<div dir="ltr"> 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. <a href="http://hedibert.org/current-teaching/#tab-BayesianEconometrics-2018" style="color:rgb(5,99,193);text-decoration:underline">http://hedibert.org/current-teaching/#tab-BayesianEconometrics-2018</a> 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 <div> </div><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"> </div></div> </div>