st: 2013 German Stata Users Group Meeting -- Program

[Ulrich Kohler <ukohler@uni-potsdam.de>]

Dear Stata-Users,

The 11th German Stata Users Group Meeting will be held at the the 
University of Potsdam on Friday, June 7 2013. Everybody from anywhere 
who is interested in using Stata is invited to attend this meeting. The 
meeting will include presentations about causal models, general 
statistics, and data management, both by researchers and by StataCorp 
staff. The meeting will also include a "wishes and grumbles" session, 
during which you may air your thoughts to Stata developers.

On the day before the conference, Ulrich Kohler, Co-author of the Book 
"Data Analysis Using Stata" and author of several user written Stata 
commands will hold a workshop on "Advanced Do-File programming and 
introduction to Ado-file programs". Details about the workshop are given 
below the program.

There is (at additional cost) the option of an informal meal at a 
restaurant in Potsdam on Friday evening. Details about accommodations 
and fees are given below.

The conference language will be English.

Also see: http://www.stata.com/meeting/germany13/


Date and Venue
-------------

June 7, 2013
University of Potsdam
Building Nr. 6, Room H06
Campus Griebnitzsee
August-Bebel-Str. 89
14482 Potsdam

http://www.uni-potsdam.de/wiso_dekanat/english

Costs
-----

Meeting only: 45 EUR (students 25 EUR)
Workshop on June 6: 65 EUR
Workshop and Conference: 85 EUR

Registration and accommodations
-------------------------------

Please travel at your own expense. You can enroll by contacting Anke 
Mrosek (anke.mrosek@dpc.de) by email or by writing, phoning, or faxing to

Anke Mrosek
Dittrich & Partner Consulting GmbH
Prinzenstrasse 2
42697 Solingen Germany

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

Scientific Organizers
--------------------

The academic program of the meeting is being organized by J. Giesecke 
(University of Bamberg) and and U. Kohler (University of Potsdam).

Logistics organizers
--------------------

The logistics are being organized by Dittrich and Partner 
(http://www.dpc.de), the distributor of Stata in several countries 
including Germany, The Netherlands, Austria, Czech Republic, and Hungary.

Program
-------

8:30–9:00 Registration

9:00–9:15 Welcome
Ulrich Kohler, University of Potsdam

9:15–10:15 Creating complex tables for publication
John Luke Gallup, Portland State University

Abstract: Complex statistical tables often must be built up by parts 
from the results of multiple Stata commands. I show the capabilities of 
frmttable and outreg for creating complex tables, and even fully 
formatted statistical appendices, for Word and TeX documents. Precise 
formatting of these tables from within Stata has the same benefits as 
writing do-files for statistics commands. They are reproducible and 
reusable when the data change, saving the user time.

10:15–11:15 An expanded framework for mixed process modeling in Stata
David Roodman, Center for Global Development

Abstract: Roodman (Stata Journal, 2011) introduced the program cmp for 
using maximum likelihood to fit multiequation combinations of 
Gaussian-based models such as tobit, probit, ordered probit, multinomial 
probit, interval censoring, and continuous linear. This presentation 
describes substantial extensions to the framework and software: factor 
variable support; the rank-ordered probit model; the ability to specify 
precensoring truncation in most model types; hierarchical random effects 
and coefficients that are potentially correlated across equations; the 
ability to include the unobserved linear variables behind endogenous 
variables—not just their observed, censored manifestations—on the right 
side of other equations and, when so doing, the allowance for 
simultaneity in the system of equations. Contrary to the title of 
Roodman (2011), models no longer need be recursive or fully observed.

11:15–11:30 Coffee

11:30–11:45 Provide, Enrich and Make Accessible: Using Stata’s 
Capabilities for Disseminating NEPS Scientific Use Data
Daniel Bela, National Educational Panel Study (NEPS), Data Center 
University of Bamberg A

