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...
View ArticleLong-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...
View ArticleIntroduction to Bayesian statistics, part 1: The basic concepts
In this blog post, I’d like to give you a relatively nontechnical introduction to Bayesian statistics. The Bayesian approach to statistics has become increasingly popular, and you can fit Bayesian...
View ArticleIntroduction to Bayesian statistics, part 2: MCMC and the Metropolis–Hastings...
In this blog post, I’d like to give you a relatively nontechnical introduction to Markov chain Monte Carlo, often shortened to “MCMC”. MCMC is frequently used for fitting Bayesian statistical models....
View ArticleUnderstanding truncation and censoring
Truncation and censoring are two distinct phenomena that cause our samples to be incomplete. These phenomena arise in medical sciences, engineering, social sciences, and other research fields. If we...
View ArticleEstimation under omitted confounders, endogeneity, omitted variable bias, and...
Initial thoughts Estimating causal relationships from data is one of the fundamental endeavors of researchers, but causality is elusive. In the presence of omitted confounders, endogeneity, omitted...
View ArticleStata 15 announced, available now
We announced Stata 15 today. It’s a big deal because this is Stata’s biggest release ever. I posted to Statalist this morning and listed sixteen of the most important new features. Here on the blog I...
View ArticleNonparametric regression: Like parametric regression, but not
Initial thoughts Nonparametric regression is similar to linear regression, Poisson regression, and logit or probit regression; it predicts a mean of an outcome for a set of covariates. If you work with...
View ArticleEstimating the parameters of DSGE models
Introduction Dynamic stochastic general equilibrium (DSGE) models are used in macroeconomics to model the joint behavior of aggregate time series like inflation, interest rates, and unemployment. They...
View ArticleBayesian logistic regression with Cauchy priors using the bayes prefix
Introduction Stata 15 provides a convenient and elegant way of fitting Bayesian regression models by simply prefixing the estimation command with bayes. You can choose from 45 supported estimation...
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