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The 2016 German Stata Users Group meeting was June 10, but you can still interact with the user community even after the meeting and learn more about the presentations shared.

Proceedings

Social network analysis using Stata
Abstract: Social network analysis is one of the most rapidly growing fields of the social sciences. Social network analysis focuses on the relationships that exist between individuals (or other units of analysis), such as friendship, advice, trust, or trade relationships. Network analysis is concerned with the visualization and analysis of network structures, as well as with the importance of networks for individuals' propensities to adopt different behaviors. Until now, such analyses have been possible to perform using specialized software for network analysis only. This tutorial introduces the nwcommands, a software suite with over 90 Stata commands for social network analysis. The software includes commands (and dialog boxes) for importing, exporting, loading, saving, handling, manipulating, replacing, generating, visualizing, and animating networks. It also includes commands for measuring various properties of the networks and the individual nodes and for detecting network patterns and measuring the similarity of different networks, as well as advanced statistical techniques for network analysis, including MR-QAP and ERGM.

Additional information
Germany16_Grund.pdf
Thomas Grund
University College Dublin
Nonparametric frontier analysis using Stata
Abstract: The package npf offers five new Stata commands that estimate and provide statistical inference in nonparametric frontier models. All commands are realized using fast plugins compiled from C codes. The first two commands, tenonradial and teradial, estimate data envelopment models where nonradial and radial technical efficiency measures are computed (Färe 1988; Färe and Lovell 1994; Färe et al. 1994). Technical efficiency measures are obtained by solving linear programming problems. The rest of the commands, teradialbc, nptestind, and nptestrts, give tools for making statistical inference regarding radial technical efficiency measures (Simar and Wilson 1998, 2000, 2002). We demonstrate the capabilities of the new commands using small and large samples. Finally, a small empirical study of productivity growth is performed.

Additional information
Germany16_Badunenko.pdf
Oleg Badunenko
University of Cologne
Bayesian data analysis using Stata
Abstract: Stata 14 provides a suite of commands for performing Bayesian analysis. Bayesian analysis is a statistical paradigm that answers research questions about unknown parameters using probability statements. For example, what is the probability that a person accused of a crime is guilty? What is the probability that there is a positive effect of schooling on wage? What is the probability that the odds ratio is between 0.3 and 0.5? And many more. In my presentation, I will describe Stata’s Bayesian suite of commands and demonstrate its use in various applications. performed.

Additional information
Germany16_Marchenko.pdf
Yulia Marchenko
StataCorp LP
Dynamic Stata help files using MarkDoc
Abstract: Software documentation is very time consuming but inevitable. Stata includes two programming languages for developing statistical packages and new commands. Nevertheless, the package documentation has been a manual process, especially for generating Stata help files in sthlp format, which requires writing the help file using SMCL markup language. A better approach to documenting packages and writing help files is provided by the MarkDoc package, if the documentation is written within the program. MarkDoc processes the documentation and not only generates Stata help files dynamically but also can use the same source to produce PDF, Microsoft Word, or HTML documentation. The presentation discusses the benefits of package documentation and its applications for front-end Stata users who program with Stata or Mata. performed.

Additional information
Germany16_Haghish.pdf
E. F. Haghish
Institute for Medical Biometry and Statistics (IMBI), University of Freiburg
texdoc 2.0: An update on creating LaTeX documents from within Stata
Abstract: At the 2009 meeting in Bonn, I presented a new Stata command called texdoc. The command allowed weaving Stata code into a LaTeX document, but its functionality and its usefulness for larger projects was limited. In the meantime, I heavily revised the texdoc command to simplify the workflow and improve support for complex documents. The command is now well suited, for example, to generate automatic documentation of data analyses or even to write an entire book. In this talk, I will present the new features of texdoc and provide examples of their application.

Additional information
Germany16_Jann.pdf
Germany16_Jann_example1.pdf
Germany16_Jann_example2.pdf
Ben Jann
University of Bern
Marginal effects in multiply imputed datasets
Abstract: Both the margins postestimation command, which obtains marginal effects or adjusted predictions, and the mi suit, which implements multiple imputation methods as a sophisticated way for dealing with missing data, are powerful features introduced in Stata 11 and further enhanced in subsequent releases. Unfortunately, it is not (yet) possible to combine their capabilities in official Stata. In this talk, I discuss an approach for estimating marginal effects in multiply imputed datasets, first suggested by Isabel Cañette and Yulia Marchenko on Statalist. Drawing on their original specific example, I briefly review the underlying theoretical considerations and demonstrate how the mimrgns command generalizes the outlined technique in a user-friendly way.

