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Proceedings

10:15–11:15 Finite mixture models for linked survey and administrative data Abstract: Researchers use finite mixture models to analyze linked survey and administrative data on labor earnings (or similar variables), taking account of various types of measurement error in each data source.
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Different combinations of error-ridden and 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 postestimation commands for assessment of reliability, marginal effects, data simulation, and prediction of hybrid earnings variables that combine information from both data sources.

Contributor:
Fernando Rios-Avila
Bard College
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Additional information:
Germany22_Jenkins.pdf

Stephen P. Jenkins
The London School of Economics and Political Science
11:45–12:15 A mixture of ordered probit models with endogenous switching between two latent classes Abstract: Ordinal responses can be generated, in a time-series context, by different latent regimes or, in a cross-sectional context, by different unobserved classes of population.
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We introduce a new command, swopit, that fits a mixture of ordered probit models with either exogenous or endogenous switching between two latent classes (or regimes). Switching is endogenous if the unobservables in the class-assignment model are correlated with the unobservables in the outcome models. We provide a battery of postestimation commands, assess by Monte Carlo experiments the finite-sample performance of the maximum likelihood estimator of the parameters, probabilities and their standard errors (both the asymptotic and bootstrap ones), and apply the new command to model the policy interest rates.

Contributors:
Jochem Huismans
Universiteit van Amsterdam
Andrei Sirchenko
Universiteit Maastricht
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Additional information:
Germany22_Huismans1.pdf

Jan Willem Nijenhuis
Universiteit Twente
12:15–12:45 nwxtregress: Network regressions in Stata Abstract: Network analysis has become critical to the study of social sciences.
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While several Stata programs are available for analyzing 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.

Contributors:
William Grieser
Texas Christian University
Morad Zekhnini
Michigan State University
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Additional information:
Germany22_Ditzen.pdf

Jan Ditzen
Free University of Bozen-Bolzano
1:45–2:15 Visualizing categorical data with hammock plots Abstract: Visualizing data with more than two variables is not straightforward, especially when some variables are categorical rather than continuous.
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My hammock plots are one option to visualize categorical data and mixed categorical or 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.

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Additional information:
Germany22_Schonlau.pdf

Matthias Schonlau
University of Waterloo
2:15–2:45 Measuring associations and evaluating forecasts of categorical and discrete variables Abstract: This presentation 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
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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 also computes the class-specific measures for each class as well as their macro and weighted averages.

Contributors:
Jochem Huismans
Universiteit van Amsterdam
Andrei Sirchenko
Universiteit Maastricht
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Additional information:
Germany22_Nijenhuis2.pdf

Jan Willem Nijenhuis
Universiteit Twente
3:15–3:45 Difference-in-differences estimation using Stata Abstract: Difference-in-differences (DID) estimation has become a popular tool in the context of treatment-effects estimation and program evaluation.
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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.

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Additional information:
Germany22_Luedicke.pdf

Joerg Luedicke
StataCorp
3:45–4:30 Open panel discussion with Stata developers
StataCorp

Workshop: Taking a page from Git: Reproducible research and dynamic documents with Stata

Presenter

Sven Spieß

Date and time

9 June from 12:00 p.m. to 7:00 p.m. CEST

Description

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 regard 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 do n0t 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, you 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, you will dive into Stata’s facilities for creating dynamic documents to automatically reflect changes in our analyses and data.

Prerequisites:

  • Working knowledge of Stata.
  • Git installed on your system. Optionally, a text editor with support for version control (for example, VS Code).
  • Free GitHub account
  • Limited prior exposure to Markdown, HTML, & CSS is beneficial but not required.

Scientific committee

Alexander Schmidt-Catran
Goethe-Universität Frankfurt am Main
Christian Czymara
Goethe-Universität Frankfurt am Main
Johannes Giesecke
Humboldt-Universität zu Berlin
Ulrich Kohler
Universität Potsdam

Logistics organizer

The logistics organizer for the 2022 German Stata Conference is DPC Software GmbH, the official distributor of Stata in Germany, the Netherlands, Austria, the Czech Republic, and Hungary.

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