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st: 2012 German Stata Users Group Meeting

From   Ulrich Kohler <>
To   statalist <>
Subject   st: 2012 German Stata Users Group Meeting
Date   Wed, 18 Apr 2012 12:35:09 +0200

2012 German Stata Users Group Meeting

Announcement and Program

Date: Friday, 1 2012, 8:45 - 18:45

Wissenschaftszentrum Berlin 
Reichpietschufer 50
10785 Berlin

Meeting only (45 €, students 25€); 
Workshop on May 31 (65 €);
Workshop and Meeting (85 €)


Description: The 10th German Stata Users Group Meeting will be held on
Friday, 1st June 2012 in Berlin at the WZB (Wissenschaftszentrum
Berlin für Sozialforschung). We would like to invite everybody from
everywhere who is interested in using Stata to attend this meeting.

The academic program of the meeting is being organized by Johannes
Giesecke ( and Ulrich Kohler
( The conference language will be English due to the
international nature of the meeting and the participation of
non-German guest speakers.

The logistics of the conference are being organized by Dittrich und
Partner, distributor of Stata in several countries including Germany,
The Netherlands, Austria, Czech Republic, and Hungary

On the day before the conference, there will be a workshop on “Applied
Data Management Using Stata". The workshop will be held at the German
Institute for Economic Research (DIW Berlin). For further information
see and description

Program Schedule

8:45 - 09:00 Registration

09:00 - 09:15 Welcome

09:15 - 10:15 Handling interactions in Stata, especially with
continuous predictors

Patrick Royston (University College London) and Willi Sauerbrei
(University of Freiburg), 

Abstract: In an era in which doctors and patients aspire to
personalized medicine, detecting and modeling interactions between
covariates or between covariates and treatment is becoming
increasingly important. In observational studies, for example, in
epidemiology, interactions are known as effect modifiers; their
presence can substantially change the understanding of how a risk
factor impacts the outcome. However, modeling interactions in an
appropriate and interpretable way is not straightforward.  In our
talk, we consider three main topics. The first topic is the nuts and
bolts of factor variables and interactions in Stata. We outline how
Stata's parameterizations of interactions between factor variables
work in regression models. The second topic is modeling interactions
in observational studies that involve at least one continuous
covariate, an area that practitioners apparently find difficult. We
introduce a new Stata program, mfpigen, for detecting and modeling
such interactions using fractional polynomials, adjusting for
confounders if necessary. The third topic is modeling interactions
between treatment and continuous covariates in randomized controlled
trials. We outline a Stata program, mfpi, designed for this
purpose. Key themes of our talk are the vital role played by graphical
displays of interactions and the importance of applying simple
plausibility checks.

10:15 - 11:15 Exploratory Spatial Data Analysis using Stata

Maurizio Pisati (University of Milano–Bicoccai),

Abstract: In this talk, I will present the basic principles of
exploratory spatial data analysis and their application using
Stata. After a brief discussion of the specific features of spatial
data, I will show some freely-available user-written Stata commands
(spmap, spgrid, spkde, spatwmat, spatgsa, spatcorr, spatlsa) that help
to carry out some exploratory analyses of real-world spatial data.

11:15 - 11:30 Coffee

11:30 - 12:00 leebounds: Lee’s Treatment Effect Bounds
for Samples with Non-Random Sample Selection

Harald Tauchmann (Rheinisch-Westfälisches Institut für

Abstract: Even if assignment of treatment is purely exogenous,
estimating treatment effects may suffer from severe bias if the
available sample is subject to nonrandom sample
selection/attrition. Lee (2009) addresses this issue by proposing an
estimator for treatment effect bounds in the presence of nonrandom
sample selection. In this approach, the lower and upper bound,
respectively, correspond to extreme assumptions about the missing
information that are consistent with the observed data. As opposed to
conventional parametric approaches to correcting for sample selection
bias, such as the classical heckit estimator, Lee bounds rest on very
few assumptions, namely, random assignment of treatment and
monotonicity. The latter means that treatment affects selection for
any individual in the same direction. I introduce the new Stata
command leebounds, which implements Lee’s bounds estimator in
Stata. The command allows for several options, such as tightening
bounds by the use of covariates, confidence intervals for the
treatment effect, and statistical inference based on a weighted
bootstrap. The command is applied to data gathered from a randomized
trial of the effect of financial incentives on weight-loss among obese
individuals.  Reference: Lee, David S. 2009. Training, wages, and
sample selection: Estimating sharp bounds on treatment effects. Review
of Economic Studies 76: 1071–1102.

