{"id":47898,"date":"2026-10-23T07:56:50","date_gmt":"2026-10-23T07:56:50","guid":{"rendered":"https:\/\/dpc-software.de\/?page_id=47898"},"modified":"2026-11-02T09:12:57","modified_gmt":"2026-11-02T09:12:57","slug":"german-stata-conference-2025","status":"publish","type":"page","link":"https:\/\/dpc-software.de\/en\/german-stata-conference-2025\/","title":{"rendered":"German Stata Conference 2025"},"content":{"rendered":"\t\t
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German Stata Conference 2025<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Report German Stata Conference 2025<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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On March 27\u201328, 2025, the DPC Software Gmbh hosted the 22nd German Stata Conference at the University of Hamburg\u00a0\u2013 an inspiring event for everyone passionate about Stata, statistical methods, and data-driven research. Participants from academia, industry, and public institutions came together to share ideas, learn from each other, and explore new developments in data analysis.\u00a0<\/p>

Workshop: Writing, Analyzing, and Presenting \u2013 Seamlessly Connected<\/strong>
The conference began with a full-day workshop titled \u201eInteraction between Text Writing and Statistical Analysis: Result Export and Dynamic Documents with Stata\u201c<\/em>, led by Dr. Christian Brzinsky-Fay (University of Hamburg). More than 20 participants joined the session, diving deep into the creation of dynamic documents in Stata. The focus was on integrating statistical analysis, text writing, and result documentation into one continuous workflow \u2014 a topic highly relevant for both academic research and practical applications. The workshop offered a productive learning environment, lively discussions, and many practical \u201eaha\u201c moments that participants will surely carry into their future work.\u00a0<\/p>

Conference Day: Inspiring Insights into the Future of Data Analysis<\/strong>
On the second day, the conference opened its doors to a broader audience with a diverse program featuring insightful talks from research and practice. A particular highlight was the keynote by Prof. Dr. Frauke Kreuter, a renowned expert in statistics and data science, on the topic \u201eCo-Creating with AI: The Role of LLMs as Intelligent Data Science Agents.\u201c<\/em> Prof. Kreuter explored the growing role of Large Language Models (LLMs) in data-driven research, illustrating how AI can increasingly act as a co-author and analysis partner. Her presentation resonated strongly with the audience, sparking lively discussions about the evolving collaboration between humans, machines, and methodologies.\u00a0<\/p>

In addition to the keynote, numerous other contributions enriched the conference program, covering a wide range of topics \u2014 from methodological innovations to practical tips for working with Stata. This diversity made for a truly dynamic and engaging conference experience.\u00a0<\/p>

The 22nd German Stata Conference 2025 was a great success \u2014 marked by intellectual depth, vibrant exchanges, and the strong community spirit that defines the Stata user base. Special thanks go to the University of Hamburg for their excellent organization and warm hospitality. We are already looking forward to next year\u2019s conference!\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Keynote Speakers<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Professor Frauke Kreuter\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Professor Frauke Kreuter is the Professor of Statistics and Data Science in Social Sciences and the Humanities at the Ludwig-Maximilians-University of Munich, Germany; Co-director of the Social Data Science Center (SoDa), and a faculty member in the Joint Program in Survey Methodology (JPSM) at the University of Maryland, USA.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Di Liu<\/span><\/span><\/p><\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Di Liu is a Principal Econometrician in the econometric development team at StataCorp LLC. He is the primary developer of some Stata features, including heterogeneous DID, instrumental variable quantile regression, treatment effects estimation using lasso, lasso for prediction, lasso for inference, spatial autoregressive models, heckpoisson, and betareg. He also published research articles in Canadian Journal of Economics, Econometrics Reviews, Empirical Economics, Econometrics and Statistics, and the Stata Journal. Di has a PhD degree in economics from Concordia University in Montreal, Canada; an engineer\u2019s degree in software engineering and statistics from Polytech\u2019Lille in Lille, France; and master\u2019s and bachelor\u2019s degrees in computer science from Hohai University in Nanjing, China.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Workshop: Interaction between Text Writing and Statistical Analysis: \nResult Export and Dynamic Documents with Stata<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The workshop deals with the exchange of results between Stata and a Word processing program. In the first part, I will demonstrate the different options to customize and export tables from Stata into MS Word. We will learn how to create single tables using Stata\u2019s dtable and etable commands, and we will proceed to the more sophisticated use of the collect suite that is available since Stata 17.<\/p>

