Archive

Posts Tagged ‘sample selection’

Solving missing data problems using inverse-probability-weighted estimators

We discuss estimating population-averaged parameters when some of the data are missing. In particular, we show how to use gmm to estimate population-averaged parameters for a probit model when the process that causes some of the data to be missing is a function of observable covariates and a random process that is independent of the outcome. This type of missing data is known as missing at random, selection on observables, and exogenous sample selection.

This is a follow-up to an earlier post where we estimated the parameters of a probit model under endogenous sample selection (http://blog.stata.com/2015/11/05/using-mlexp-to-estimate-endogenous-treatment-effects-in-a-probit-model/). In endogenous sample selection, the random process that affects which observations are missing is correlated with an unobservable random process that affects the outcome. Read more…

Probit model with sample selection by mlexp

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…

Categories: Statistics Tags: , ,