12:00 - 12:30 Comparing observed and theoretical distributions

Maarten Buis (University of Tübingen) 

Abstract: In this talk, I aim to discuss tools to compare the observed
distribution of a variable with the theoretical distribution assumed
by a model. In particular, I will focus on the situation where a model
assumes a certain distribution for the explained/dependent/y variable
and one or more parameters of this distribution, often the mean,
change when one or more explanatory/independent/x variables
change. The challenge is that the dependent variable no longer follows
the theoretical distribution, but rather follows a mixture of these
theoretical distributions. In the case of a linear regression, we can
circumvent this difficulty by looking at the residuals, which should
follow a normal distribution. However, this circumvention does not
generalize to other models. I will show the margdistfit package, which
graphically compares the distribution of the dependent variable with
the theoretical mixture distribution.

12:30 - 13:30 Lunch

13:30 - 14:00 A simple alternative to the linear probability model for
binary choice models with endogenous regressors

Christopher F. Baum (Boston College & DIW Berlin),
Yingying Dong (University of California Irvine),
Arthur Lewbel (Boston College), Tao Yang (Boston

Abstract: Dong and Lewbel have developed the theory of simple
estimators for binary choice models with endogenous or mismeasured
regressors, depending on a “special regressor" as defined by Lewbel
(2000). These estimators can be used with limited, censored,
continuous, or discrete endogenous regressors and have significant
advantages over the linear probability model. These estimators are
numerically straightforward to implement.  We present and demonstrate
an improved version of a Stata routine that provides both estimation
and postestimation features, and we give a simple example where the
linear probability model fails to estimate any useful quantity.
Reference: Lewbel, A. 2000. Semiparametric qualitative response model
estimation with unknown heteroscedasticity and instrumental
variables. Journal of Econometrics 97: 145–177.

14:00 - 14:30 Robust Regression in Stata

Ben Jann (University of Bern) 

Abstract: Least-squares regression is a major workhorse in applied
research. Yet its estimates may be deemed nonrobust under various
conditions. One example is heavy-tailed error distributions, in which
least-squares estimation may lose its cutting edge with respect to
efficiency. More importantly, ordinary regression methods can produce
biased results if the data are contaminated by a set of observations
stemming from an alternative process. Various robust regression
estimators have been proposed in the literature to address these
problems, but they do not seem to be employed much in practical
research. One reason for this underutilization may be a lack of
convenient software implementations, as is exemplified by a
close-to-complete absence of robust estimators from official Stata. In
this talk, I will therefore present a number of user-written commands
geared toward robust estimation of regression models.

14:30 - 15:00 Working in the Margins to Plot a Clear Course

Bill Rising (StataCorp) 

Abstract: Visualizing the true effect of a predictor over a range of
values can be difficult for models that are not parameterized in their
natural metric, such as for logistic or (even more so) probit
models. Interaction terms in such models cause even more fogginess. In
this talk, I show how both the margins and the marginsplot commands
can make for much clearer explanations of effects for both
nonstatisticians and statisticians alike.

15:00 - 15:15 Coffee

15:15 - 15:45 Can multilevel multiprocess models be estimated using
Stata? A case for the the cmp command

Tamás Bartus (Corvinus University of Budapest) 

Abstract: Multilevel multiprocess models are routinely used to study
parallel processes of repeated demographic events, like births, union
formation, and union dissolution. Multilevel multiprocess models are
simultaneous equations for hazards including heterogeneity components,
and the joint estimation of hazard models allows researchers to
control for the effects of unobserved personality traits. Such models
are routinely estimated using MLwiN and aML. In this talk, I discuss
the capabilities of Stata to estimate multiprocess multilevel
models. In the presentation, I focus on the application of the
user-written cmp command, developed by David Roodman (2007). The cmp
command can estimate recursive systems of multilevel (random-effects)
equations with correlated disturbances. I illustrate the application
of the cmp command using examples from demographic research.
Reference: Roodman, D. 2007. cmp: Stata module to implement
conditional (recursive) mixed process estimator. Statistical Software
Components S456882, Department of Economics, Boston

15:45 - 16:15 Rescaling results of mixed nonlinear probability models
to compare regression coefficients or variance components across
hierarchically nested models

