QuantEcon

Workshop on Bayesian Modeling with Jim Savage

Time: 1:15pm - 5pm

Date: June 19, 2017

Venue: Fred Gruen Seminar Room, Level 1, H.W. Arndt Building (Building 25A), Australian National University

Cost: Free

Registration: Please register here and bring along a personal laptop!

In this afternoon workshop, participants will be given an introduction to the Stan modeling language.

Stan is a flexible modeling language capable of performing efficient Bayesian inference on any model with a continuous parameter space for which we can evaluate a (log) likelihood. It implements a cutting-edge variety of Hamiltonian Monte Carlo, which will happily work with tens of thousands of parameters, and often produces reliable estimates with only a few hundred iterations. It is currently possible to call Stan from within R, Python, Julia, Mathematica, MATLAB, Stata, or at the command line.

The workshop:

  • 1.15 - 2:00 A brief introduction to Stan.
    • What it is, how programs are set up, how to call them.
    • Exploring model fits in Shinystan
  • 2:00 - 3:00 A modern statistical workflow. This workflow helps researchers iterate towards richer, higher quality models with less pain. We’ll use a simple time-varying-parameters AR model as the example.
  • 3:15 - 5:00 Working through a more complex model. We’ll deploy the workflow on a model that is known to be fairly hard to fit: aggregate random coefficient logit (AKA BLP).

Pre-requisites:

Participants should have R, R Studio and Stan installed (we will only use R to run and evaluate models; participants needn’t be proficient in R).

About the instructor:

Jim Savage is an applied modeler and Data Science Lead at frontier markets lender Lendable in New York City. Previously he was at the Grattan Institute, La Trobe University, and the Australian Treasury. With Andrew Gelman, Shoshana Vasserman and David Stephan, he is currently writing a book on Bayesian Econometrics in Stan.