Bayesian model averaging (BMA)

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Bayesian model averaging (BMA) New

Perform Bayesian model averaging with the bma suite to account for model uncertainty in your analysis. Perform model choice, inference, and prediction. Identify influential models and important predictors. Explore model complexity, model fit, and predictive performance. Perform sensitivity analysis to the assumptions about importance of models and predictors. Generate predictions. And much more.

Learn about Bayesian model averaging and see examples of Bayesian model averaging features.

Watch Bayesian model averaging.

Also see overview examples.

Bayesian model averaging (BMA) for linear regression

Model priors: uniform, binomial, and beta-binomial
Many fixed and random g-priors
Grouping of predictors
Support of always-included predictors
Heredity rules for interactions
Support of factor variables and times-series operators
Automatic grouping of factor variables and interactions

Model space

Full model enumeration with fewer than 25 predictors
MC3 sampling with fixed g parameter
MC3 and MH sampling with random g parameter
Options to control MCMC sampling
Convergence diagnostics with sampling

BMA posterior summaries

Analytical posterior means and standard deviations for regression coefficients with fixed g
MCMC posterior means and standard deviations for regression coefficients with sampling
Posterior summaries for random g and the shrinkage parameter
Posterior model probabilities (PMPs) and cumulative PMPs
Posterior inclusion probabilities (PIPs) for predictors
Posterior model-size summaries

Simulating posterior distributions of model parameters

Analytical posterior distributions
MCMC-based posterior distributions

Model and variable-inclusion summary

Models ranked by PMP
Highest probability model (HPM)
Median probability model (MPM)
Models containing specific predictors

Posterior distributions of regression coefficients

Density plots for one coefficient
Density plots for multiple or for all coefficients
Analytical posterior densities
MCMC-sample posterior densities
Posterior probability of noninclusion
Customized graphs

Model-size distribution summaries

Analytical prior mean and median model size
Analytical posterior mean and median model size
Frequency posterior mean and median model size

BMA plots

Checking BMA convergence
Prior and posterior model-size distribution plots
Variable-inclusion maps
Customized graphs

Jointness measures for predictors

Doppelhofer—Weeks measure
Ley—Steel type 1 measure
Ley—Steel type 2 measure
Yule's Q measure
Modified Yule's Q measure

Log predictive-score (LPS)

Analytical LPS
Frequency-based LPS
LPS summaries
Entropy

BMA predictions

Analytical posterior predictive means and standard deviations with fixed g
MCMC-sample posterior predictive summaries: mean, median, standard deviation, and credible intervals
Predictions of simulated outcome
Replicates of simulated outcome
Log predictive-score

Postestimation Selector

View and run all postestimation features for your command
- Automatically updated as estimation commands are run

Watch Postestimation Selector.

Posterior summaries

Means
Medians
Standard deviations
Monte Carlo standard errors (MCSEs)
Credible intervals (CrIs)

Compute any of the above for parameters or functions of parameters
Summaries for log likelihood and log posterior
Summaries for simulated outcomes and their functions

MCSE estimation methods

Using effective sample size
Using batch means

Bayesian predictions

Generate predictions: simulate outcome values and their functions
Save all or a subset of predictions in a separate dataset
Save posterior summaries of predictions as variables in current dataset
Save a subset of MCMC replicates as variables in current dataset
Obtain graphical and posterior summaries, perform hypothesis tests, and more
Use built-in tools to create functions of predictions, or write your own Mata functions and Stata programs
Generate replicated data for posterior predictive checks

Watch Bayesian analysis: Predictions.

Model goodness of fit

Posterior predictive p-values
MCMC replicates
Predictions

Tools to check MCMC convergence

Diagnostic plots in compact form
Trace plots
Autocorrelation plots
Histograms
Density plots
Cumulative sum plots
Bivariate scatterplots
Produce any of the 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 the above for parameters or functions of parameters

Additional resources

Watch Bayesian model averaging

See Bayesian analysis
See Bayesian econometrics

See New in Stata 18 to learn about what was added in Stata 18.

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