Archive

Posts Tagged ‘treatment effects’

An ordered-probit inverse probability weighted (IPW) estimator

teffects ipw uses multinomial logit to estimate the weights needed to estimate the potential-outcome means (POMs) from a multivalued treatment. I show how to estimate the POMs when the weights come from an ordered probit model. Moment conditions define the ordered probit estimator and the subsequent weighted average used to estimate the POMs. I use gmm to obtain consistent standard errors by stacking the ordered-probit moment conditions and the weighted mean moment conditions. Read more…

Multiple-equation models: Estimation and marginal effects using gmm

We estimate the average treatment effect (ATE) for an exponential mean model with an endogenous treatment. We have a two-step estimation problem where the first step corresponds to the treatment model and the second to the outcome model. As shown in Using gmm to solve two-step estimation problems, this can be solved with the generalized method of moments using gmm.

This continues the series of posts where we illustrate how to obtain correct standard errors and marginal effects for models with multiple steps. In the previous posts, we used gsem and mlexp to estimate the parameters of models with separable likelihoods. In the current model, because the treatment is endogenous, the likelihood for the model is no longer separable. We demonstrate how we can use gmm to estimate the parameters in these situations. Read more…

Using mlexp to estimate endogenous treatment effects in a heteroskedastic probit model

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…

Using mlexp to estimate endogenous treatment effects in a probit model

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…

Introduction to treatment effects in Stata: Part 2

This post was written jointly with David Drukker, Director of Econometrics, StataCorp.

In our last post, we introduced the concept of treatment effects and demonstrated four of the treatment-effects estimators that were introduced in Stata 13.  Today, we will talk about two more treatment-effects estimators that use matching. Read more…

Introduction to treatment effects in Stata: Part 1

This post was written jointly with David Drukker, Director of Econometrics, StataCorp.

The topic for today is the treatment-effects features in Stata.

Treatment-effects estimators estimate the causal effect of a treatment on an outcome based on observational data.

In today’s posting, we will discuss four treatment-effects estimators:

  1. RA: Regression adjustment
  2. IPW: Inverse probability weighting
  3. IPWRA: Inverse probability weighting with regression adjustment
  4. AIPW: Augmented inverse probability weighting

We’ll save the matching estimators for part 2.

We should note that nothing about treatment-effects estimators magically extracts causal relationships. As with any regression analysis of observational data, the causal interpretation must be based on a reasonable underlying scientific rationale. Read more…

Using gmm to solve two-step estimation problems

Two-step estimation problems can be solved using the gmm command.

When a two-step estimator produces consistent point estimates but inconsistent standard errors, it is known as the two-step-estimation problem. For instance, inverse-probability weighted (IPW) estimators are a weighted average in which the weights are estimated in the first step. Two-step estimators use first-step estimates to estimate the parameters of interest in a second step. The two-step-estimation problem arises because the second step ignores the estimation error in the first step.

One solution is to convert the two-step estimator into a one-step estimator. My favorite way to do this conversion is to stack the equations solved by each of the two estimators and solve them jointly. This one-step approach produces consistent point estimates and consistent standard errors. There is no two-step problem because all the computations are performed jointly. Newey (1984) derives and justifies this approach. Read more…

Stata 13 ships June 24

There’s a new release of Stata. You can order it now, it starts shipping on June 24, and you can find out about it at www.stata.com/stata13/.

Well, we sure haven’t made that sound exciting when, in fact, Stata 13 is a big — we mean really BIG — release, and we really do want to tell you about it.

Rather than summarizing, however, we’ll send you to the website, which in addition to the standard marketing materials, has technical sheets, demonstrations, and even videos of the new features.

And all 11,000 pages of the manuals are now online.