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Estimating Treatment Effects in the Presence of Correlated Binary Outcomes and Contemporaneous Selection

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  • Matthew P. Rabbitt

    (Economic Research Service, U.S. Department of Agriculture)

Abstract

Estimating the causal effect of a treatment is challenging when selection into the treatment is based on contemporaneous unobservable characteristics, and the outcome of interest is represented by a series of correlated binary outcomes. Under these assumptions, traditional non-linear panel data models, such as the random-effects logistic model, will produce biased estimates of the treatment effect due to correlation between the treatment variable and model unobservables. In this presentation, I will introduce a new Stata estimation command, ETXTLOGIT, which can estimate a model where the outcome is a series of J correlated logistic binary outcomes and selection into the treatment is based on contemporaneous unobservable characteristics. The presentation will introduce the new estimation command, present Monte Carlo evidence, and offer empirical examples. Special cases of the model will be discussed, including applications based on the explanatory (behavioral) Rasch model, a model from Item Response Theory (IRT).

Suggested Citation

  • Matthew P. Rabbitt, 2017. "Estimating Treatment Effects in the Presence of Correlated Binary Outcomes and Contemporaneous Selection," 2017 Stata Conference 23, Stata Users Group.
    • Handle: RePEc:boc:scon17:23
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    File URL: http://fmwww.bc.edu/repec/scon2017/Baltimore17_Rabbitt.pdf
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    References listed on IDEAS

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    1. Joseph Terza, 2009. "Parametric Nonlinear Regression with Endogenous Switching," Econometric Reviews, Taylor & Francis Journals, vol. 28(6), pages 555-580.
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