{"id":25814,"date":"2022-07-28T14:41:28","date_gmt":"2022-07-28T12:41:28","guid":{"rendered":"https:\/\/ritme.com\/swiss-stata-conference-2022-register-now\/"},"modified":"2023-07-07T13:48:36","modified_gmt":"2023-07-07T11:48:36","slug":"swiss-stata-conference-2022-jetzt-anmelden","status":"publish","type":"post","link":"https:\/\/ritme.com\/de\/swiss-stata-conference-2022-jetzt-anmelden\/","title":{"rendered":"Swiss STATA Conference 2022"},"content":{"rendered":"\n<p>Haben Sie als Stata-Benutzer F\u00e4higkeiten in <strong><a href=\"https:\/\/ritme.com\/de\/software\/stata\/\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">Stata 17<\/mark><\/a><\/strong> entwickelt, die Sie gerne weitergeben m\u00f6chten? Oder m\u00f6chten Sie andere Benutzer und StataCorp-Entwickler treffen?<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:30px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p class=\"has-text-align-center\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">Dann besuchen Sie uns an der Universit\u00e4t Bern an der Swiss STATA Conference 2022:<\/mark><\/strong><\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:35px\" aria-hidden=\"true\"><\/div>\n\n\n\n<a href=\"https:\/\/widget.weezevent.com\/ticket\/E872109\/?code=19354&amp;locale=en-GB&amp;width_auto=1&amp;color_primary=00AEEF\" onclick=\"var w=window.open('https:\/\/widget.weezevent.com\/ticket\/E872109\/?code=19354&amp;locale=en-GB&amp;width_auto=1&amp;color_primary=00AEEF', 'Billetterie_weezevent', 'width=650, height=600, top=100, left=100, toolbar=no, resizable=yes, scrollbars=yes, status=no'); w.focus(); return false;\" style=\"text-decoration: none;color: #FFFFFF;background: #88d0d8;padding:1em;border-radius:16px;text-align:center;display: block;width: 60%;margin-inline: auto;\">Anmeldung f\u00fcr die Swiss STATA 2022 Conference<\/a>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:55px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p>Diese Konferenz bietet Stata-Anwendern aus aller Welt die M\u00f6glichkeit, <strong>Ideen, Erfahrungen und Informationen<\/strong> \u00fcber neue Anwendungen der Software auszutauschen.<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:45px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading has-medium-font-size\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\"><i style=\"padding-right:10px;\" class=\"cbt-fa-icons fas fa-search\"><\/i>N\u00fctzliche Informationen<\/mark><\/h2>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:25px\" aria-hidden=\"true\"><\/div>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Datum<\/strong><span style=\"color: initial;\">: Freitag, November 18, 2022<\/span><\/li>\n\n\n\n<li><strong>Dauer<\/strong>: Ganzt\u00e4gig, von 9 Uhr bis 18:30 Uhr<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Standort<\/strong>: Universit\u00e4t Bern &#8211; 3012 BERN (SCHWEIZ)<br>Hallerstrasse 6, 2. OG | <a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/Plan-Hallerstrasse-6.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">Siehe die Karte<\/mark><\/a><\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Konferenz auf Englisch | <strong>Vor Ort <\/strong>oder <strong>online via Zoom<\/strong><\/li>\n<\/ul>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-fd643502 uagb-columns__columns-1 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-1\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-36d8e77e\"><div class=\"uagb-column__overlay\"><\/div>\n<ul class=\"wp-block-list\">\n<li><strong>Grundpreis<\/strong> <strong>(Vor Ort)<\/strong>: 75,00 CHF<\/li>\n\n\n\n<li><strong>Studentenpreis<\/strong> <strong>(Vor Ort)<\/strong>: 20,00 CHF<\/li>\n<\/ul>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Grundpreis<\/strong> <strong>(online)<\/strong> <mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">*<\/mark>: 50,00 CHF<\/li>\n\n\n\n<li><strong>Studentenpreis<\/strong> <strong>(online)<\/strong> <mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">*<\/mark>: 15,00 CHF<\/li>\n<\/ul>\n\n\n\n<p><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">*<\/mark> Der Link f\u00fcr die Zoom-Konferenz wird nach der Registrierung gesendet<\/em>.<\/p>\n<\/div>\n<\/div><\/section>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:40px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading has-medium-font-size\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\"><i style=\"padding-right:15px\" class=\"cbt-fa-icons fas fa-microphone-alt\"><\/i>Spannende Referenten aus der ganzen Welt!