Estimation

Thousands of built-in models, by combining

10 likelihood models including univariate and multivariate normal, logit, probit, ordered logit, ordered probit, Poisson, …

18 prior distributions including normal, lognormal, multivariate normal, gamma, beta, Wishart, …

Continuous, binary, ordinal, and count outcomes

Univariate, multivariate, and multiple-equation models

Linear and nonlinear models

Continuous univariate, multivariate, and discrete priors

bayes: prefix

Simply type bayes: in front of any of 45 estimation commands to fit Bayesian regression models

Change any of the default priors

Change any of the simulation or sampling settings

Time-series operators

Control Panel lets you specify and fit models from an easy-to-use interface

Use GUI to fit models

Use command language to fit models

Watch bayes: prefix for fitting Bayesian regressions.
Watch Graphical user interface for Bayesian analysis in Stata 14.

 

Classes of models

Linear regression

Nonlinear regression

Multivariate regression

Multivariate nonlinear regression

Generalized linear models with canonical links

Generalized nonlinear models with canonical links

Zero-inflated models

Sample-selection models

Survival models

Multilevel models

Autoregressive models

Multiple-equation models

 

Likelihood models

Normal

Lognormal

Exponential

Probit

Logit/Logistic

Binomial

Ordered probit

Ordered logistic

Poisson

Negative binomial

Multivariate normal (MVN)

User-defined

Multilevel

Normal

Probit, logit/logistic, complementary log-log

Ordered probit and logit

Poisson and negative binomial

Generalized linear models

Survival

 

Prior distributions

Normal

Lognormal

Uniform

Gamma

Inverse gamma

Exponential

Beta

Chi-squared

Multivariate normal

Wishart

Inverse Wishart

Bernoulli

Discrete

Poisson

User-defined density

User-defined log density

Specialized priors

Flat

Jeffreys

Multivariate Jeffreys

Zellner’s g

 

Markov chain Monte Carlo (MCMC) methods

Adaptive Metropolis-Hastings (MH)

Hybrid MH (adaptive MH with Gibbs updates)

Full Gibbs sampling for some models

 

Adaptive MH sampling

Blocking of parameters

Adaptation within each block

Diminishing adaptation

Random-effects parametersNew

Control scale and covariance of the proposal distribution

Control adaptation

Length of adaptation

Maximum and minimum numbers of adaptive iterations

Acceptance rate

Adaptation rate

Target acceptance rate

Acceptance rate tolerance

Simulation

Three MCMC methods

Control burn-in iterations

Control MCMC iterations

Thinning

Review model summary before simulation

Save simulation results for future use

 

Starting values

Automatic

May specify for some or all parameters

 

Factor variables

Automatically create indicators based on categorical variables

Form interactions among discrete and continuous variables

Include polynomial terms

Watch Introduction to Factor Variables in Stata tutorials

 

graph

 

Add your own models

Write your own programs to calculate likelihood function and choose built-in priors

Write you own programs to calculate posterior density directly

Use built-in adaptive MH sampling to simulate marginal posterior

 

Postestimation Selector

View and run all postestimation features for your command

Automatically updated as estimation commands are run

Watch Postestimation Selector.

 

Graphical tools to check MCMC convergence

Diagnostics plots in compact form

Trace plots

Autocorrelation plots

Histograms

Density plots

Cumulative sum plots

Bivariate scatterplots

Produce any of above for parameters or functions of parameters

Multiple separate graphs or multiple plots on one graph

Pause between multiple graphs

Customize the look of each graph

 

Tools to check MCMC efficiency

Effective sample sizes

Autocorrelation times

Efficiencies

Compute any of above for parameters or functions of parameters

 

Posterior summaries

Means

Medians

Standard deviations

Monte Carlo standard errors (MCSEs)

Credible intervals (CrIs)

Equal-tailed

Highest posterior density (HPD)

Compute any of above for parameters or functions of parameters

Summaries for log likelihood and log posterior

 

MCSE estimation methods

using effective sample size

using batch means

 

Hypothesis testing

Interval-based by computing probability of an interval hypothesis

Linear and nonlinear

Single and joint

Continuous parameters

Discrete parameters

Model-based by computing model posterior probabilities

 

Model comparison

Deviance information criterion (DIC)

Bayes factors

Model posterior probabilities

Nested and nonnested models

 

Additional resources

Watch bayes: prefix for fitting Bayesian regressions

In the Spotlight: Bayesian “random effects” models