In the second part, we will learn how to create dynamic or automated documents. These are documents containing particular commands (tags) that integrate up-to-date results (graphs, tables) into text documents, which avoids repeated and annoying copy-and-paste actions between Stata and MS Word. Using the dyndoc command, HTML or docx-files can be created. I will also explain how to customize Word or Excel files by using putdocx and putexcel.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Presenter\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Dr. Christian Brzinsky-Fay\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Dr Christian Brzinsky-Fay has been working for DPC Software since March 2022 and puts his heart and soul into running Stata training courses. His view of the world was largely shaped by Monty Python. He studied Political Science at the Free University of Berlin and holds a PhD in Social Policy from the University of Tampere (Finland). As a quantitative social researcher, he has been using Stata for statistical analyses for 25 years and teaches statistics and empirical methods of social research at the University of Hamburg.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Lectures<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Frauke Kreuter (LMU M\u00fcnchen)<\/strong><\/p>

Abstract:<\/em> As AI advances, large language models (LLMs) are shifting from passive tools to active agents that collaborate with experts to co-create knowledge and artifacts. In this talk, we explore the role of LLMs as intelligent agents in data science workflows \u2013 partners that not only automate tasks but also enhance decision-making by understanding core data science principles, identifying cognitive biases, and nudging experts toward more robust conclusions.<\/p>

We discuss how an LLM, equipped with statistical reasoning, ethical AI considerations, and an awareness of human cognitive pitfalls, can challenge assumptions, suggest alternative methodologies, and improve model interpretability. From guiding feature selection to questioning spurious correlations, these AI agents act as reflective collaborators rather than mere calculators.<\/p>

We will examine case studies where LLMs have meaningfully influenced analytical processes, highlight challenges in aligning AI nudges with human intent, and explore the future of AI-augmented data science, generally and while using Stata.<\/p>

This talk is primarily conceptual and designed to inspire but also to rethink our relationship with AI \u2013 not as a tool, but as a co-creator in the pursuit of knowledge.<\/p>

Download Presentation<\/a><\/strong><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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Daniel Kr\u00e4hmer (LMU Munich, Department for Sociology)<\/strong><\/p>

Abstract:<\/em> Multiverse analysis is becoming an important tool in the methodological repertoire of social scientists. The idea behind the method\u2014variously referred to as \u201cmultiverse analysis,\u201d \u201cmultimodel analysis,\u201d \u201cspeciAfcation curve analysis,\u201d or \u201cvibration of efects\u201d\u2014is straightforward: since there are many credi- ble ways of formulating an analysis, and any single statistical estimate may sufer from selective reporting, multiverse analysis explores all reasonable speciAfcations, contrasting authors\u2019 preferred estimate with a range of possible estimates. Instead of luring readers into a dark corner of the \u201cgarden of forking paths,\u201d multiverse analysis provides a bird\u2019s-eye view of the maze of researcher decisions and the resulting range of defensible findings. While multiverse analysis holds significant promise for quantitative empirical research, it poses conceptual, computational, and practical challenges. This talk provides a primer on implementing multiverse analysis in Stata. It highlights the strengths and limitations of existing multi- verse tools (e.g., mrobust, multivrs) and introduces a new plot type designed to visualize multiverse results effectively. By addressing key challenges in conducting and visualizing multiverse results, the talk seeks to encourage Stata users to adopt multiverse analysis and unlock its potential for robust and transparent research.<\/p>

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Daniel Klein (DZHW) and Felix Bittman (LIFBI)<\/strong><\/p>