Abstract: The National Educational Panel Study (NEPS) is rising as one 
of Germany’s major publisher of scientific use data for educational 
research. Disseminating data from six panel cohorts makes not only 
structured data editing but also documentation and user support a major 
challenge. In order to accomplish this task, the NEPS Data Center has 
implemented a sophisticated metadata system. It does not only allow the 
structured documentation of the metadata of survey instruments and data 
files. It also allows one to enrich the scientific use files with further 
information, thus significantly easing access for data analyses. As a 
result, NEPS provides bilingual dataset files (German and English) and 
allows the user to instantly see, for instance, the exact wording of the 
question leading to the data in a distinct variable without leaving the 
dataset. To achieve this, structured metadata is attached to the data 
using Stata’s characteristics functionality. To make handling additional 
metadata even easier, the NEPS Data Center provides a package of 
user-written programs, NEPStools, to data users. The presentation will 
cover an introduction to the NEPS data preparation workflow, focusing on 
the metadata system and its role in enriching the scientific use data by 
using Stata’s capabilities. Afterward, NEPStools will be introduced.

11:45–12:00 newspell—Easy Management of Complex Spell Data
Hannes Neiss, German Institute for Economic Research (DIW) and Berlin 
Graduate School of Social Sciences (BGSS)

Abstract: Biographical data gathered in surveys is often stored in spell 
format, allowing for overlaps between spell states. This gives useful 
information to researchers but leaves them with a very complex data 
structure, which is not easy to handle. I present my work on the 
ado-package newspell. It includes several subprograms for management of 
complex spell data. Spell states can be merged, reducing the overall 
number of spells. newspell allows a user to fill gaps with information 
from spells before and after the gap, given a user-defined preference. 
However, the two most important features of newspell are, first, the 
ability to rank spells and cut off overlaps according to the rank order. 
This is a necessary step before performing, for example, sequence 
analysis on spell data. Second, newspell can combine overlapping spells 
into new categories of spells, generating entirely new states. This is 
useful for cleaning data, for analyzing simultaneity of states, or for 
combining two spell datasets that have information on different kinds of 
states (for example, labor market and marital status). newspell is 
useful for users who are not familiar with complex spell data and have 
little experience in Stata programming for data management. For 
experienced users, it saves a lot of time and coding work.

12:00–12:30 Instrumental variables estimation using 
heteroskedasticity-based instruments
Christopher F. Baum, Arthur Lewbel (Boston College) Mark E. Schaffer 
(Heriot–Watt University, Edinburgh), Oleksandr Talavera (University of 
Sheffield)

Abstract: In a 2012 article in the Journal of Business and Economic 
Statistics, Arthur Lewbel presented the theory of allowing the 
identification and estimation of “mismeasured and endogenous regressor 
models” by exploiting heteroskedasticity. These models include linear 
regression models customarily estimated with instrumental variables (IV) 
or IV-GMM techniques. Lewbel’s method, under suitable conditions, can 
provide instruments where no conventional instruments are available or 
augment standard instruments to enable tests of overidentification in the 
context of an exactly identified model. In this talk, I discuss the 
rationale for Lewbel’s methodology and illustrate its implementation in 
a variant of Baum, Schaffer, and Stillman’ sivreg2 routine, ivreg2h.

12:30–13:30 Lunch

13:30–14:00 Using simulation to inspect the performance of a test, in 
particular tests of the parallel regressions assumption in ordered logit 
and probit models
Maarten L. Buis, Social Science Research Center (WZB) and Richard 
Williams, University of Notre Dame

Abstract: In this talk, we will show how to use simulations in Stata to 
explore to what extent and under what circumstances a test is 
problematic. We will illustrate this for a set of tests of the parallel 
regression assumption in ordered logit and probit models: the Brant, 
likelihood ratio, Wald, score, and Wolfe-Gould test of the parallel 
regression assumption. A common impression is that these tests tend to 
be too anti-conservative; that is, they tend to reject a true null 
hypothesis too often. We will use simulations to try to quantify when 
and to what extent this is the case. We will also use these simulations 
to create a more robust bootstrap variation of the tests. The purpose of 
this talk is twofold: first, we want to explore the performance of these 
tests. For this purpose, we will present a new program, oparallel, that 
implements all tests and their bootstrap variation. Second, we want to 
give more general advice on how to use Stata to create simulations when 
one has doubts about a certain test. For this purpose, we will present 
the simpplot command, which can help to interpret the p-values returned 
by such a simulation.

