In this article, I present ivtreatreg, a command for fitting four different binary treatment models with and without heterogeneous average treatment effects under selection-on-unobservables (that is, treatment endogeneity). Depending on the model specified by the user, ivtreatreg provides...

This article reviews Mostly Harmless Econometrics: An Empiricist's Companion, by Joshua D. Angrist and Jorn-Steffen Pischke.

The two-stage least-squares (2SLS) instrumental variables estimator is commonly used to address endogeneity. However, the estimator suffers from bias that is exacerbated when the instruments are only weakly correlated with the en- dogenous variables and when many instruments are used. In this...

We consider inference in the linear regression model with one endoge- nous variable and potentially weak instruments. We construct confidence sets for the coefficient on the endogenous variable by inverting the Anderson-Rubin, Lagrange multiplier, and conditional likelihood-ratio tests. Our...

This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003)....

Problems with inferring causal relationships from nonexperimental data are briefly reviewed, and four broad classes of methods designed to allow estimation of and inference about causal parameters are described: panel regression, matching or reweighting, instrumental variables, and regression...

This paper derives and gives explicit formulas for a derived sandwich variance estimate. This variance estimate is appropriate for generalized linear additive measurement error models fitted using instrumental variables. We also generalize the known results for linear regression. As such, this...

We discuss and illustrate the method of simulation extrapolation for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). As in Hardin, Schmiediche, and Carroll (2003),...

The classical instrumental-variables estimator is extremely sensitive to the presence of outliers in the sample. This is a concern because outliers can strongly distort the estimated effect of a given regressor on the dependent variable. Although outlier diagnostics exist, they frequently fail...