Dirk Enzmann (University of Hamburg) and Ulrich Kohler
(Social Science Research Center Berlin) 

Abstract: Because of the scaling of the unobserved latent dependent
variable in logistic and probit multilevel models, the lowest level
residual variance is always pi2/3 (logistic regression) or 1.0 (probit
regression). As a consequence, a change of regression coefficients and
variance components between hierarchically nested models cannot be
interpreted unambiguously. To overcome this issue, rescaling of the
unobserved latent dependent variable of nested models to the scale of
the intercept-only model has been proposed (Hox 2010). In this talk,
we demonstrate the use of the program meresc, which implements this
procedure to rescale the results of mixed nonlinear probability models
such as xtmelogit, xtlogit, or xtprobit.  Reference: Hox,
J. J. 2010. Multilevel Analysis: Techniques and Applications. New
York: Routledge.

16:15 - 16:45 Multi-level tools

Katja Möhring and Alexander Schmidt (University of Cologne) 

Abstract: The Stata package "multilevel tools" (mlt) includes a range
of ado-files for postestimation after multilevel models
(xtmixed/xtmelogit). Up to now, it contains three commands (more
ado-files will be added in the future): 1.  mltrsq gives the
Boskers/Snijders R-square and the Bryk/Raudenbusch R-square values.
2.  mltcooksd gives the influence measures Cook’s D and DFBETAs for
the higher-level units in hierarchical mixed models.  3.  mltshowm
presents how the model looks if those cases detected as influential
are excluded from the sample.  In our presentation, we will discuss
the issue of influential cases in multilevel modeling. We will use
some research examples to stress the importance of considering
influential cases, particularly in multilevel analysis. We will show
how the influence measures for second-level units are defined and how
we calculate them.

16:45 - 17:00 Coffee

17:00 - 17:30 Modular Programming in Stata

Daniel Schneider (University of Frankfurt/Main) 

Abstract: Stata provides an easy and effective way of programming and
distributing user-written additions to Stata’s command
universe. However, a Stata programmer may face problems when trying to
distribute an ado-file whose code in turn depends on one or many other
self-written or third-party user-written routines. Distributing the
ado-files as a package may not be appropriate, or it may be cumbersome
in terms of compilation and maintenance.  The user-written command
copycode facilitates code production, code certification, code
maintenance, and code distribution in a context of extensive ado-file
programming with many interdependencies among user-written files. Its
main purpose is to assemble ado-files for distribution that are
nondependent on other user-written files. It does so by copying the
relevant code into one file. The programmer’s burden of keeping track
of all first-order and higher dependencies is reduced to the
compilation of a list of first-order dependencies, which is given to
copycode as an input. copycode will then assemble a
ready-to-distribute, nondependent ado-file that contains unique
first-order and higher Stata subroutines and Mata code as private

17:30 - 18:15 Report to the users

Bill Gould (StataCorp)

18:15 - 18:45 Wishes and grumbles

18:45 End of the meeting

Registration and accommodations:

Participants are asked to travel at their own expense. The conference
fee covers costs for coffee, tea, and lunch. There will also be an
optional informal meal at additional cost at a restaurant in Berlin on
Friday evening.  You can enroll by emailing Anke Mrosek
( or by writing, phoning, or faxing to:

Anke Mrosek
Dittrich & Partner Consulting GmbH 
Prinzenstrasse 2 
42697 Solingen 

Tel: +49 (0)212 260 6624 
Fax: +49 (0)212 260 6666

Conference venue 

Wissenschaftszentrum Berlin 
Reichpietschufer 50 
10785 Berlin 


Workshop “Applied Data Management Using Stata"

Date and Place:

Thursday, May 31 2012, 9:00 – 17:00 

DIW Berlin 
(German Institute for Economic Research)
Mohrenstraße 58 
10117 Berlin 

Presenters: Kerstin Hoenig, Sebastian Wenz, and Sven-Oliver Spieß

Fees: 65 € (Workshop and Conference: 85 €) 



This workshop covers how to use Stata for data management tasks, both
basic and advanced. It is targeted to participants with limited
knowledge of Stata and to those who use Stata for estimating models
but do the data management in other software.  We'll start the
workshop with various ways to input data into Stata. We then discuss
data cleaning and various special problems of data management,
including data management in datasets with hierarchical
structures. Each topic will be illustrated with examples and deepened
with practical exercises.

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