<\/mark><\/h2>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:20px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p>Vielen Dank an die folgenden fortgeschrittenen <strong>Stata-Benutzer<\/strong>, die aus der ganzen Welt kommen, um ihre besten Praktiken und Erfahrungen mit <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">Stata 17<\/mark><\/strong> zu teilen:<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:40px\" aria-hidden=\"true\"><\/div>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-3000cccc uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-d9487964\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong>Achim AHRENS<\/strong><br><\/strong>Senior Data Scientist &#8211; Swiss Federal Institute of Technology<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">Pystacked: Stacking generalization and machine learning in Stata<\/mark><\/strong><\/p>\n\n\n\n<p>Pystacked implements stacked generalization (Wolpert, 1992) for regression and binary classification via Python\u2019s scikit-learn. Stacking combines multiple supervised machine learners &#8211; the \u201cbase\u201d or \u201clevel-0\u201d learners &#8211; into a single learner. The currently supported base learners include regularized regression, random forest, gradient boosted trees, support vector machines, and feed-forward neural nets (multi-layer perceptron). pystacked can also be used with as a \u2018regular\u2019 machine learning program to fit a single base learner and, thus, provides an easy-to-use API for scikit-learn\u2019s machine learning algorithms.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Ahrens-Bern2022-pystacked.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:15px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">DDML: Double\/debiased machine learning in Stata<\/mark><\/strong><\/p>\n\n\n\n<p>We introduce the Stata package ddml which implements Double\/Debiased Machine Learning (DDML) for causal inference aided by supervised machine learning. Five different models are supported, allowing for multiple treatment variables in the presence of high-dimensional controls and\/or instrumental variables. ddml is compatible with many existing supervised machine learning programs in Stata.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Ahrens-Bern2022-ddml.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-7c23d695\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/eth-Zurich-1.png\" alt=\"\"\/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-42ac4776 uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-beea8936\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong>Steve K<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>A LOK WONG<strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><br><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>IHEID PhD Student, affiliated to Centre for Finance and Development &#8211; Graduate Institute Geneva<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">Stata-Python API for bulk data download: Example with UN Comtrade<\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/p>\n\n\n\n<p>This presentation aims to guide the audience through the bulk download of Comtrade data via a Stata-Python integration setup, which has been made available since Stata 16. Though this presentation is explicitly about the UN Comtrade dataset, the methodology employed is generalizable to other data platforms that allow API downloads.<br>The UN Comtrade Database is one of the best sources when it comes to bilateral trade data by product code. As of early 2022, it covers more country-year observations than the World Trade Organization and the International Trade Centre. However, tailoring the raw data to each researcher\u2019s needs is often time-consuming. Using the Comtrade API with my Stata-Python setup would allow researchers to tailor their downloaded data to their desired specification. In addition, employing this setup significantly reduces human error when compared to the manual downloading and cleaning of Comtrade data.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Wong-Bern2022-comtrade.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-b78d8281\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/Graduate-Institute-Geneva-500x500.png\" alt=\"Graduate Institute Geneva\" width=\"60\"\/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-5962e29a uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-33d3dea4\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong>Nicolai T. BORGEN<\/strong><br>Department of Special Needs Education &#8211; University of Oslo<\/p>\n\n\n\n<p><strong><strong>Andreas <\/strong>HAUPT<\/strong><br>Institute of Sociology, Media and Cultural Studies &#8211; Karlsruhe Institute of Technology<\/p>\n\n\n\n<p><strong><strong><strong>\u00d8yvind WIBORG<\/strong><\/strong><\/strong><br>Department of Sociology and Human Geography &#8211; University of Oslo<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong>Flexible and fast estimation of quantile treatment effects: The rqr and rqrplot commands<\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/p>\n\n\n\n<p>Using quantile regression models to estimate quantile treatment effects is becoming increasingly popular. This paper introduces the rqr command that can be used to estimate residualized quantile regression (RQR) coefficients and the rqrplot postestimation command that can be used to effortless plot the coefficients. The main advantages of the rqr command compared to other Stata commands that estimate (unconditional) quantile treatment effects are that it can include high-dimensional fixed effects and that it is considerably faster than the other commands.