[ABE-L] Bayesian Statistical Learning in Econometrics

[Hedibert Lopes]

Boa tarde povo.



Gostaria de divulgar um curso em nivel de doutorado que lecionarei nas
proximas varias semanas aqui no Insper.  Detalhes abaixo.  Interessados
entrar em contato comigo para verificar viabilidade.


http://hedibert.org/current-teaching/#tab-BayesianEconometrics-2018



Obrigado e abracos,

Hedibert





Objective



The end of the course goal is to allow the student to critically decide
between a Bayesian, a frequentist or Bayesian-frequentist compromise when
facing real world problems in the fields of micro- and macro-econometrics
and finance, as well as in quantitative marketing, strategy and business
administration.  With this end in mind, we will visit well known Bayesian
issues, such as prior specification and model comparison and model
averaging, but also study regularization via Bayesian LASSO, Spike-and-Slab
and related schemes, “small n, large p” issues, Bayesian statistical
learning via additive regression trees, random forests, large-scale VAR and
(dynamic) factor models.



Course description



Basic ingredients: prior, posterior, and predictive distributions,
sequential Bayes, conjugate analysis, exchangeability, principles of data
reduction and decision theory.  Model criticism: Bayes factor, computing
marginal likelihoods, Savage-Dickey ratio, reversible jump MCMC, Bayesian
model averaging and deviance information criterion.  Modern computation via
(Markov chain) Monte Carlo methods: Monte Carlo integration,
sampling-importance resampling, Gibbs sampler, Metropolis-Hastings
algorithms.  Mixture models, Hierarchical models, Bayesian regularization,
Instrumental variables modeling, Large-scale (sparse) factor modeling,
Bayesian additive regression trees (BART) and related topics, Dynamic
models, Sequential Monte Carlo algorithms, Bayesian methods in
microeconometrics, macroeconometrics, marketing and finance.





Part I Bayesian ingredients

Inference: likelihood, prior, predictive and posterior distributions

Model criticism: Marginal likelihoods, Bayes factor, model averaging and
decision theory

Computation: An introduction (Markov chain and sequencial) Monte Carlo
methods



Part II Multivariate models

Large-scale vector autoregressive models

Factor models and other dimension reduction models

Time-varying high-dimensional covariance models



Part III Modern Bayesian statistical learning

Mixture models and the Dirichlet process: handling non-Gaussian models

Regularization: sparsity via shrinkage and variable selection

Large vector-autoregressive and factor models: combining sparsity and
parsimony

Classification and support vector machines

Regression trees and random forests

Latent Dirichlet allocation: Text as data, text mining
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