2022 German Stata Conference

June 10, 2022

The next German Stata Conference will take place this year on:

Friday, June 10, 2022 at:

Goethe-University Frankfurt am Main
Campus Westend – Casino Building
Room: Renate von Metzler-Saal (Cas 1.801)
60323 Frankfurt am Main


You expect exciting topics from various areas related to working with Stata software.


09:45 – 10:00 Registration

10:00 – 10:15 Welcome

10:15 – 11:15 Finite Mixture Models for Linked Survey and Administrative Data
Stephen P. Jenkins, Fernando Rios-Avila (The London School of Economics and Political Science; Bard College)

11:15 – 11:45 Coffee

11:45 – 12:15 Evaluating forecasts and measuring associations of categorical variables: The classify command
Jan Willem Nijenhuis (University of Twente),Jochem Huismans (University of Amsterdam) and Andrei Sirchenko (Maastricht University)

12:15 – 12:45 Network analysis using nwxtregress
Jan Ditzen, William Grieser, Morad Zekhnini (Free University of Bozen-Bolzano; Texas Christian University; Michigan State University)

12:45 – 13:45 Lunch

13:45 – 14:15 Visualizing categorical data with hammock plots
Matthias Schonlau (University of Waterloo)

14:15 – 14:45 Measuring the accuracy of the probabilistic predictions of discrete-choice models: The classify command
Jochem Huismans, Jan Willem Nijenhuis, Andrei Sirchenko (Maastricht University)

14:45 – 15:15 Coffee

15:15 – 15:45 Difference-in-differences estimation using Stata
Joerg Luedicke (StataCorp)

15:45 – 16:30 Wishes and grumbles

16:30 End of the meeting

Finite Mixture Models for Linked Survey and Administrative Data Stephen P. Jenkins, Fernando Rios-Avila (The London School of Economics and Political Science; Bard College)

Abstract: Estimation and Post-estimation Researchers use finite  mixture models to analyze  linked survey and administrative  data on labour earnings (or similar variables), taking account of various types of measurement error in each data source. Different combinations of error-ridden and/or error-free observations characterize latent classes. Latent class probabilities depend on the probabilities of the different types of error. We introduce a set of Stata commands to fit a general class of finite mixture models to fit to linked survey-administrative  data We also provide post-estimation commands for assessment of reliability, marginal effects, data simulation, and prediction of hybrid earnings variables that combine information from both data sources.

Evaluating forecasts and measuring associations of categorical variables: The classify command Jan Willem Nijenhuis (University of Twente),Jochem Huismans (University of Amsterdam) and Andrei Sirchenko (Maastricht University)

Abstract: This paper introduces a new Stata command classify that constructs a classification table and computes various measures of association between two categorical variables as well as diagnostic scores of the accuracy of probabilistic and deterministic forecasts of a categorical (binary and multiclass ordinal or nominal) variable. We compiled a comprehensive list of about 200 coefficients, along with the synonymy and bibliography associated with them. In addition to the general measures, the command computes also the class-specific ones for each class as well as their macro and weighted averages.

Network analysis using nwxtregress Jan Ditzen, William Grieser, Morad Zekhnini (Free University of Bozen-Bolzano; Texas Christian University; Michigan State University)

Abstract: 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.

Visualizing categorical data with hammock plots Matthias Schonlau (University of Waterloo)

Abstract: 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. Hammock plots also incorporate optional univariate descriptors such as category labels into the graph. I will introduce my Stata program for hammock plots and give examples.

Difference-in-differences estimation using Stata Joerg Luedicke (StataCorp)

Abstract: Difference-in-differences (DID) estimation has become a popular tool in the context of treatment-effects estimation and program evaluation.  In this presentation, I will show how to use Stata’s didregress and xtdidregress commands to estimate treatment effects with repeated cross-sectional as well as panel data. I will also discuss a variety of methods for calculating cluster-robust standard errors when the number of clusters is small. Finally, I will show how to use diagnostic tools for checking the parallel-trends assumption, which is an identifying assumption of DID.

Workshop “Taking a page from Git: Reproducible research & dynamic documents with Stata”

Date and Place Thursday, June 9, 2022
12:00 pm – 7:00 pm
Campus Westend, PEG-Building, Room PEG 2G111
Presenter Sven Spieß

Reproducibility has always been a hallmark of Stata. The popular version control system Git offers useful additions to the versioning features implemented in Stata with regards to keeping track of revisions of individual (do-)files over the course of evolving research projects. The advantages are even more substantive in “distributed” projects where collaborators don’t necessarily work on a common infrastructure. Leveraging Git in combination with the power of dynamic documents furthers your ability to easily present and disseminate your most recent findings.

In this workshop we will first learn the basics of working with the free and open source version control system Git in conjunction with Stata. After having Git up and running, we will dive into Stata’s facilities for creating dynamic documents to automatically reflect changes in our analyses and/or data.

• Working knowledge of Stata
• Git installed on your system ([https://git-scm.com/downloads](https://git-scm.com/downloads)); optionally: text editor with support for version control (e.g., VS Code, [https://code.visualstudio.com](https://code.visualstudio.com))
• Free GitHub account ([https://github.com/signup](https://github.com/signup))
• Limited prior exposure to Markdown, HTML, & CSS is beneficial but not required

Sven Spieß is a Stata consultant with DPC Software.


Preliminary – subject to change


The conference language will be English because of the international nature of the meeting and the participation of non-German guest speakers.

Scientific Organizers

Alexander Schmidt-Catran
Goethe-University Frankfurt am Main
Christian Czymara
Goethe-University Frankfurt am Main

Johannes Giesecke
Humboldt University Berlin


Ulrich Kohler
University of Potsdam


Logistics Organizer

DPC Software GmbH (dpc-software.de), the distributor of Stata in several countries, including Germany, the Netherlands, Austria, the Czech Republic, and Hungary.

You can enroll by contacting Natascha Hütter by email or by writing or phoning.

Natascha Hütter
DPC Software GmbH
Phone: +49-212-224716 -21
E-Mail: natascha.huetter@dpc-software.de

Registration for the 2022 German Stata Conference – June 10, 2022 (binding)

This 2022 German Stata Conference is optionally available as a digital or live event. Please register for one variant.

Registration fee

Included will be the lunch, coffee and soft drinks in the morning and afternoon break and also pens and books at the Live Event.

Meeting fees (all prices are incl. VAT)Price
Meeting only: Professionals44,99€
Meeting only: Students25€
Workshop only65€
Workshop only: Students50€
Workshop + Meeting85€
Workshop + Meeting: Students70€
Meeting DIGITAL (Free)0 €


  • Workshop - Only
  • Stata Conference - Only
  • Workshop and Stata Conference
(pay by yourself)

All fields marked with an asterisk (*) are mandatory.

A free cancellation is no longer possible 14 days after registration. In case of cancellation after the 14 day cancellation period 100% of the booked amount will be charged.


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All fields marked with an asterisk (*) are mandatory.

STEP 2: PAYMENT (Only for Live Event)

Pay by PayPal or Bank transfer

Pay by PayPal

PAY Stata Conference and/or Workshop

Pay by Bank transfer

Pay till 25. May 2012!

Konto Inhaber: DPC Software GmbH

Konto Nummer: 237 689 17
Bankleitzahl: 720 200 70
IBAN: DE26 7202 0070 0023 7689 17

Use as Usage: Stata Conference 2022


Natascha Hütter

Phone:+49-212-224716 -21

E-Mail: natascha.huetter@dpc-software.de