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Comparing 2SLS vs 2SRI for Binary Outcomes and Binary Exposures

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  • Anirban Basu
  • Norma Coe
  • Cole G. Chapman

Abstract

This study uses Monte Carlo simulations to examine the ability of the two-stage least-squares (2SLS) estimator and two-stage residual inclusion (2SRI) estimators with varying forms of residuals to estimate the local average and population average treatment effect parameters in models with binary outcome, endogenous binary treatment, and single binary instrument. The rarity of the outcome and the treatment are varied across simulation scenarios. Results show that 2SLS generated consistent estimates of the LATE and biased estimates of the ATE across all scenarios. 2SRI approaches, in general, produce biased estimates of both LATE and ATE under all scenarios. 2SRI using generalized residuals minimizes the bias in ATE estimates. Use of 2SLS and 2SRI is illustrated in an empirical application estimating the effects of long-term care insurance on a variety of binary healthcare utilization outcomes among the near-elderly using the Health and Retirement Study.

Suggested Citation

  • Anirban Basu & Norma Coe & Cole G. Chapman, 2017. "Comparing 2SLS vs 2SRI for Binary Outcomes and Binary Exposures," NBER Working Papers 23840, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:23840
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    1. repec:eee:socmed:v:229:y:2019:i:c:p:117-125 is not listed on IDEAS
    2. repec:eee:respol:v:47:y:2018:i:10:p:1945-1963 is not listed on IDEAS

    More about this item

    JEL classification:

    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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