Archive for October 2016

Long-run restrictions in a structural vector autoregression

\(\def\bfA{{\bf A}}
\def\bfB{{\bf }}
\def\bfC{{\bf C}}\)Introduction

In this blog post, I describe Stata’s capabilities for estimating and analyzing vector autoregression (VAR) models with long-run restrictions by replicating some of the results of Blanchard and Quah (1989). Read more…

Programming an estimation command in Stata: Writing an estat postestimation command

estat commands display statistics after estimation. Many of these statistics are diagnostics or tests used to evaluate model specification. Some statistics are available after all estimation commands; others are command specific.

I illustrate how estat commands work and then show how to write a command-specific estat command for the mypoisson command that I have been developing.

This is the 28th post in the series Programming an estimation command in Stata. I recommend that you start at the beginning. See Programming an estimation command in Stata: A map to posted entries for a map to all the posts in this series. Read more…

The new Stata News

Have you seen the latest issue of the Stata News? It has a new format that we think you will love. And, I want to make sure that you are not missing out on the articles discussing what our developers and users are doing with Stata.

We have a new section, User’s corner, that highlights interesting, useful, and fun user contributions. In this issue, you will see how Belén Chavez uses Stata to analyze her Fitbit® data.

We kept your favorite sections, including Spotlight articles written by Stata developers. In this issue, Enrique Pinzón demonstrates Estimating, graphing, and interpreting interactions using margins.

If you haven’t been reading the Stata News, you may want to browse previous Spotlight articles on topics such as endogenous treatment effects, item response theory (IRT), and Bayesian analysis. You can find all the previous Spotlight articles here.

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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 ( 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…

Estimating covariate effects after gmm

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…