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Learn about Bayesian analysis and see examples of Bayesian features, including Bayesian econometrics and Bayesian model averaging (BMA).

See what's new in Bayesian analysis.

Also see an Overview example.

Estimation

• Thousands of built-in models, by combining
  • over 60 likelihood models, including univariate and multivariate normal, logit, probit, ordered logit, ordered probit, Poisson ...
  • Many prior distributions, including normal, lognormal, multivariate normal, gamma, beta, Wishart ...
  • Continuous, binary, ordinal, count, and survival 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 58 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
• Multiple chains
• Use GUI to fit models
• Use command language to fit models
• Time-series operators

Classes of models

• Linear regression
• Nonlinear regression
• Multivariate regression
• Multivariate nonlinear regression
• Generalized linear models
• Generalized nonlinear models with canonical links
• Zero-inflated models
• Sample-selection models
• Survival models
• Panel-data models
• Multilevel models
• Autoregressive models
• Threshold autoregressive models (TAR)
• Multivariate time-series models
• Multiple-equation models
• Lasso
• Econometrics models

Likelihood models

• Normal
• Student's t
• Lognormal
• Exponential
• Probit
• Logit/Logistic
• Binomial
• Ordered probit
• Ordered logistic
• Poisson
• Negative binomial
• Survival models
• Panel-data models
• Multilevel
• Normal
• Probit, logit/logistic, complementary log-log
• Ordered probit and logit
• Poisson and negative binomial
• Generalized linear models
• Survival
• Multivariate normal (MVN)
• Multivariate VAR
• Linear and nonlinear DSGE models
• User-defined

Prior distributions

• Normal
• Generalized (location-scale) t
• Lognormal
• Uniform
• Gamma
• Inverse gamma
• Exponential
• Laplace
• Cauchy
• Beta
• Chi-squared
• Pareto
• Multivariate normal
• Dirichlet
• Wishart
• Inverse Wishart
• Bernoulli
• Geometric
• Discrete
• Poisson
• User-defined density
• User-defined log density
• Specialized priors
• MVN with exchangeable, independent, identity, and scaled covariances
• Flat
• Jeffreys
• Multivariate Jeffreys
• Zellner's g

Add your own models

• Write your own programs to calculate likelihood function and choose built-in priors
• Write your own programs to calculate posterior density directly
• Use built-in adaptive MH sampling to simulate marginal posterior

Markov chain Monte Carlo (MCMC) methods

• Adaptive Metropolis-Hastings (MH)
• Hybrid MH (adaptive MH with Gibbs updates)
• Full Gibbs sampling for some models

Simulation

• Produce multiple chains
• Three MCMC methods
• Control burn-in iterations
• Control MCMC iterations
• Thinning
• Review model summary before simulation
• Save simulation results for future use

Adaptive MH sampling

• Blocking of parameters
• Adaptation within each block
• Diminishing adaptation
• Random-effects parameters
• 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

Starting values

• Automatic
• May specify for some or all parameters
• May specify for some or all chains

Postestimation Selector

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