IDEAS home Printed from https://ideas.repec.org/p/hhs/osloec/2017_013.html
   My bibliography  Save this paper

Identification, Instruments, Omitted Variables, and Rudimentary Models: Fallacies in the ‘Experimental Approach’ to Econometrics

Author

Listed:

Abstract

Since identification, instrumental variables and variables exclusion, core concepts in econometrics, are entwined, several questions arise: How is identification related to the existence of IVs? How are identification criteria related to omitted variables? Is omission/inclusion of variables from a model’s equations part of the definition of IVs? Is exogeneity a critical claim to an IV? Is ‘omitted variables’ a meaningful term for a single equation when its ‘environment’ is incompletely described? Which are the borderlines between omitted, observable variables, omitted non-modeled variables, latent variables represented by proxies or measurement error mechanisms? These are among the questions addressed in this paper, partly with reference to the conflict between ‘experimentalists’ and ‘structuralists’, specifically relating to: (i) the contrast between ‘rudimentary models’ and models for ‘limited information inference’, (ii) the distinction between exogeneity of variables and the orthogonality claim for IVs and disturbances or errors, (iii) the role of predetermined variables in selecting IVs, and (iv) the ‘omitted variables’ concept and the role of IVs in ‘handling’ such variables, when considering the ‘origin’ of the omission.

Suggested Citation

  • Biørn, Erik, 2017. "Identification, Instruments, Omitted Variables, and Rudimentary Models: Fallacies in the ‘Experimental Approach’ to Econometrics," Memorandum 13/2017, Oslo University, Department of Economics.
  • Handle: RePEc:hhs:osloec:2017_013
    as

