Applications deadline April 30th
Nonparametric Bayesian methods make use of infinite-dimensional mathematical structures to allow the practitioner to learn more from their data as the size of their data set grows. What does that mean, and how does it work in practice? In this tutorial, we'll cover why machine learning and statistics need more than just parametric Bayesian inference. We'll introduce such foundational nonparametric Bayesian models as the Dirichlet process and Chinese restaurant process and touch on the wide variety of models available in nonparametric Bayes. Along the way, we'll see what exactly nonparametric Bayesian methods are and what they accomplish.
Bayesian econometric methods are increasingly popular in empirical macroeconomics. In particular, flexible models that allow for non-Gaussian distributions and time variation in coefficients and volatility are now widely used among macroeconomists. The overarching purpose of this course is to bring you to the research
frontier so that you are prepared to do research in Bayesian macroeconometrics.
This course first provides an overview of Bayesian theory and computations. It then
gives a brief review of the linear regression and the Gibbs sampler. Some flexible variations of the linear regression will then be introduced, along with various more sophisticated MCMC algorithms. We will then dive into a few state-of-the-art macroeconometric
models, including unobserved components models, time-varying parameter models and
stochastic volatility models.
Course notes: The course notes are available at http://joshuachan.org/notes_BayesMacro.html
1. Overview of Bayesian econometrics: Bayesian theory and computations
2. Linear regressions: Gaussian and t errors, moving average errors, independence-chain Metropolis-Hastings, Griddy-Gibbs
3. Mixture models: scale mixture of normals, finite mixture of normals
4. Linear state space models: unobserved components models, time-varying parameter models, precision-based samplers
5. Nonlinear state space models: stochastic volatility model, stochastic volatility in mean, auxiliary mixture sampler
* Intermediate knowledge of econometrics
* Know what a prior, likelihood, and posterior are.
* Know how to use Bayes' Theorem to calculate a posterior for both discrete and continuous parametric distributions.
* Understand what a generative model is.
* Have a basic idea of what Gibbs sampling is and when it is useful (at least check out the Wikipedia article in advance).
The Module will be held in the Bank of Italy's Scuola di Automazione per Dirigenti Bancari (SADiBa), via San Marco n.54, Perugia. Participants will be accommodated at SADiBa.
Fees and Enrollment
- Students and university staff: 800€
- Others: 2300€
Fee includes full board accommodation starting from Sunday.
- For administrative issues : Alessandra Picariello phone: +39 0512092637; e-mail: firstname.lastname@example.org
- For more information : email@example.com