Introduction to Bayesian analysis using Stata

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Introduction to Bayesian analysis using Stata

Description

This course will provide a conceptual introduction to Bayesian analysis and introduce Stata's suite of commands for fitting models, performing MCMC diagnostics, and conducting model evaluation in the Bayesian framework.

Learn how to perform Bayesian analysis in Stata using the bayes prefix and the bayesmh command. The bayes prefix provides Bayesian support for over 50 estimation commands using either default or user-supplied priors. Fit an even wider variety of models in the Bayesian framework using the bayesmh command by providing the model formulation, likelihood function, and prior distributions for each model parameter.

Price: $1,395 Enroll now

We offer a 15% discount for group enrollments of three or more participants.

Course leader

Gustavo Sánchez is a Senior Econometrician and Director of the Technical Services department at StataCorp LLC. He has a master's degree in econometrics from Southampton University, UK, and he got his PhD in agricultural economics at Texas A&M University. Gustavo worked at the Central Bank of Venezuela, and he was a professor of econometrics at the Universidad Central de Venezuela.

Gustavo has been an instructor for a few time-series and panel-data courses using Stata. He taught a workshop on Bayesian analysis using Stata at the 2019 American Political Science Association (APSA), and he taught a course on Bayesian analysis using Stata at the 2020 ICSPR summer program. He has also given webinars on Bayesian analysis using Stata at a few Stata conferences. In October 2021, he gave a talk on Bayes VAR analysis using Stata at the Stata Mexican conference.

Course topics

Introduction to Bayesian analysis
- Motivating example
- What is Bayesian analysis?
- Prior and posterior distributions
- Advantages and disadvantages of Bayesian analysis
Bayesian analysis in Stata
- Stata's Bayesian suite of commands
- Fitting basic models in the Bayesian framework
- Point and interval estimation
- Monte Carlo standard error (MCSE)
- Posterior summaries
- Credible intervals
- Deviance information criterion (DIC)
- Bayes factors
- Sensitivity analysis to the choice of prior
Markov chain Monte Carlo (MCMC)
- What is MCMC?
- Why MCMC?
- Adaptive Metropolis–Hastings and Gibbs MCMC sampling
- Convergence of MCMC
- Efficiency of MCMC
- Multiple chains
Report results from a Bayesian analysis

Prerequisite

Basic knowledge of statistics and regression analysis and a working knowledge of Stata.

Next session

Course	Dates	Location	Cost	Enroll
Introduction to Bayesian analysis using Stata	14–17 May 2024	Web based Web-based training offers the same great content as our classroom training. And just like our classroom training, web-based training is designed to be interactive. Watch and listen from your computer as the instructor teaches; follow along in your printed notes; ask the instructor questions; and work examples using Stata. The differences are that you attend web-based training courses in the comfort of your own home or office and that courses are divided into three- to four-hour sessions that take place over consecutive days.	$1,395	Enroll

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Notes

Enrollment is limited. This course is offered in both classroom and web-based settings.

Classroom training courses are two-day courses that run from 8:30 a.m. to 4:30 p.m. each day. These courses take place at a training center where computers with Stata installed are provided. A continental breakfast, lunch, and an afternoon snack will also be provided; the breakfast is available before the course begins.

Web-based training courses are four-day courses that run for three and a half hours each day. You will be provided with a temporary Stata license to install on your computer, a printed copy of the course notes, and all the course datasets so that you can easily follow along. Learn more about how our web-based training courses work, watch a video demonstration, and find technical requirements for participating in this type of training.

Introduction to Bayesian analysis using Stata

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