    Download full text from publisher

    File URL: http://www.sv.uio.no/econ/english/research/unpublished-works/working-papers/pdf-files/2017/memo-13-2017.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Keane, Michael P., 2010. "Structural vs. atheoretic approaches to econometrics," Journal of Econometrics, Elsevier, vol. 156(1), pages 3-20, May.
    2. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-455, June.
    3. 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.
    4. Kenneth I. Wolpin & Mark R. Rosenzweig, 2000. "Natural "Natural Experiments" in Economics," Journal of Economic Literature, American Economic Association, vol. 38(4), pages 827-874, December.
    5. Gordon B. Dahl & Andreas Ravndal Kostøl & Magne Mogstad, 2014. "Family Welfare Cultures," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1711-1752.
    6. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    7. Mari Rege & Kjetil Telle & Mark Votruba, 2012. "Social Interaction Effects in Disability Pension Participation: Evidence from Plant Downsizing," Scandinavian Journal of Economics, Wiley Blackwell, vol. 114(4), pages 1208-1239, December.
    8. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    9. Heckman, James J. & Urzúa, Sergio, 2010. "Comparing IV with structural models: What simple IV can and cannot identify," Journal of Econometrics, Elsevier, vol. 156(1), pages 27-37, May.
    10. James H. Stock & Francesco Trebbi, 2003. "Retrospectives: Who Invented Instrumental Variable Regression?," Journal of Economic Perspectives, American Economic Association, vol. 17(3), pages 177-194, Summer.
    11. Schmidt, Peter, 1990. "Three-stage least squares with different instruments for different equations," Journal of Econometrics, Elsevier, vol. 43(3), pages 389-394, March.
    12. Spanos, Aris, 1989. "On Rereading Haavelmo: A Retrospective View of Econometric Modeling," Econometric Theory, Cambridge University Press, vol. 5(3), pages 405-429, December.
    13. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    14. Chesher, Andrew & Smolinski, Konrad, 2012. "IV models of ordered choice," Journal of Econometrics, Elsevier, vol. 166(1), pages 33-48.
    15. Hausman, Jerry A & Newey, Whitney K & Taylor, William E, 1987. "Efficient Estimation and Identification of Simultaneous Equation Models with Covariance Restrictions," Econometrica, Econometric Society, vol. 55(4), pages 849-874, July.
    16. Jerry A. Hausman, 1974. "Full Information Instrumental Variables Estimation of Simultaneous Equations Systems," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 641-652, National Bureau of Economic Research, Inc.
    17. von Hinke, Stephanie & Davey Smith, George & Lawlor, Debbie A. & Propper, Carol & Windmeijer, Frank, 2016. "Genetic markers as instrumental variables," Journal of Health Economics, Elsevier, vol. 45(C), pages 131-148.
    18. Philip Oreopoulos, 2006. "Estimating Average and Local Average Treatment Effects of Education when Compulsory Schooling Laws Really Matter," American Economic Review, American Economic Association, vol. 96(1), pages 152-175, March.
    19. Abbring, Jaap H. & Heckman, James J., 2007. "Econometric Evaluation of Social Programs, Part III: Distributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 72, Elsevier.
    20. Turkington,Darrell A., 2013. "Generalized Vectorization, Cross-Products, and Matrix Calculus," Cambridge Books, Cambridge University Press, number 9781107032002.
    21. Guido W. Imbens, 2010. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 399-423, June.
    22. Andrew Chesher, 2010. "Instrumental Variable Models for Discrete Outcomes," Econometrica, Econometric Society, vol. 78(2), pages 575-601, March.
    23. Joshua D. Angrist & Kathryn Graddy & Guido W. Imbens, 2000. "The Interpretation of Instrumental Variables Estimators in Simultaneous Equations Models with an Application to the Demand for Fish," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(3), pages 499-527.
    24. White, Halbert & Pettenuzzo, Davide, 2014. "Granger causality, exogeneity, cointegration, and economic policy analysis," Journal of Econometrics, Elsevier, vol. 178(P2), pages 316-330.
    25. Petter Lundborg & Erik Plug & Astrid Würtz Rasmussen, 2017. "Can Women Have Children and a Career? IV Evidence from IVF Treatments," American Economic Review, American Economic Association, vol. 107(6), pages 1611-1637, June.
    26. Willassen, Yngve, 2000. "The Et Interview: Professor Olav Reiersøl," Econometric Theory, Cambridge University Press, vol. 16(1), pages 113-125, February.
    27. Anderson, T.W., 2005. "Origins of the limited information maximum likelihood and two-stage least squares estimators," Journal of Econometrics, Elsevier, vol. 127(1), pages 1-16, July.
    28. Hendry, David F. & Johansen, Søren, 2015. "Model Discovery And Trygve Haavelmo’S Legacy," Econometric Theory, Cambridge University Press, vol. 31(1), pages 93-114, February.
    29. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    30. Aldrich, John, 1994. "Haavelmo's Identification Theory," Econometric Theory, Cambridge University Press, vol. 10(1), pages 198-219, March.
    31. Imbens, Guido W., 2014. "Instrumental Variables: An Econometrician's Perspective," IZA Discussion Papers 8048, Institute of Labor Economics (IZA).
    32. repec:fth:prinin:455 is not listed on IDEAS
    33. 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..
    34. Kevin A. Clarke, 2005. "The Phantom Menace: Omitted Variable Bias in Econometric Research," Conflict Management and Peace Science, Peace Science Society (International), vol. 22(4), pages 341-352, September.
    35. Pearl, Judea, 2015. "Trygve Haavelmo And The Emergence Of Causal Calculus," Econometric Theory, Cambridge University Press, vol. 31(1), pages 152-179, February.
    36. John Ioannidis & Chris Doucouliagos, 2013. "What'S To Know About The Credibility Of Empirical Economics?," Journal of Economic Surveys, Wiley Blackwell, vol. 27(5), pages 997-1004, December.
    37. Geraci, Vincent J., 1976. "Identification of simultaneous equation models with measurement error," Journal of Econometrics, Elsevier, vol. 4(3), pages 263-283, August.
    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. Peter Hull & Michal Kolesár & Christopher Walters, 2022. "Labor by design: contributions of David Card, Joshua Angrist, and Guido Imbens," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 603-645, July.
    2. David I. Stern, 2011. "From Correlation to Granger Causality," Crawford School Research Papers 1113, Crawford School of Public Policy, The Australian National University.
    3. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    4. Kugler Franziska & Schwerdt Guido & Wößmann Ludger, 2014. "Ökonometrische Methoden zur Evaluierung kausaler Effekte der Wirtschaftspolitik," Perspektiven der Wirtschaftspolitik, De Gruyter, vol. 15(2), pages 105-132, June.
    5. Thoresen, Thor O. & Vattø, Trine E., 2015. "Validation of the discrete choice labor supply model by methods of the new tax responsiveness literature," Labour Economics, Elsevier, vol. 37(C), pages 38-53.
    6. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    7. Dionissi Aliprantis, 2013. "Covariates and causal effects: the problem of context," Working Papers (Old Series) 1310, Federal Reserve Bank of Cleveland.
    8. Breen, Richard & Ermisch, John, 2021. "Instrumental Variable Estimation in Demographic Studies: The LATE interpretation of the IV estimator with heterogenous effects," SocArXiv vx9m7, Center for Open Science.
    9. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.
    10. Karim Chalak & Halbert White, 2011. "Viewpoint: An extended class of instrumental variables for the estimation of causal effects," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(1), pages 1-51, February.
    11. 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.
    12. Hyunseung Kang & Laura Peck & Luke Keele, 2018. "Inference for instrumental variables: a randomization inference approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1231-1254, October.
    13. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    14. Karim Chalak & Halbert White, 2007. "An Extended Class of Instrumental Variables for the Estimation of Causal Effects," Boston College Working Papers in Economics 692, Boston College Department of Economics, revised 30 Nov 2009.
    15. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
    16. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    17. Michael P. Murray, 2006. "Avoiding Invalid Instruments and Coping with Weak Instruments," Journal of Economic Perspectives, American Economic Association, vol. 20(4), pages 111-132, Fall.
    18. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.
    19. Tilak Abeysinghe & Gu Jiaying, 2009. "Does the IV estimator establish causality? Re-examining Chinese fertility-growth relationship," Microeconomics Working Papers 22758, East Asian Bureau of Economic Research.
    20. Dionissi Aliprantis, 2012. "Redshirting, Compulsory Schooling Laws, and Educational Attainment," Journal of Educational and Behavioral Statistics, , vol. 37(2), pages 316-338, April.

    More about this item

    Keywords

    Identification; Instrumental variables; Omitted variables; Limited information; Experimental approach;
    All these keywords.

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hhs:osloec:2017_013. 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: Mari Strønstad Øverås (email available below). General contact details of provider: https://edirc.repec.org/data/souiono.html .

    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.