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Comparing IV with structural models: What simple IV can and cannot identify

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  • Heckman, James J.
  • Urzúa, Sergio

Abstract

This paper compares the economic questions addressed by instrumental variables estimators with those addressed by structural approaches. We discuss Marschak's Maxim: estimators should be selected on the basis of their ability to answer well-posed economic problems with minimal assumptions. A key identifying assumption that allows structural methods to be more informative than IV can be tested with data and does not have to be imposed.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 156 (2010)
Issue (Month): 1 (May)
Pages: 27-37

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Handle: RePEc:eee:econom:v:156:y:2010:i:1:p:27-37

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Instrumental variables Structural approaches Marschak's Maxim;

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  1. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 70 Elsevier.
  2. Heckman, James J., 2008. "Econometric Causality," IZA Discussion Papers 3425, Institute for the Study of Labor (IZA).
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  5. Cunha, Flavio & Heckman, James J. & Navarro, Salvador, 2007. "The Identification and Economic Content of Ordered Choice Models with Stochastic Thresholds," IZA Discussion Papers 2940, Institute for the Study of Labor (IZA).
  6. Keane, Michael P & Wolpin, Kenneth I, 1997. "The Career Decisions of Young Men," Journal of Political Economy, University of Chicago Press, vol. 105(3), pages 473-522, June.
  7. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
  8. Flavio Cunha & James Heckman & Salvador Navarro, 2005. "Separating uncertainty from heterogeneity in life cycle earnings," Oxford Economic Papers, Oxford University Press, vol. 57(2), pages 191-261, April.
  9. James J. Heckman & Daniel Schmierer, 2009. "Testing the Correlated Random Coefficient Model," Working Papers 200937, Geary Institute, University College Dublin.
  10. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1, October.
  11. Heckman, James J. & Navarro, Salvador, 2005. "Dynamic Discrete Choice and Dynamic Treatment Effects," IZA Discussion Papers 1790, Institute for the Study of Labor (IZA).
  12. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects and Econometric Policy Evaluation," NBER Working Papers 11259, National Bureau of Economic Research, Inc.
  13. James J. Heckman & Sergio Urzua & Edward J. Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," NBER Working Papers 12574, National Bureau of Economic Research, Inc.
  14. Stephen V. Cameron & James J. Heckman, 1998. "Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts of American Males," Journal of Political Economy, University of Chicago Press, vol. 106(2), pages 262-333, April.
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  16. Matzkin, Rosa L., 1986. "Restrictions of economic theory in nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 42, pages 2523-2558 Elsevier.
  17. James J. Heckman & Jora Stixrud & Sergio Urzua, 2006. "The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior," NBER Working Papers 12006, National Bureau of Economic Research, Inc.
  18. Matzkin, Rosa L., 2007. "Nonparametric identification," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 73 Elsevier.
  19. Matzkin, Rosa L., 1993. "Nonparametric identification and estimation of polychotomous choice models," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 137-168, July.
  20. Flavio Cunha & James J. Heckman, 2007. "The Evolution of Inequality, Heterogeneity and Uncertainty in Labor Earnings in the U.S. Economy," NBER Working Papers 13526, National Bureau of Economic Research, Inc.
  21. Sergio Urzúa, 2008. "Racial Labor Market Gaps: The Role of Abilities and Schooling Choices," Journal of Human Resources, University of Wisconsin Press, vol. 43(4).
  22. James J. Heckman & Paul A. LaFontaine & Pedro L. Rodriguez, 2008. "Taking the Easy Way Out: How the GED Testing Program Induces Students to Drop Out," Working Papers 200829, Geary Institute, University College Dublin.
  23. Jacob A. Mincer, 1974. "Introduction to "Schooling, Experience, and Earnings"," NBER Chapters, in: Schooling, Experience, and Earnings, pages 1-4 National Bureau of Economic Research, Inc.
  24. James J. HECKMAN & Sergio URZUA & Edward VYTLACIL, 2008. "Instrumental Variables in Models with Multiple Outcomes: The General Unordered Case," Annales d'Economie et de Statistique, ENSAE, issue 91-92, pages 151-174.
  25. Heckman, James & Layne-Farrar, Anne & Todd, Petra, 1996. "Human Capital Pricing Equations with an Application to Estimating the Effect of Schooling Quality on Earnings," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 562-610, November.
  26. Carneiro, Pedro & Hansen, Karsten T. & Heckman, James J., 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College Choice," IZA Discussion Papers 767, Institute for the Study of Labor (IZA).
  27. 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.
  28. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 71 Elsevier.
  29. Matzkin, Rosa L, 1992. "Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models," Econometrica, Econometric Society, vol. 60(2), pages 239-70, March.
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As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Rise and Fall of a Method
    by Agent Continuum in Agent Continuum on 2010-04-21 13:53:35
  2. Causality and Econometrics
    by Liam Delaney in Geary Behaviour Centre on 2009-04-28 19:19:00
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