Bayesian Estimation Tools (BET)¶
A suite of tools for bayesian estimation and analysis of a pre-packaged models in GAUSS.
Description¶
The Bayesian Estimation Tools package provides a suite of tools for estimation and analysis of a number of pre-packaged models. The internal Bayesian models provide quickly accessible, full-stage modeling including data generation, estimation, and post-estimation analysis. Modeling flexibility is provided through control structures for setting modeling parameters.
Installation¶
If you’re interested in purchasing BET Please contact us to request pricing and installation information.
If you already own BET , you can use the GAUSS Package Manager for quick download and installation..
Key Features¶
Data generation tools¶
Univariate and multivariate linear models
Autoregressive error terms (AR)
Hierarchical Bayes (HB)
Probit and logit data
Supported models for Markov Chain Monte Carlo (MCMC) Estimation¶
Univariate and multivariate linear models
Autoregressive error terms (AR)
Hierarchical Bayes (HB)
Probit model
Dynamic two-factor model
Structural vector autoregressive (SVAR)
Flexible MCMC estimation controls¶
Number of saved iterations
Skipped iterations
Burn-in iterations
Total number of iterations
Inclusion of intercept
Optional graph and results output
Elective maximum likelihood estimation (MLE) initialization
Comprehensive results¶
Draws for all parameters at each iteration
Posterior mean of parameters
Posterior standard deviation of parameters
Predicted variable values and residuals
Correlation matrix between observed and predicted data
PDF values and corresponding PDF graphs
Log-likelihood values (when applicable)