In Stata 14.2, we added the ability to use **margins** to estimate covariate effects after **gmm**. In this post, I illustrate how to use **margins** and **marginsplot** after **gmm** to estimate covariate effects for a probit model.

Margins are statistics calculated from predictions of a previously fit model at fixed values of some covariates and averaging or otherwise integrating over the remaining covariates. They can be used to estimate population average parameters like the marginal mean, average treatment effect, or the average effect of a covariate on the conditional mean. I will demonstrate how using margins is useful after estimating a model with the generalized method of moments. Read more…

This post was written jointly with Joerg Luedicke, Senior Social Scientist and Statistician, StataCorp.

The command **gmm** is used to estimate the parameters of a model using the generalized method of moments (GMM). GMM can be used to estimate the parameters of models that have more identification conditions than parameters, overidentified models. The specification of these models can be evaluated using Hansen’s *J* statistic (Hansen, 1982).

We use **gmm** to estimate the parameters of a Poisson model with an endogenous regressor. More instruments than regressors are available, so the model is overidentified. We then use **estat overid** to calculate Hansen’s *J* statistic and test the validity of the overidentification restrictions.

In previous posts Read more…

I use features new to Stata 14.1 to estimate an average treatment effect (ATE) for a heteroskedastic probit model with an endogenous treatment. In 14.1, we added new prediction statistics after **mlexp** that **margins** can use to estimate an ATE.

I am building on a previous post in which I demonstrated how to use **mlexp** to estimate the parameters of a probit model with an endogenous treatment and used **margins** to estimate the ATE for the model Using mlexp to estimate endogenous treatment effects in a probit model. Currently, no official commands estimate the heteroskedastic probit model with an endogenous treatment, so in this post I show how **mlexp** can be used to extend the models estimated by Stata. Read more…

I use features new to Stata 14.1 to estimate an average treatment effect (ATE) for a probit model with an endogenous treatment. In 14.1, we added new prediction statistics after **mlexp** that **margins** can use to estimate an ATE.

I am building on a previous post in which I demonstrated how to use **mlexp** to estimate the parameters of a probit model with sample selection. Our results match those obtained with **biprobit**; see [R] biprobit for more details. In a future post, I use these techniques to estimate treatment-effect parameters not yet available from another Stata command. Read more…

**Overview**

In a previous post, David Drukker demonstrated how to use **mlexp** to estimate the degree of freedom parameter in a chi-squared distribution by maximum likelihood (ML). In this post, I am going to use **mlexp** to estimate the parameters of a probit model with sample selection. I will illustrate how to specify a more complex likelihood in **mlexp** and provide intuition for the probit model with sample selection. Our results match the **heckprobit** command; see **[R] heckprobit** for more details. Read more…