<font face="Default Sans Serif,Verdana,Arial,Helvetica,sans-serif" size="2"><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;">Bom dia a todos!</div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;"><br></div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;">Gostaria de convida-los para o Seminario do DEST na proxima sexta-Feira,<b>16 de maio, as 13h30</b>.</div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;">Este seminario sera proferido pelo Prof. <b>Hedibert Freitas Lopes</b> (INSPER). Hedibert eh professor</div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;">titula no INSPER e tem dado importantes contribuições em diferentes áreas da Estatistica. Voces </div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;">podem descobrir mais sobre o Hedibert em https://hedibert.org/about-me/.</div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;"><br></div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;">O seminario sera de forma virtual, pela plataforma zoom no seguinte link</div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;"><br></div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;"><div><font face="Verdana, Arial, Helvetica, sans-serif"><a href="https://us06web.zoom.us/j/89585065330?pwd=3SB50k20KtqwQGynDDAGhJoGrjcr7k.1" target="_blank" style="cursor: pointer;">https://us06web.zoom.us/j/89585065330?pwd=3SB50k20KtqwQGynDDAGhJoGrjcr7k.1</a></font></div><div><font face="Verdana, Arial, Helvetica, sans-serif"><br></font></div><div><font face="Verdana, Arial, Helvetica, sans-serif">ID da reunião: 895 8506 5330</font></div><div><font face="Verdana, Arial, Helvetica, sans-serif">Senha: 038165</font></div></div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;"><br></div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;">Maiores informacoes sobre o seminario estao abaixo.</div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;"><br></div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;">otimo dia e otimo fim de semana a todos</div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;">Rosangela</div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;"><br></div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;"><br></div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;">PS: Titulo e resumo</div><div style="font-family: "Default Sans Serif", Verdana, Arial, Helvetica, sans-serif; font-size: small;"><p class="MsoNormal"><b><span style="font-family: Bahnschrift, sans-serif;">Minnesota BART<o:p></o:p></span></b></p><p class="MsoNormal"><b><span style="font-family: Bahnschrift, sans-serif;">Hedibert F. Lopes</span></b></p><p class="MsoNormal">Joint with:<b> </b>Pedro Lima<b>, </b>Carlos M. Carvalho<b>, </b>Andrew Herren</p><p class="MsoNormal" style="text-align: justify;">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.</p><p class="MsoNormal"><o:p> </o:p></p><div><o:p><br></o:p></div></div><div></div></font>