically efficient tests in an instrumental variables regression," CeMMAP working papers CWP03/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Bruce E. Hansen & Seojeong Lee, 2021. "Inference for Iterated GMM Under Misspecification," Econometrica, Econometric Society, vol. 89(3), pages 1419-1447, May.
    6. Antoine, Bertille & Lavergne, Pascal, 2023. "Identification-robust nonparametric inference in a linear IV model," Journal of Econometrics, Elsevier, vol. 235(1), pages 1-24.
    7. Masakatsu Okubo, 2011. "The Intertemporal Elasticity of Substitution: An Analysis Based on Japanese Data," Economica, London School of Economics and Political Science, vol. 78(310), pages 367-390, April.
    8. Deniz Dutz & Ingrid Huitfeldt & Santiago Lacouture & Magne Mogstad & Alexander Torgovitsky & Winnie van Dijk, 2021. "Selection in Surveys," NBER Working Papers 29549, National Bureau of Economic Research, Inc.
      • Deniz Dutz & Ingrid Huitfeldt & Santiago Lacouture & Magne Mogstad & Alexander Torgovitsky & Winnie van Dijk, 2021. "Selection in Surveys," Discussion Papers 971, Statistics Norway, Research Department.
    9. Ivan A Canay & Magne Mogstad & Jack Mount, 2024. "On the Use of Outcome Tests for Detecting Bias in Decision Making," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(4), pages 2135-2167.
    10. Xin Meng & Sen Xue, 2020. "Social networks and mental health outcomes: Chinese rural–urban migrant experience," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(1), pages 155-195, January.
    11. Robert A. Moffitt & Matthew V. Zahn, 2019. "The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation," NBER Working Papers 26028, National Bureau of Economic Research, Inc.
    12. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    13. Humlum, Anders & Munch, Jakob R. & Rasmussen, Mette, 2023. "What Works for the Unemployed? Evidence from Quasi-Random Caseworker Assignments," IZA Discussion Papers 16033, Institute of Labor Economics (IZA).
    14. Markus Frölich & Michael Lechner, 2004. "Regional treatment intensity as an instrument for the evaluation of labour market policies," University of St. Gallen Department of Economics working paper series 2004 2004-08, Department of Economics, University of St. Gallen.
    15. Giorgio d’Agostino & John Paul Dunne & Luca Pieroni, 2019. "Military Expenditure, Endogeneity and Economic Growth," Defence and Peace Economics, Taylor & Francis Journals, vol. 30(5), pages 509-524, July.
    16. Yu-Chang Chen & Haitian Xie, 2022. "Personalized Subsidy Rules," Papers 2202.13545, arXiv.org, revised Mar 2022.
    17. Thomas M. Russell, 2020. "Policy Transforms and Learning Optimal Policies," Papers 2012.11046, arXiv.org.
    18. Nadja van ’t Hoff & Arthur Lewbel & Giovanni Mellace, 2023. "Limited Monotonicity and the Combined Compliers LATE," Boston College Working Papers in Economics 1059, Boston College Department of Economics, revised 20 Jan 2025.
    19. Gomes, Fábio Augusto Reis & Ribeiro, Priscila Fernandes, 2015. "Estimating the elasticity of intertemporal substitution taking into account the precautionary savings motive," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 108-123.
    20. Caner, Mehmet, 2014. "Near exogeneity and weak identification in generalized empirical likelihood estimators: Many moment asymptotics," Journal of Econometrics, Elsevier, vol. 182(2), pages 247-268.

    More about this item

    Keywords

    Instrumental variables; Misspecification;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

    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:211:y:2019:i:1:p:294-307. 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.