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Haupt-Bern2022-rqr.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-8f6875bc\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/Universite_dOslo_logo.svg\" alt=\"University of Oslo\" width=\"80\"\/><\/figure>\n<\/div>\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:25px\" aria-hidden=\"true\"><\/div>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/Logo_KIT.svg\" alt=\"Karlsruhe Institute of Technology\" width=\"80\"\/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-5f3b6eda uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-f5886167\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong><strong><strong><strong>Blaise MELLY<\/strong><br><\/strong><\/strong><\/strong><\/strong>Professor at the Department of Economics &#8211; University of Bern<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">Stata commands to estimate quantile regression with panel and grouped data<\/mark><\/strong><\/p>\n\n\n\n<p>In this presentation, we introduce two Stata commands that allow estimating quantile regression with panel and grouped data. The commands implement two-step minimum-distance estimators. We first compute a quantile regression within each unit and then apply GMM to the fitted values from the first stage. The command xtmdqr applies to classical panel data, where we follow the same units over time while the command mdqr applies to grouped data, where the observations are at the individual level, but the treatment varies at the group level. Depending on the variables assumed to be exogenous, this approach provides quantile analogs of the classical least squares panel data estimators such as the fixed effects, random effects, between, and Hausman-Taylor estimators. For grouped (instrumental) quantile regression, we provide a more precise estimator than the existing estimators. In our companion paper (Melly and Pons, \u201cMinimum Distance Estimation of Quantile Panel Data Models\u201d), we study the theoretical properties of these estimators.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Melly-Bern2022-mdqr.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-822d7f30\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/University-of-Bern.png\" alt=\"\"\/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-b5f050fe uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-690a0717\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong>Yiannis KARAVIAS<\/strong><\/strong><\/strong><br><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>Associate Professor &#8211; University of Birmingham<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">Improved Tests for Granger Non-Causality in Panel Data<\/mark><\/strong><\/p>\n\n\n\n<p>Granger causality is an important aspect of applied panel (longitudinal) data analysis as it can be used to determine whether one variable is useful in forecasting another. This presentation describes xtgranger, a community-contributed Stata command, which implements the panel Granger non-causality test of Juodis, Karavias, and Sarafidis (2021). This test offers superior size and power performance to existing tests, which stems from the use of a pooled estimator that has a faster convergence rate. The test has several other useful properties; it can be used in multivariate systems, it has power against both homogeneous as well as heterogeneous alternatives, and it allows for cross-section dependence and cross-section heteroskedasticity. The command is used to examine the type of temporal relation between profitability, cost efficiency and asset quality in the U.S. banking industry.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Karavias-Bern2022-xtgranger.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-54c89a11\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/University-Birmingham.png\" alt=\"\"\/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-bedc01cb uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-828df24c\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong>Kit BAUM<\/strong><br><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>Professor of Economics and Social Work &#8211; Boston College<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong>Drivers of COVID-19 deaths in the United States: A two-stage modeling approach<\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/p>\n\n\n\n<p>We offer a two-stage (time-series and cross-section) econometric modeling approach to examine the drivers behind the spread of COVID-19 deaths across counties in the United States. Our empirical strategy exploits the availability of two years (January 2020 through January 2022) of daily data on the number of confirmed deaths and cases of COVID-19 in the 3,000 U.S. counties of the 48 contiguous states and the District of Columbia. In the first stage of the analysis, we use daily time-series data on COVID-19 cases and deaths to fit mixed models of deaths against lagged confirmed cases for each county. Because the resulting coefficients are county specific, they relax the homogeneity assumption that is implicit when the analysis is performed using geographically aggregated cross-section units. In the second stage of the analysis, we assume that these county estimates are a function of economic and sociodemographic factors that are taken as fixed over the course of the pandemic. Here we employ the novel one-covariate-at-a-time variable-selection algorithm proposed by Chudik et al. (Econometrica, 2018) to guide the choice of regressors.