Additional information
Germany16_Klein.pdf
Daniel Klein
University of Kassel
The assessment of fit in the class of logistic regression models: A pathway out of the jungle of pseudo-R2s using Stata
Abstract: Since the early nineties, logistic regression for binary, ordinal, and nominal dependent variables has become widely spread in the social sciences. Nevertheless, there is no consensus on how to assess the fit of these models corresponding to practical significiance. A lot of pseudo coefficients of determination have been proposed but seldom used in applied research. Most of these pseudo-R2s follow the principle of the proportional reduction of error comparing the likelihood, log likelihood, or the precision of prediction with those of a baseline model including the constant only. Alternatively, McKelvey and Zavoina (1975) have proposed a different one estimating the proportion of explained variance of the underlying latent dependent variable. Summarizing the Monte Carlo studies of Hagle and Mitchell (1992), Veall and Zimmermann (1992,1994), and Windmeijer (1995), I show that the McKelvey and Zavoina pseudo-R2 is the best one to evaluate the fit of binary and ordinal logit and probit models. Applying the assumption of identical independent distributed errors, I also propose a generalization of the McKelvey and Zavoina pseudo-R2 to the multinomial logistic regression assessing the fit of each binary comparison simultaneously. The usefulness of this concept is demonstrated by applied data analysis of an election study with Stata using a user-written mzr2.ado file.

Additional information
Germany16_Langer.pdf
Wolfgang Langer
University of Halle-Wittenberg
Influence functions at work
Abstract: This presentation illustrates practical uses of influence functions (IF) in Stata. First (and most obviously), inspection of IFs helps detecting influential sample observations. I show how this can be done in practice and how similar this is to examining jackknife replicates. I illustrate how looking at the influence function helps one understand the underlying structure of the measures being estimated. Second, IFs make it easy to calculate (asymptotic) standard errors and confidence intervals for a wide range of statistics. I illustrate how this can be done in Stata with the total command to account for complex survey design easily. Third and finally, application of "recentered influence function (RIF) regression" has recently been advocated to approximate the impact of covariates on (unconditional) distribution statistics. I demonstrate this use of IFs in Stata and discuss interpretation of RIF regression model coefficients. Empirical applications pertain to income distribution analysis.

Additional information
Germany16_van Kerm.pdf
Phillipe van Kerm
Luxembourg Institute of Socio-Economic Research
Analysis of sequences using Stata, 2.0
Abstract: Sequences are entities built by a limited number of elements that are ordered in a specific way. A typical example is human DNA, where the elements adenine, cytosine, guanine, and thymine (the organic bases) are ordered into a sequence. Other sequences are words, which are built by letters that appear in a specific order, or careers of employers, which are built by specific job positions ordered along time. The user-written SQ-Ados, published in 2006, provide a number of tools to describe sequences and to measure the similiartiy between pairs of sequences. Since its first publication, the SQ-Ados have been continiously updated with new functionalties. This presentation gives an overview of existing programs and discusses the most recent additions, including functions for string comparisions, nearest neighbor identification, and a simple interface to the plugins for sequence analysis developed by Brandan Halpin.

Additional information
Germany16_Kohler.pdf
Ulrich Kohler
University of Potsdam

Workshop: Introduction to Mata

by Ulrich Kohler, University of Potsdam

Description

Mata is a full-fledged programming language that operates in the Stata environment. It is designed to make programming functions for matrices really easy. The workshop provides an introduction to basic Mata concepts and a step-by-step example of implementing an estimator for Stata with Mata. The workshop will cover the following:

  • First principles
  • Stata in Mata
  • Implementing an estimator
  • Common problems, proper solutions
  • Mata programming style

The workshop alternates between the lecturer's input and self-learning exercises.

Prerequisites

Previous experience with Mata is not required. However, participants should be fluent with Stata and should have experience with programming tools such as local macros (local) and loops (foreach, forvalues). They should also know in principle what an ado-file is. Reading chapter 12 of Kohler and Kreuter's Data Analysis Using Stata would provide excellent preparation for participants.

Lecturer

Dr. Ulrich Kohler is the Chair for Methods of Empirical Social Research at the University of Potsdam, Germany. He is coauthor of Data Analysis Using Stata and author of various user-written Stata commands.

Registration

Registration is closed.

Meeting fees Price
Meeting only: Professionals €45
Meeting only: Students €35
Workshop only €65
Workshop + meeting €85

Organizers

Scientific committee

Johannes Giesecke
Humboldt University of Berlin

Ulrich Kohler
University of Potsdam

Alexander Schmidt-Catran
University of Cologne

Alexander Jedinger
GESIS Cologne

Alexia Katsanidou
GESIS Cologne

Logistics organizer

The logistics organizer for the 2016 German Stata Users Group meeting is Dittrich & Partner Consulting GmbH, the distributor of Stata in Germany, the Netherlands, Austria, the Czech Republic, and Hungary.

For more information about Dittrich & Partner Consulting GmbH, visit their website.

View the proceedings of previous Stata Users Group meetings.


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