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Instrumental Variables Estimation with Partially Missing Instruments

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  • Mogstad, Magne

    ()
    (University of Chicago)

  • Wiswall, Matthew

    ()
    (Arizona State University)

Abstract

We examine instrumental variables estimation in situations where the instrument is only observed for a sub-sample, which is fairly common in empirical research. Typically, researchers simply limit the analysis to the sub-sample where the instrument is non-missing. We show that when the instrument is non-randomly missing, standard IV estimators require strong, auxiliary assumptions to be consistent. In many (quasi)natural experiments, the auxiliary assumptions are unlikely to hold. We therefore introduce alternative IV estimators that are robust to non-randomly missing instruments without auxiliary assumptions. A Monte-Carlo study illustrates our results.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 4689.

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Length: 23 pages
Date of creation: Jan 2010
Date of revision:
Publication status: published in: Economics Letters, 2012, 114 (2), 186-189
Handle: RePEc:iza:izadps:dp4689

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Keywords: sub-sample estimation; sample selection; partially missing instruments; instrumental variables;

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References

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  1. Magne Mogstad & Matthew Wiswall, 2009. "How Linear Models Can Mask Non-Linear Causal Relationships. An Application to Family Size and Children's Education," Discussion Papers, Research Department of Statistics Norway 586, Research Department of Statistics Norway.
  2. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, Elsevier, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74 Elsevier.
  3. Angrist, Joshua D, 2001. "Estimations of Limited Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 19(1), pages 2-16, January.
  4. Heckman, James J. & Urzua, Sergio & Vytlacil, Edward, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," IZA Discussion Papers 2320, Institute for the Study of Labor (IZA).
  5. David Card, 1993. "Using Geographic Variation in College Proximity to Estimate the Return to Schooling," NBER Working Papers 4483, National Bureau of Economic Research, Inc.
  6. Joshua Angrist & Alan Krueger, 1990. "Does Compulsory School Attendance Affect Schooling and Earnings?," Working Papers, Princeton University, Department of Economics, Industrial Relations Section. 653, Princeton University, Department of Economics, Industrial Relations Section..
  7. Carneiro, Pedro & Heckman, James J., 2002. "The Evidence on Credit Constraints in Post-Secondary Schooling," IZA Discussion Papers 518, Institute for the Study of Labor (IZA).
  8. Julio Cáceres-Delpiano, 2006. "The Impacts of Family Size on Investment in Child Quality," Journal of Human Resources, University of Wisconsin Press, vol. 41(4).
  9. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, Econometric Society, vol. 69(5), pages 1127-60, September.
  10. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, Econometric Society, vol. 62(2), pages 467-75, March.
  11. Rosenzweig, Mark R & Wolpin, Kenneth I, 1980. "Testing the Quantity-Quality Fertility Model: The Use of Twins as a Natural Experiment," Econometrica, Econometric Society, Econometric Society, vol. 48(1), pages 227-40, January.
  12. S Black & Paul Devereux & Kjell Salvanes, 2005. "The More the Merrier? The Effect of Family Size and Birth Order on Childrens Education," CEE Discussion Papers, Centre for the Economics of Education, LSE 0050, Centre for the Economics of Education, LSE.
  13. Åslund, Olof & Grönqvist, Hans, 2010. "Family size and child outcomes: Is there really no trade-off?," Labour Economics, Elsevier, Elsevier, vol. 17(1), pages 130-139, January.
  14. repec:fth:prinin:317 is not listed on IDEAS
  15. Joshua Angrist & Victor Lavy & Analia Schlosser, 2010. "Multiple Experiments for the Causal Link between the Quantity and Quality of Children," Journal of Labor Economics, University of Chicago Press, University of Chicago Press, vol. 28(4), pages 773-824, October.
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Cited by:
  1. Valentina Dimitrova-Grajzl & Peter Grajzl & Katarina Zajc, 2014. "Inside Post-Socialist Courts: The Determinants of Adjudicatory Outcomes in Slovenian Commercial Disputes," CESifo Working Paper Series 4894, CESifo Group Munich.
  2. Bagger, Jesper & Birchenall, Javier A. & Mansour, Hani & Urzua, Sergio, 2013. "Education, Birth Order, and Family Size," IZA Discussion Papers 7454, Institute for the Study of Labor (IZA).
  3. Wang, Le, 2013. "Estimating returns to education when the IV sample is selective," Labour Economics, Elsevier, Elsevier, vol. 21(C), pages 74-85.

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