<\/p>\n\n\n\n<p><strong>Coauthors<\/strong>: Andr\u00e9s Garcia-Suaza <em>(U. del Rosario)<\/em>, Miguel Henry <em>(Greylock McKinnon Associates)<\/em>, Jes\u00fas Otero <em>(U. del Rosario)<\/em><\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Baum-Bern2022-covid.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-1ef75bd2\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/Boston_College_seal.svg-1024x1024.png\" alt=\"Boston College (USA)\" class=\"wp-image-27309\" width=\"70\" srcset=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/Boston_College_seal.svg-1024x1024.png 1024w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/Boston_College_seal.svg-500x500.png 500w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/Boston_College_seal.svg-300x300.png 300w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/Boston_College_seal.svg-768x768.png 768w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/Boston_College_seal.svg.png 1200w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-3fa2b377 uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-901e9b45\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong>David&nbsp;SCHENCK<\/strong><br>Senior Econometrician &#8211; StataCorp<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">Bayesian Time Series in Stata 17<\/mark><\/strong><\/p>\n\n\n\n<p>Stata 17 introduced Bayesian support for several multivariate time-series commands. In this talk, I will discuss Bayesian vector autoregressive models and Bayesian DSGE models. Bayesian estimation is well suited to these models because economic considerations often impose structure that is captured well by informative priors. I will describe the main features of these commands, as well as Bayesian diagnostics, posterior hypothesis tests, predictions, impulse-response functions, and forecasts.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Schenck-Bern2022.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-38bc7868\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/StataCorp.png\" alt=\"\"\/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-fb2ab6d1 uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-7efb19d8\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong><strong>Jan DITZEN<\/strong><br><\/strong><\/strong>Assistant Professor &#8211; Free University of Bozen-Bolzano<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">Network regressions in Stata<\/mark><\/strong><\/p>\n\n\n\n<p>Network analysis has become critical to the study of social sciences. While several Stata programs are available for analysing network structures, programs that execute regression analysis with a network structure are currently lacking. We fill this gap by introducing the nwxtregress command. Building on spatial econometric methods (LeSage and Pace 2009), nwxtregress uses MCMC estimation to produce estimates of endogenous peer effects, as well as own-node (direct) and cross-node (indirect) partial effects, where nodes correspond to cross-sectional units of observation, such as firms, and edges correspond to the relations between nodes. Unlike existing spatial regression commands (for example, spxtregress), nwxtregress is designed to handle unbalanced panels of economic and social networks as in Grieser et al. (2021). Networks can be directed or undirected with weighted or unweighted edges, and they can be imported in a list format that does not require a shapefile or a Stata spatial weight matrix set by spmatrix. Finally, the command allows for the inclusion or exclusion of contextual effects. To improve speed, the command transforms the spatial weighting matrix into a sparse matrix. Future work will be targeted toward improving sparse matrix routines, as well as introducing a framework that allows for multiple networks.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Ditzen-Bern2022-nwxtregress.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-1f8d8b2b\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/Free_University_of_Bozen-Bolzano_logo.svg-1024x819.png\" alt=\"\" width=\"55\"\/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-dd5fa2cd uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-9c9c84d2\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong>Philippe VAN KERM<\/strong><\/strong><\/strong><\/strong><br><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>Professor in Social Inequality and Social Policy Analysis &#8211; University of Luxembourg<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">Exchangeably weighted bootstrap schemes<\/mark><\/strong><\/p>\n\n\n\n<p>The exchangeably weighted bootstrap is one of the many variants of bootstrap resampling schemes. Rather than directly drawing observations with replacement from the data, weighted bootstrap schemes generate vectors of replication weights to form bootstrap replications. Various ways to generate the replication weights can be adopted and some choices bring practical computational advantages. This talk demonstrates how easily such schemes can be implemented and where they are particularly useful, and introduces the exbsample command which facilitates their implementation.