Abstract:<\/em> Researchers often want to mitigate the increased risk of type I errors that arises from multiple pairwise comparisons of means. Stata provides seven methods to adjust the corresponding confidence intervals and p-values. However, four of these methods assume equal sample sizes, variances, or both, and none explicitly addresses unequal variances, which might pose limitations on applied research. In this presentation, we briefly review how the implemented methods modify the significance level or obtain critical values from alternative distributions to adjust for multiple comparisons. We then discuss three methods that explicitly account for unequal variances by making additional adjustments to standard errors and degrees of freedom. Finally, we (re-)introduce the pwmc command in Stata, which implements these three methods, and compare their performance using a Monte Carlo simulation.<\/p>

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Ulrich Kohler (University of Potsdam, Faculty for Economic and Social Sciences)<\/strong><\/p>

Abstract:<\/em> This talk presents an egen-function to convert units of measurements for length, areas, volumes, angles, masses, temperatures and currency. The function allows both, to convert many non-SI units (e.g., inch, furlong, sunradius) to SI-units (from pico to peta) or directly from a non-SI unit to another non-SI unit. Currencies are converted by calling the European Central Bank through an API. The conversion rate can be selected on a daily base or by an average of a specified period. German users may be relieved to realize that the function allows converting areas also into units of \u201cSaarland\u201d.<\/p>

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Di Liu (StataCorp)<\/strong><\/p>

Abstract:<\/em> Stata 18 introduced two commands (each with four estimators) to fit heterogeneous DID models: hdidregress for repeated cross-sectional data and xthdidregress for panel\/longitudinal data. In this talk, we briefly introduce the theory behind both estimators and then show how to fit heterogeneous DID models using the new commands. We also demonstrate postestimation tools to\u00a0aggregate and visualize heterogeneous treatment effects and perform diagnostic tests.<\/p>

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Raoul Dittrich (DPC Software GmbH)<\/strong><\/p>

Abstract:<\/em> The Stata license model is gradually changing from perpetual licenses to a pay-as-you-go model called StataNow. Whilst this gives researchers and users of Stata the advantage of always having access to the latest features of the software, the pay-as-you-go model requires different planning and budgeting for software. Many different options to license Stata exist, depending on edition, usage, organisation, and many other factors. This talk makes suggestions for finding a license option that meets the functional requirements, including multi-year models and covering of EUR\/USD fluctuations.<\/p>

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Ben Jann (University of Bern)<\/strong><\/p>

Abstract:<\/em> In 2008, I published the Stata command -oaxaca-, which implements the popular Oaxaca-Blinder (OB) decomposition technique. This technique is used to analyze differences in outcomes between groups, such as the wage gap by gender or race. Over the years, both the functionality of Stata and the literature on decomposition methods have evolved, so that an update of the -oaxaca- command is now long overdue. In this talk I will present a revised version of -oaxaca- that uses modern Stata features such as factor-variable notation and supports additional decomposition variants that have been proposed in the literature (e.g., reweighted decompositions or decompositions based on recentered infuence functions).<\/p>

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Johannes Giesecke\n\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Humboldt University Berlin\n\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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johannes.giesecke@hu-berlin.de<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Ulrich Kohler\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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University of Potsdam\n\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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ulrich.kohler@uni-potsdam.de<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Christian Brzinsky-Fay<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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University of Hamburg\n\n<\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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christian.brzinsky-fay@dpc-software.de<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

German Stata Conference 2025 Report German Stata Conference 2025 On March 27\u201328, 2025, the DPC Software Gmbh hosted the 22nd German Stata Conference at the University of Hamburg\u00a0\u2013 an inspiring event for everyone passionate about Stata, statistical methods, and data-driven research. Participants from academia, industry, and public institutions came together to share ideas, learn from…<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-47898","page","type-page","status-publish","hentry"],"yoast_head":"\nGerman Stata Conference 2025 | DPC Software GmbH<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dpc-software.de\/en\/german-stata-conference-2025\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"German Stata Conference 2025 | DPC Software GmbH\" \/>\n<meta property=\"og:description\" content=\"German Stata Conference 2025 Report German Stata Conference 2025 On March 27\u201328, 2025, the DPC Software Gmbh hosted the 22nd German Stata Conference at the University of Hamburg\u00a0\u2013 an inspiring event for everyone passionate about Stata, statistical methods, and data-driven research. 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