2018 Swiss Stata User Group Meeting

ZURICH – October 25th, 2018

ETH Zürich


Ritme, the official distributor of Stata, and Swiss Federal Institute of Technology in Zurich are excited to present the 2018 Swiss Stata User Group Meeting.

The meeting provides Stata users from across Switzerland and the world the opportunity to share experiences with the software and information on new commands. Anyone interested in Stata is welcome. The conference language will be English.

We wanted to offer a refreshing balance between lectures and practice, which is why you’ll find presentations so much as workshops during a whole day dedicated to Stata practices.

Our whole team is looking forward to meeting you and to overcome the success of the last edition!

The User Meeting will take place on Thursday October 25th .



  • Do you want to gain new perspectives on how to use Stata? 

  • Do you have insights that would enlighten colleagues in your field?

  • Or do you just want to meet fellow users and StataCorp developers?


Then join us and register soon, the number of attendees is restricted.

 The participation of early career scholars and postgraduate researchers is positively encouraged. Presentation topics include but are not limited to:

  • Discussions of user-written Stata commands;

  • Case studies of research or teaching using Stata;




The Stata User Meeting will be held from 9h00 until 17h00


Estimating long run effects in models with cross-sectional dependence using xtdcce2.

Author: Jan Ditzen, Centre for Energy Economics Research and Policy, Heriot-Watt University,


Using the Stata user written command xtdcce2, it is shown how to estimate long run coefficients in a dynamic panel with heterogeneous coefficients and common factors and a large number of observations over cross-sectional units and time periods...


Inference with Arbitrary Clustering

Author: Fabrizio Colella, Rafael Lalive, Seyhun Orcan Sakally, Mathias Thoenig, University of Lausanne


We develop an estimator for the variance-covariance matrix (VCV) of OLS and IV estimates that allows for arbitrary dependence between observational units. Arbitrary here refers to the fact that there are no restrictions in the way units could be correlated with each other in space and time: this estimator can account for indirect links in the cross-sectional dependence, time dependence, and alteratio...


Prediction, model selection, and causal inference with regularized regression. Introducing two Stata packages: LASSOPACK and PDSLASSO

Authors: ACHIM AHRENS, Economic and Social Research Institute


The field of machine learning is attracting increasing attention among social scientists and economists. At the same time, Stata offers only a limited set of machine learning tools to date. This presentation introduces two Stata packages, LASSOPACK and PDSLASSO, which implement regularized regression methods, including but not limited to the lasso, for Stata.


Estimating the average causal effect on an ordinal outcome of an endogenously assigned treatment from an endogenously selected sample



This talk discusses the average causal effect (ACE) of an endogenous binary treatment on an ordinal outcome when the sample is subject to endogenous selection.  It shows how to estimate the ACE using an extended regression model (ERM) command in Stata It illustrates how to do regression adjustment in Stata and discusses standard errors for sample-averaged treatment effects and population-averaged treatment effects.


Customizing Stata graphs made easy

Author: BEN JANN


The overall look of Stata's graphs is determined by so-called scheme files. Scheme files are system components, that is, they are part of the local Stata installation. In this talk I will argue that style settings deviating from default schemes should be part of the script producing the graphs rather than being kept in separate scheme files, and I will present software that supports such practice.



Three-hour workshop: "Solving the two-step estimation problem using GMM in Stata"


Many estimators in statistics, econometrics, and biostatistics are cast as multi-step estimators. Multi-step estimators produce consistent point estimates but the standard errors must be corrected. This problem is so common that it even emerges when estimating population averaged effects from a regression with powers or interactions. This talk introduces the solution of stacked moment equations which is a special case the generalized method of moments (GMM) it shows how to implement this solution using the gmm command in Stata.

This talk also includes an introduction to Monte Carlo simulations. In addition to describing the mechanics of running a Monte Carlo in Stata, the talk discusses how to use Monte Carlo simulations to illustrate a theoretical point.


  1. An introduction to generalized method of moments (GMM) estimators and to the gmmcommand in Stata
    1. The generalized method of moments
    2. The gmm command
    3. Estimating equations as GMM
  1. Solving the two-step estimation problem by casting multistep estimators as GMM estimators
    1. Regression adjustment
    2. Inverse-probability weighting
    3. Control function estimators
  1. Monte Carlo simulations in Stata
    1. Using Monte Carlo simulations to understand estimators and their properties


Please do not hesitate to contact us if you have any question at:

  • info@ritme.ch or +41 (0)21 711 15 20



20 CHF HT for students




Allister Loder - ETH Zürich
Henrik Becker - ETH Zürich
Basil Schmid - ETH Zürich
Ben Jann - University of Bern


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