Bayesian 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...
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 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 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 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 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 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 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 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 ArticleSolving 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 ArticleEstimating 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...
View ArticleQuantile regression allows covariate effects to differ by quantile
Quantile regression models a quantile of the outcome as a function of covariates. Applied researchers use quantile regressions because they allow the effect of a covariate to differ across conditional...
View ArticleStructural vector autoregression models
\(\def\bfy{{\bf y}} \def\bfA{{\bf A}} \def\bfB{{\bf B}} \def\bfu{{\bf u}} \def\bfI{{\bf I}} \def\bfe{{\bf e}} \def\bfC{{\bf C}} \def\bfsig{{\boldsymbol \Sigma}}\)In my last post, I discusssed...
View ArticleAn 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...
View ArticleCointegration or spurious regression?
\(\newcommand{\betab}{\boldsymbol{\beta}}\)Time-series data often appear nonstationary and also tend to comove. A set of nonstationary series that are cointegrated implies existence of a long-run...
View ArticleTwo faces of misspecification in maximum likelihood: Heteroskedasticity and...
For a nonlinear model with heteroskedasticity, a maximum likelihood estimator gives misleading inference and inconsistent marginal effect estimates unless I model the variance. Using a robust estimate...
View ArticleGroup comparisons in structural equation models: Testing measurement invariance
When fitting almost any model, we may be interested in investigating whether parameters differ across groups such as time periods, age groups, gender, or school attended. In other words, we may wish to...
View ArticleExact matching on discrete covariates is the same as regression adjustment
I illustrate that exact matching on discrete covariates and regression adjustment (RA) with fully interacted discrete covariates perform the same nonparametric estimation. Comparing exact matching with...
View ArticleVector autoregressions in Stata
Introduction In a univariate autoregression, a stationary time-series variable \(y_t\) can often be modeled as depending on its own lagged values: \begin{align} y_t = \alpha_0 + \alpha_1 y_{t-1} +...
View ArticleMultiple-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...
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