[ABE-L] Seminário do Projeto Temático Séries Temporais, Ondaletas, Dados de Alta Dimensão e Aplicações (STODAD)

[Hedibert Lopes]

Sexta-feira 11 abr. 2025 ⋅ 15:00 – 16:00 (Horário Padrão de Brasília)
Link da reunião - meet.google.com/ubr-suyi-sdm
Link para o artigo no ArXiv: http://arxiv.org/abs/2503.13759
Link para os slides:
http://hedibert.org/wp-content/uploads/2025/03/Minnesota-BART-CIMAT.pdf

Title: Minnesota BART
Speaker:  Hedibert Freitas Lopes
Abstract:  Vector autoregression (VAR) models are crucial for forecasting
and analyzing macroeconomic variables, serving as a fundamental tool for
applied macroeconomists. Recent literature has explored nonparametric
approaches, such as Bayesian additive regression trees (BART), which allow
for flexibility without strong parametric assumptions; however, existing
frameworks like the one proposed by Huber and Rossini (2022) do not
adequately accommodate high-dimensional data or time dependency in the
prior construction. This study enhances the literature by extending
previous work to enable high-dimensional data analysis and variable
selection through a sparsity-inducing Dirichlet hyperprior, as in Linero
(2018) on the regression tree’s splitting probabilities, while also
proposing a prior that decrease the probability of splitting on variables
that are have higher lags than smaller lags, similar to the approach taken
by the Minnesota Prior. Empirical results show improvement compared to the
baseline BART prior structure and a BVAR.  Joint Pedro Lima, Carlos M.
Carvalho and Andrew Herren (UT Austin).

-- 
Hedibert Freitas Lopes, PhD
Professor of Statistics and Econometrics
Head of Data and Decision Sciences Unit
INSPER - Institute of Education and Research
Rua Quatá, 300 - São Paulo, SP 04546-042 Brazil
Phone: +55 11 4504-2343
www.hedibert.org
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