Gelman–Rubin convergence diagnostic using multiple chains
Overview MCMC algorithms used for simulating posterior distributions are indispensable tools in Bayesian analysis. A major consideration in MCMC simulations is that of convergence. Has the simulated...
View ArticleTests of forecast accuracy and forecast encompassing
\(\newcommand{\mub}{{\boldsymbol{\mu}}} \newcommand{\eb}{{\boldsymbol{e}}} \newcommand{\betab}{\boldsymbol{\beta}}\)Applied time-series researchers often want to compare the accuracy of a pair of...
View ArticleMultiple equation models: Estimation and marginal effects using gsem
Starting point: A hurdle model with multiple hurdles In a sequence of posts, we are going to illustrate how to obtain correct standard errors and marginal effects for models with multiple steps. Our...
View ArticleMultiple equation models: Estimation and marginal effects using mlexp
We continue with the series of posts where we illustrate how to obtain correct standard errors and marginal effects for models with multiple steps. In this post, we estimate the marginal effects and...
View ArticleUnit-root tests in Stata
\(\newcommand{\mub}{{\boldsymbol{\mu}}} \newcommand{\eb}{{\boldsymbol{e}}} \newcommand{\betab}{\boldsymbol{\beta}}\)Determining the stationarity of a time series is a key step before embarking on any...
View ArticleFlexible discrete choice modeling using a multinomial probit model, part 1
\(\newcommand{\xb}{{\bf x}} \newcommand{\betab}{\boldsymbol{\beta}} \newcommand{\zb}{{\bf z}} \newcommand{\gammab}{\boldsymbol{\gamma}}\)We have no choice but to choose We make choices every day, and...
View ArticleFlexible discrete choice modeling using a multinomial probit model, part 2
Overview In the first part of this post, I discussed the multinomial probit model from a random utility model perspective. In this part, we will have a closer look at how to interpret our estimation...
View ArticleEffects of nonlinear models with interactions of discrete and continuous...
I want to estimate, graph, and interpret the effects of nonlinear models with interactions of continuous and discrete variables. The results I am after are not trivial, but obtaining what I want using...
View ArticleDoctors versus policy analysts: Estimating the effect of interest
\(\newcommand{\Eb}{{\bf E}}\)The change in a regression function that results from an everything-else-held-equal change in a covariate defines an effect of a covariate. I am interested in estimating...
View ArticleProbability differences and odds ratios measure conditional-on-covariate...
\(\newcommand{\Eb}{{\bf E}} \newcommand{\xb}{{\bf x}} \newcommand{\betab}{\boldsymbol{\beta}}\)Differences in conditional probabilities and ratios of odds are two common measures of the effect of a...
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...
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 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 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 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 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 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 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 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 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...
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