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Two-Sample Instrumental Variables Estimators

Author

Listed:
  • Atsushi Inoue

    (North Carolina State University)

  • Gary Solon

    (Michigan State University)

Abstract

Following an influential article by Angrist and Krueger (1992) on two-sample instrumental variables (TSIV) estimation, numerous empirical researchers have applied a computationally convenient two-sample two-stage least squares (TS2SLS) variant of Angrist and Krueger's estimator. In the two-sample context, unlike the single-sample situation, the IV and 2SLS estimators are numerically distinct. We derive and compare the asymptotic distributions of the two estimators and find that the commonly used TS2SLS estimator is more asymptotically efficient than the TSIV estimator. We also resolve some confusion in the literature about how to estimate standard errors for the TS2SLS estimator. © 2010 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • Atsushi Inoue & Gary Solon, 2010. "Two-Sample Instrumental Variables Estimators," The Review of Economics and Statistics, MIT Press, vol. 92(3), pages 557-561, August.
    • Handle: RePEc:tpr:restat:v:92:y:2010:i:3:p:557-561
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    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Andersen, Torben G & Sorensen, Bent E, 1996. "GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 328-352, July.
    3. Borjas, George J., 2004. "Food insecurity and public assistance," Journal of Public Economics, Elsevier, vol. 88(7-8), pages 1421-1443, July.
    4. Joshua D. Angrist & Alan B. Krueger, 1993. "Split Sample Instrumental Variables," Working Papers 699, Princeton University, Department of Economics, Industrial Relations Section..
    5. Joshua D. Angrist & Alan B. Krueger, 1990. "The Effect of Age at School Entry on Educational Attainment: An Application of Instrumental Variables with Moments from Two Samples," NBER Working Papers 3571, National Bureau of Economic Research, Inc.
    6. Murphy, Kevin M & Topel, Robert H, 2002. "Estimation and Inference in Two-Step Econometric Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 88-97, January.
    7. Currie, Janet & Yelowitz, Aaron, 2000. "Are public housing projects good for kids?," Journal of Public Economics, Elsevier, vol. 75(1), pages 99-124, January.
    8. Thomas S. Dee & William N. Evans, 2003. "Teen Drinking and Educational Attainment: Evidence from Two-Sample Instrumental Variables Estimates," Journal of Labor Economics, University of Chicago Press, vol. 21(1), pages 178-209, January.
    9. Angrist, J D & Imbens, G W & Krueger, A B, 1999. "Jackknife Instrumental Variables Estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(1), pages 57-67, Jan.-Feb..
    10. Tauchen, George, 1986. "Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 397-416, October.
    11. Angrist, Joshua D & Krueger, Alan B, 1995. "Split-Sample Instrumental Variables Estimates of the Return to Schooling," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 225-235, April.
    12. Bjorklund, Anders & Jantti, Markus, 1997. "Intergenerational Income Mobility in Sweden Compared to the United States," American Economic Review, American Economic Association, vol. 87(5), pages 1009-1018, December.
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    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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