{"id":1227,"date":"2023-09-01T11:15:24","date_gmt":"2023-09-01T14:15:24","guid":{"rendered":"https:\/\/www.ufrgs.br\/ppgest\/?page_id=1227"},"modified":"2023-09-04T14:31:27","modified_gmt":"2023-09-04T17:31:27","slug":"2022-2","status":"publish","type":"page","link":"https:\/\/www.ufrgs.br\/ppgest\/2022-2\/","title":{"rendered":"2022"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1227\" class=\"elementor elementor-1227\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-76fddb1e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"76fddb1e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-353a84cc\" data-id=\"353a84cc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f9e8a06 elementor-widget elementor-widget-text-editor\" data-id=\"f9e8a06\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.6.2 - 04-04-2022 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#818a91;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#818a91;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\">Semin\u00e1rios indicados com * cont\u00e9m suas grava\u00e7\u00f5es disponibilizadas<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">\n<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span class=\"collapseomatic \" id=\"id6ae4c74eadaf3\"  tabindex=\"0\" title=\"Semin\u00e1rio 07\/06\/2022.&nbsp; Prof Cleiton Guollo Taufemback&nbsp;&nbsp;- PPGEst UFRGS\"    >Semin\u00e1rio 07\/06\/2022.&nbsp; Prof Cleiton Guollo Taufemback&nbsp;&nbsp;&#8211; PPGEst UFRGS<\/span><div id=\"target-id6ae4c74eadaf3\" class=\"collapseomatic_content \"><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\">T\u00edtulo:<\/span>&nbsp;<span style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-weight: var( --e-global-typography-text-font-weight ); background-color: var(--ast-global-color-5); font-size: 1rem;\">Asymptotic behavior of temporal aggregation in mixed-frequency datasets<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span style=\"font-weight: bold;\">Resumo:<\/span>&nbsp;S<span style=\"color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; font-weight: var( --e-global-typography-text-font-weight ); background-color: var(--ast-global-color-5); font-size: 1rem;\">Herein, we present an unexplored issue with temporal aggregation; namely, when a model contains frequency-dependent coefficients, such as a distinct long- and short-term coefficient, temporal aggregation leads to inconsistent least squares estimators. Because the subsampled variable&#8217;s spectrum is equal to its folded original spectrum, the low-frequency variable may exhibit a mixture of distinct linear relations for a given frequency. We propose a new method to disentangle frequencies superposition based on band spectrum regression, thus avoiding the inconsistency problem. As a result, we can test for the presence of frequency-dependent coefficients. We use stationary and nonstationary linear semiparametric models to demonstrate our findings. Our Monte Carlo simulations show good finite sample size and power properties. Finally, we present an empirical application of our method using quarterly GDP and monthly indicators from the United States.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span class=\"collapseomatic \" id=\"id6ae4c74eadb27\"  tabindex=\"0\" title=\"Semin\u00e1rio 14\/06\/2022.&nbsp; Dr. Gabriel F.R Vasconcelos&nbsp; - BOCOM BBM\"    >Semin\u00e1rio 14\/06\/2022.&nbsp; Dr. Gabriel F.R Vasconcelos&nbsp; &#8211; BOCOM BBM<\/span><div id=\"target-id6ae4c74eadb27\" class=\"collapseomatic_content \"><\/p>\n<p><span style=\"font-size: 16px; font-style: normal; font-weight: bold;\">T\u00edtulo:<\/span>&nbsp;<span style=\"background-color: var(--ast-global-color-5);\">MANAGERS VERSUS MACHINES: DO ALGORITHMS REPLICATE HUMAN INTUITION IN CREDIT RATINGS?<\/span><\/p>\n<p><span style=\"font-size: 16px; font-style: normal; font-weight: bold;\">Resumo:<\/span>&nbsp;<span style=\"background-color: var(--ast-global-color-5);\">We use machine learning techniques to investigate whether it is possible to replicate the behavior of bank managers who assess the risk of commercial loans made by a large commercial US bank. Even though a typical bank already relies on an algorithmic scorecard process to evaluate risk, bank managers are given significant latitude in adjusting the risk score in order to account for other holistic factors based on their intuition and experience. We show that it is possible to find machine learning algorithms that can replicate the behavior of the bank managers. The input to the algorithms consists of a combination of standard financials and soft information available to bank managers as part of the typical loan review process. We also document the presence of significant heterogeneity in the adjustment process that can be traced to differences across managers and industries. Our results highlight the effectiveness of machine learning based analytic approaches to banking and the potential challenges to high-skill jobs in the financial sector.