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/VanKerm-Ben2022-exbsample.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-da1bb25b\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/University-Luxembourg.png\" alt=\"\"\/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-2fb4c8ee uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-3e75a494\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong><strong><strong>Ben JANN<\/strong><br><\/strong><\/strong><\/strong>Professor at the Institute of Sociology &#8211; University of Bern<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">Marginal odds ratios: What they are, how to compute them, and why applied researchers might want to use them<\/mark><\/strong><\/p>\n\n\n\n<p>Logistic response models form the backbone of much applied quantitative resesarch in epidemiology and the social sciences. However, recent methodological research highlights difficulties in interpreting odds ratios, particularly in a multivariate modeling setting. These difficulties arise from the fact that coefficients from nonlinear probability models such as the logistic response model (i.e., log odds ratios) depend on model specification in ways that differ from the linear model. Applied researchers have responded to this situation by reporting marginal effects on the probability scale implied by the nonlinear probability model or obtained by the linear probability model.<br>Although marginal effects on the probability scale have many desirable properties, they do not align well with research in which relative inequality is a key concept. We argue that in many cases the odds ratio is preferable because it is a relative measure that does not depend on the marginal distribution of the dependent variable. In our talk, we aim to remedy the declining popularity of the odds ratio by introducing what we term the \u201cmarginal odds ratio\u201d; that is, logit coefficients that have similar properties as marginal effects on the probability scale, but which retain the odds ratio interpretation. We define the marginal odds ratio theoretically in terms of potential outcomes, both for binary and continuous treatments, we develop estimation methods using three different approaches (G-computation, inverse probability weighting, RIF regression), and we present examples that illustrate the usefulness and interpretation of the marginal odds ratio.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Jann-Bern2022-lnmor.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-3e1c3439\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/University-of-Bern.png\" alt=\"\"\/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-bebb0d20 uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-7049400d\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong>Maarten BUIS<\/strong><br><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>Lecturer of statistics for the social sciences at the department of sociology &#8211; University of Konstanz<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong>It is all about the data<\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/p>\n\n\n\n<p>This talk is a collection of tips for exploring a new dataset and preparing a dataset using both official and community contributed commands. Community contributed commands that will be covered are lany, lookfor2, htmlcb, and closedesc.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Buis-Bern2022.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-e90c32c3\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/UniKonstanz_Logo.svg\" alt=\"University of Konstanz\" class=\"wp-image-27426\" width=\"85\"\/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-ccc59a87 uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-7eb34d1f\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong><strong><strong>Lukas B\u00fctikofer<\/strong><br><\/strong><\/strong><\/strong>Statistician &#8211; University of Bern<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">btable: Extensive summary tables in Stata<\/mark><\/strong><\/p>\n\n\n\n<p>The construction of summary tables is a very common, repetitive and time-consuming step in data analysis. btable is a flexible, easy-to-use and powerful algorithm for generating such tables in Stata. It is freely available from github.