Short-courses and tutorials

24. CIRS Webinar – An Introduction to Time Series Analysis via Dynamic Linear Models – Recording available here 

December 13th, 2023, 12pm-1:30pm (Eastern Standard Time)

1) Dynamic models
Introduction to dynamic linear mode ls, computation aspects and forecasting
2) Normal Dynamic Linear regression
Rmarkdown-based HTML (notebook) with R code for simple samples
3) R package BSTS (Bayesian Structural Time Series)
Airlines data example
4) Stochastic volatility modeling

23. Modern Bayesian Econometrics, ESOBE 2021, UC3M, Madrid, August 2021 – Recording available here

• DAY 1: Basic Bayes + NDLM/SVM + particle filtering
  • Brief overview of key Bayesian ingredients and computation
  • Model selection/comparison: Bernoulli, logit/probit regression?  (Rmd file)
  • Bayes factor and Bayesian model averaging
  • Normal dynamic linear modeling (NDLM)
  • Stochastic volatility model (Petrobras example)
  • Particle filtering (Petrobras example)
• DAY 2: Research topics
  • Variable selection and regularization
  • The illusion of the illusion of sparsity
  • Dynamic sparsity in dynamic regressions
  • Dynamic ordering learning in multivariate forecasting

22. Tutorial on Topics of Bayesian Learning, Brazilian Meeting of Econometrics, December 2020 – Lecture 1 & Lecture 2

21. Workshop on Bayesian Statistics (Programa Avançado de Data Science, PADS), November 2020

20. Modern Bayesan Econometrics, De Nederlandsche Bank, Amsterdam, December 2019

19. Bayesian computation via MC methods: A brief introduction, VI Congreso Bayesiano de America Latina (VI COBAL), Lima, Peru, June 2019

18. Statistics, Data Science, Machine Learning, Big Data? II Workshop em Bioestatistica, UEM, December 2018

17. Computational Methods for Bayesian Inference, 33 FNE y 13 CLATSE, Guadalajara, Mexico, October 2018

16. Modern Bayesian Statistics in high-dimensional models, 13a Amostra de Estatistica, IME-USP, Sao Paulo, October 2018

• Part I: Statistics, data science, machine learning
• Part II: Bayesian inference and computation
• Part III: Exemplo 1: time-varying variance modeling
• Part III: Exemplo 2: spatially hierarchical factor modeling
• Part III: Exemplo 3: dynamic sparsity in dynamic regressions
• Part III: Exemplo 4: efficient Bayesian inference for factor SV models

15. A Brief review of Bayesian Econometrics and Time Series.  Faculdad Economia y Negocios, Universidad de Chile, Santiago, Chile, October 2017

14. Statistics, machine learning and modern data analysis: an overview, XIICONRE-3 Statistical Meeting , Sao Paulo, Brazil, October 2016

13. Bayesian Regularization, Tutorial, XVI Brazilian School of Time Series and Econometrics, Campos do Jordao, Brazil, August 2015

12. Simulation-based approaches to modern Bayesian econometrics, IME-USP, Sao Paulo, September 2012

11. Bayesian Methods for Empirical Macroeconomics, Banco Central do Brasil, Brasilia, June 2012

10. Bayesian Econometrics, Department of Economics, University of Pretoria, South Africa, December 2011

9. Monte Carlo Methods, INPE, Sao Jose dos Campos, August 2010

8. Monte Carlo Methods and Stochastic Volatility, Bocconi University, Milan, Italy, November 2009

7. Bayesian Statistics: Techniques and Implementation, INPE, Sao Jose dos Campos, September 2009

6. An Introduction to Particle Filters, XIII School of Time Series and Econometrics, Sao Carlos, Brazil, July 2009

5. MCMC and SMC in Dynamic and Volatility Models, Technical University of Catalonia, Barcelona, Spain, June 2009

4. Tutorial on Sequential Monte Carlo, SAMSI, Research Triangle Park, USA, September 2008

3. Tutorial on Modern Bayesian Econometrics, IX Brazilian Meeting of Bayesian Statistics, Maresias, Brazil, February 2008

2. MCMC Methods for Latent Variable Models, International Meeting of the Psychometric Society, Tokyo, Japan, July 2007

1. Bayesian Econometrics, Institute of Advanced Studies, Vienna, Austria, February 2006