mte: Fitting a Marginal Treatment Effects (MTE) Model. In localIV: Estimation of Marginal Treatment Effects using Local Instrumental Variables

Description

mte fits a MTE model using either the semiparametric local instrumental variables (local IV) method or the normal selection model (Heckman, Urzua, Vytlacil 2006). The user supplies a formula for the treatment selection equation, a formula for the outcome equations, and a data frame containing all variables. The function returns an object of class mte. Observations that contain NA (either in selection or in outcome) are removed.

Usage

 1 2 3 4 5 6 7 8 9 10 11 mte( selection, outcome, data = NULL, method = c("localIV", "normal"), bw = NULL ) mte_localIV(mf_s, mf_o, bw = NULL) mte_normal(mf_s, mf_o)

Arguments

 selection A formula representing the treatment selection equation. outcome A formula representing the outcome equations where the left hand side is the observed outcome and the right hand side includes predictors of both potential outcomes. data A data frame, list, or environment containing the variables in the model. method How to estimate the model: either "localIV" for the semiparametric local IV method or "normal" for the normal selection model. bw Bandwidth used for the local polynomial regression in the local IV approach. Default is 0.25. mf_s A model frame for the treatment selection equations returned by model.frame mf_o A model frame for the outcome equations returned by model.frame

Details

mte_localIV estimates \textup{MTE}(x, u) using the semiparametric local IV method, and mte_normal estimates \textup{MTE}(x, u) using the normal selection model.

Value

An object of class mte.

 coefs A list of coefficient estimates: gamma for the treatment selection equation, beta10 (intercept) and beta1 (slopes) for the baseline outcome equation, beta20 (intercept) and beta2 (slopes) for the treated outcome equation, and theta1 and theta2 for the error covariances when method = "normal". ufun A function representing the unobserved component of \textup{MTE}(x, u). ps Estimated propensity scores. ps_model The propensity score model, an object of class glm if method = "localIV", or an object of class selection if method = "normal". mf_s The model frame for the treatment selection equation. mf_o The model frame for the outcome equations. complete_row A logical vector indicating whether a row is complete (no missing variables) in the original data call The matched call.

References

Heckman, James J., Sergio Urzua, and Edward Vytlacil. 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity." The Review of Economics and Statistics 88:389-432.