<br><br>btable can summarize continuous, categorical, count and time-to-event variables within one table using various descriptive statistics, which can be individually chosen and combined for each variable. If the summary is grouped, effect measures with confidence intervals and p-values are added. User-defined effect measures and tests can be integrated.<br><br>The table is constructed in a two-step approach using two functions: btable produces an unformatted, raw table, which is then formatted by btable_format to produce a final, publication-ready table. By default, the raw table contains all descriptive statistics, and, if grouped, effect measures with confidence intervals and p-values. The formatting step allows for a variable-specific selection and formatting.<br><br>The two-step approach separates data analysis and formatting. The analysis step does not change the current dataset and the raw data table can be loaded, formatted by hand, or used for other purposes. The formatting step can be modified without re-running the analysis.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Buetikofer-Bern2022-btable.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-351c0b8d\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/07\/University-of-Bern.png\" alt=\"\"\/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-8b3540bc uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-779ecd9b\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong>Matthias SCHONLAU<\/strong><br><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>German Professor of statistics and actuarial science &#8211; University of Waterloo<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><strong>Visualizing categorical data with hammock plots<\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/strong><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/p>\n\n\n\n<p>Visualizing data with more than two variables is not straight forward, especially when some variables are categorical rather than continuous. My hammock plots are one option to visualize categorical data and mixed categorical \/ continuous data. Hammock plots can be viewed as a generalization of parallel coordinate plots where the lines are replaced by rectangles that are proportional to the number of observations they represent. I will introduce my Stata program for hammock plots and give several short examples where I have found them useful.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Schonlau-Bern2022-hammock.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-4b9fd4b0\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/University_of_Waterloo_seal.svg-1024x1024.png\" alt=\"University of Waterloo\" class=\"wp-image-27256\" width=\"70\" srcset=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/University_of_Waterloo_seal.svg-1024x1024.png 1024w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/University_of_Waterloo_seal.svg-500x500.png 500w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/University_of_Waterloo_seal.svg-300x300.png 300w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/University_of_Waterloo_seal.svg-768x768.png 768w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/University_of_Waterloo_seal.svg.png 1200w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<section class=\"wp-block-uagb-columns uagb-columns__wrap uagb-columns__background-none uagb-columns__stack-mobile uagb-columns__valign- uagb-columns__gap-10 align uagb-block-cedb90e8 uagb-columns__columns-2 uagb-columns__max_width-theme\"><div class=\"uagb-columns__overlay\"><\/div><div class=\"uagb-columns__inner-wrap uagb-columns__columns-2\">\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-d3753184\"><div class=\"uagb-column__overlay\"><\/div>\n<p><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong>Asjad N<\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>AQVI<strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><strong><br><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong><\/strong>Senior Economist &#8211; Austrian Institute for Economic Research (WIFO)<\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:5px\" aria-hidden=\"true\"><\/div>\n\n\n\n<p><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\"><strong><mark style=\"padding:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-green-color\">C<strong>irclebar<\/strong>: A Stata package for plotting circular bar graphs<\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/mark><\/strong><\/p>\n\n\n\n<p>The presentation will introduce \u201ccirclebar\u201d, a Stata package that allows users to visualize data as circular bar graphs organized in polar coordinates. The command allows for flexibility of selecting and changing bar dimensions including starting and ending circles, colors and label placements, and controlling spacing between the bars.<\/p>\n\n\n\n<p><i style=\"padding-right:7px;\" class=\"cbt-fa-icons far fa-file-alt has-ritme-blue-color\"><\/i> <span> <strong><a href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/11\/Naqvi-Bern2022-circlebar-spider.