<span style=\"color: rgba(0, 0, 0, 0); font-family: var( --e-global-typography-text-font-family ), Sans-serif;\"><span style=\"font-size: 1rem; font-weight: var( --e-global-typography-text-font-weight );\">.<\/span><\/span><\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\">\n<\/p><p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><span class=\"collapseomatic \" id=\"id6ae4c74eadb3b\"  tabindex=\"0\" title=\"Semin\u00e1rio 21\/06\/2022.&nbsp;Prof. Dr. Luiz Max de Carvalho - FGV\"    >Semin\u00e1rio 21\/06\/2022.&nbsp;Prof. Dr. Luiz Max de Carvalho &#8211; FGV<\/span><div id=\"target-id6ae4c74eadb3b\" class=\"collapseomatic_content \"><\/p>\n<p><span style=\"font-size: 16px; font-style: normal; font-weight: bold;\">T\u00edtulo:<\/span>&nbsp;<span style=\"background-color: var(--ast-global-color-5);\">Evaluating Markov chain Monte Carlo for phylogenetics using exchangeable distributions<\/span><\/p>\n<p><span style=\"font-size: 16px; font-style: normal; font-weight: bold;\">Resumo:<\/span>&nbsp;<span style=\"background-color: var(--ast-global-color-5);\">Clades are a fundamental object in phylogenetics, being the building blocks of phylogenies.&nbsp;<\/span><span style=\"font-style: inherit; font-weight: inherit; color: var( --e-global-color-text ); font-family: var( --e-global-typography-text-font-family ), Sans-serif; background-color: var(--ast-global-color-5);\">Zhu et al. (2011) derive the joint distribution of clades under two important phylogenetic models, the Yule-Harding-Kingman (YHK) model and the proportional to distinguishable arrangements (PDA) model, which are known as exchangeable phylogenetic distributions (Aldous, 1996). In this talk we build on the results of Zhu et al. (2011) to study the correlation matrix between clade indicators in exchangeable distributions. We then show how to use these theoretical results for simultaneously assessing correctness and performance of phylogenetic MCMC samplers.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-weight: 400; font-style: normal; font-size: 16px;\"><span class=\"collapseomatic \" id=\"id6ae4c74eadb4b\"  tabindex=\"0\" title=\"Semin\u00e1rio 28\/06\/2022.&nbsp;&nbsp;Profa. Dra. Tha\u00eds Paiva Galletti - UFMG\"    >Semin\u00e1rio 28\/06\/2022.&nbsp;&nbsp;Profa. Dra. Tha\u00eds Paiva Galletti &#8211; UFMG<\/span><div id=\"target-id6ae4c74eadb4b\" class=\"collapseomatic_content \"><\/p>\n<p style=\"\"><span style=\"font-size: 16px; font-style: normal; font-weight: bold;\">T\u00edtulo:<\/span>&nbsp;<span style=\"background-color: var(--ast-global-color-5);\">CovidLP: um aplicativo para prever a evolu\u00e7\u00e3o da COVID-19 baseado nos dados<\/span><\/p>\n<p style=\"\"><span style=\"font-size: 16px; font-style: normal; font-weight: bold;\">Resumo:<\/span>&nbsp;<span style=\"background-color: var(--ast-global-color-5);\">Nesta palestra, pretendo apresentar o processo de desenvolvimento de um aplicativo Shiny para previs\u00e3o de curto e longo prazos para a pandemia da COVID-19. Essas previs\u00f5es s\u00e3o completamente baseadas nos dados observados diariamente de casos confirmados e mortes, e permitem estimar caracter\u00edsticas relevantes da pandemia, como pico e fim dos casos\/mortes e n\u00famero total de casos\/mortes. Toda a infer\u00eancia \u00e9 sumarizada em preditores pontuais, acompanhados dos respectivos intervalos de credibilidade para intervalos de curto e longo prazo. Desafios associados ao desenvolvimento de um sistema de larga escala para v\u00e1rios pa\u00edses e estados tamb\u00e9m ser\u00e3o descritos, al\u00e9m dos produtos resultantes como um pacote do R e o livro &#8220;Building a Platform for Data-Driven Pandemic Prediction&#8221; publicado em 2021. Esse projeto foi desenvolvido em conjunto com Dani Gamerman, Marcos Prates, Vinicius Mayrink, Guido Moreira e um grupo de alunos e ex-alunos dos Programas de P\u00f3s-Gradua\u00e7\u00e3o em Estat\u00edstica da UFMG e da UFRJ.<\/span><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><\/div><\/p>\n<p style=\"font-size: 16px; font-style: normal; font-weight: 400;\"><br><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Semin\u00e1rios indicados com * cont\u00e9m suas grava\u00e7\u00f5es disponibilizadas<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-global-header-display":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","footnotes":""},"_links":{"self":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/1227"}],"collection":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/comments?post=1227"}],"version-history":[{"count":14,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/1227\/revisions"}],"predecessor-version":[{"id":2109,"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/pages\/1227\/revisions\/2109"}],"wp:attachment":[{"href":"https:\/\/www.ufrgs.br\/ppgest\/wp-json\/wp\/v2\/media?parent=1227"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}