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">View the presentation<\/mark><\/a><\/strong><\/span> <\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-uagb-column uagb-column__wrap uagb-column__background-undefined uagb-block-aaf97628\"><div class=\"uagb-column__overlay\"><\/div><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/Austrian-Institute-for-Economic-Research-1024x218.jpg\" alt=\"\" class=\"wp-image-27225\" width=\"80\" srcset=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/Austrian-Institute-for-Economic-Research-1024x218.jpg 1024w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/Austrian-Institute-for-Economic-Research-500x106.jpg 500w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/Austrian-Institute-for-Economic-Research-768x164.jpg 768w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/Austrian-Institute-for-Economic-Research-1536x327.jpg 1536w, https:\/\/ritme.com\/wp-content\/uploads\/2022\/09\/Austrian-Institute-for-Economic-Research.jpg 1700w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/div><\/div>\n<\/div><\/section>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:30px\" aria-hidden=\"true\"><\/div>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-icon icon-eye\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/ritme.com\/wp-content\/uploads\/2022\/10\/Swiss-Stata-Conference-Program-2022.pdf\" target=\"_blank\" rel=\"noopener\">Consult the full program<\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:50px\" aria-hidden=\"true\"><\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-dots\"\/>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:35px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong><mark style=\"padding-left:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">Submissions<\/mark><\/strong><\/h3>\n\n\n\n<p>Please email the scientific committee if you are interested in giving a presentation.<br>Please include a short informative title and an abstract, and indicate whether you wish to be considered for a 10-minutes or 20-minutes presentation (both to be followed by 5 minutes for questions). Presenters will be asked to provide the organizers with electronic materials (a copy of the presentation and any programs or datasets, where applicable). If your presentation has multiple authors, please identify the presenter, for whom the conference registration fee will be waived.<\/p>\n\n\n\n<p><strong>Abstracts should be sent no later than October 1, 2022, to <a href=\"mailto:ben.jann@unibe.ch\"><mark style=\"padding-left:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">ben.jann@unibe.ch<\/mark><\/a><\/strong><\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:30px\" aria-hidden=\"true\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong><mark style=\"padding-left:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">Presentation guidelines<\/mark><\/strong><\/h3>\n\n\n\n<p>Presentation topics may include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>new community-contributed commands for model estimation, graphing, data management, results reporting, or other purposes;<\/li>\n\n\n\n<li>new approaches for using Stata together with other software;<\/li>\n\n\n\n<li>innovative use or evaluations of existing Stata commands;<\/li>\n\n\n\n<li>new analytic methods of particular relevance to Stata users;<\/li>\n\n\n\n<li>case studies of using Stata for applications in various disciplines;<\/li>\n\n\n\n<li>methods and resources for teaching statistics with Stata or for teaching the use of Stata.<\/li>\n<\/ul>\n\n\n\n<p>No level of sophistication is assumed for presenters or attendees.<\/p>\n\n\n\n<p><em><strong>Scientific committee<\/strong><\/em><br>&#8212;&#8212;&#8212;<\/p>\n\n\n\n<p>Ben JANN<br>University of Bern<br><a href=\"mailto:ben.jann@unibe.ch\"><mark style=\"padding-left:0px;background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ritme-blue-color\">ben.jann@unibe.ch<\/mark><\/a><\/p>\n\n\n\n<div class=\"wp-block-getwid-advanced-spacer\" style=\"height:50px\" aria-hidden=\"true\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Als offizieller Distributor von Stata freuen wir uns, am Freitag, 18. November 2022, die vierte Swiss STATA Conference an der Universit\u00e4t Bern zu organisieren und durchzuf\u00fchren. Diese Konferenz wird Stata-Anwendern aus der ganzen Welt die M\u00f6glichkeit geben, Ideen, Erfahrungen und Informationen \u00fcber neue Anwendungen der Software auszutauschen.<\/p>\n","protected":false},"author":3370,"featured_media":25500,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"_uag_custom_page_level_css":"","footnotes":""},"categories":[48,235,155,50],"tags":[],"class_list":["post-25814","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-evenements","category-veranstaltungen-de","category-events-en","category-eventi-it"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Swiss STATA Conference 2022 an der Universit\u00e4t Bern - RITME<\/title>\n<meta name=\"description\" content=\"F\u00fcr diese neue Ausgabe der Stata-Konferenz treffen wir uns in der Schweiz, an der Universit\u00e4t Bern am 18. 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