14:00–14:30 Fitting Complex Mixed Logit Models with Particular Focus on 
Labor Supply Estimation
Max Löffler, Institute for the Study of Labor (IZA)

Abstract: When one estimates discrete choice models, the mixed logit 
approach is commonly superior to simple conditional logit setups. Mixed 
logit models not only allow the researcher to implement difficult random 
components but also overcome the restrictive IIA assumption. Despite 
these theoretical advantages, the estimation of mixed logit models 
becomes cumbersome when the model’s complexity increases. Applied works 
therefore often rely on rather simple empirical specifications because 
this reduces the computational burden. I introduce the user-written 
command lslogit, which fits complex mixed logit models using maximum 
simulated likelihood methods. As lslogit is a d2-ML-evaluator written in 
Mata, the estimation is rather efficient compared with other routines. It 
allows the researcher to specify complicated structures of unobserved 
heterogeneity and to choose from a set of frequently used functional 
forms for the direct utility function—for example, Box-Cox 
transformations, which are difficult to estimate in the context of logit 
models. The particular focus of lslogit is on the estimation of labor 
supply models in the discrete choice context; therefore, it facilitates 
several computationally exhausting but standard tasks in this research 
area. However, the command can be used in many other applications of 
mixed logit models as well.

14:30–15:00 Simulated Multivariate Random Effects Probit Models for 
Unbalanced Panels
Alexander Plum, Otto-von-Guericke University Magdeburg

Abstract: This paper develops an implementation method of a simulated 
multivariate random-effects probit model for unbalanced panels, 
illustrating it by using artificial data. By mdraws, generated Halton 
draws are used to simulate multivariate normal probabilities with the 
command mvnp(). The estimator can be easily adjusted (for example, to 
allow for autocorrelated errors). Advantages of this simulated 
estimation are high accuracy and lower computation time compared with 
existing commands such as redpace.

15:00–15:15 Coffee

15:15–15:45 xsmle—A Command to Estimate Spatial Panel Models in Stata
Federico Belottia, Gordon Hughes, Andrea Piano Mortari CEIS, University 
of Rome "Tor Vergata" and School of Economics, University of Edinburg.

Abstract: Econometricians have begun to devote more attention to spatial 
interactions when carrying out applied econometric studies. The new 
command we are presenting, xsmle, fits fixed- and random-effects spatial 
models for balanced panel data for a wide range of specifications: the 
spatial autoregressive model, spatial error model, spatial Durbin model, 
spatial autoregressive model with autoregressive disturbances, and 
generalized spatial random effect model with or without a dynamic 
component. Different weighting matrices may be specified for different 
components of the models and both Stata matrices and spmat objects are 
allowed. Furthermore, xsmle calculates direct, indirect, and total 
effects according to Lesage (2008), implements Lee and Yu (2010) data 
transformation for fixed-effects models, and may be used with mi prefix 
when the panel is unbalanced.

15:45–16:15 Estimating the dose-response function through the GLM approach
Barbara Guardabascio, Marco Ventura Italian Nationale Institute of 
Statistics, Rome

Abstract: How effective are policy programs with continuous treatment 
exposure? Answering this question essentially amounts to estimating a 
dose-response function as proposed in Hirano and Imbens (2004). Whenever 
doses are not randomly assigned but are given under experimental 
conditions, estimation of a dose-response function is possible using the 
Generalized Propensity Score (GPS). Since its formulation, the GPS has 
been repeatedly used in observational studies, and ad hoc programs have 
been provided for Stata users (doseresponse and gpscore, Bia and Mattei 
2008). However, many applied works remark that the treatment variable 
may not be normally distributed. In this case, the Stata programs are 
not usable because they do not allow for different distribution 
assumptions other than the normal density. In this paper, we overcome 
this problem. Building on Bia and Mattei’s (2008) programs, we provide 
doseresponse2 and gpscore, which allow one to accommodate different 
distribution functions of the treatment variable. This task is 
accomplished through by the application of the generalized linear models 
estimator in the first step instead of the application of maximum 
likelihood. In such a way, the user can have a very versatile tool 
capable of handling many practical situations. It is worth highlighting 
that our programs, among the many alternatives, take into account the 
possibility to consistently use the GPS estimator when the treatment 
variable is fractional, the flogit case by Papke and Wooldridge (1998), 
a case of particular interest for economists. Predictive Margins and 
Marginal Effects in Stata

16:15–16:45 Predictive Margins and Marginal Effects in Stata
Ben Jann, University of Bern jann@soz.unibe.ch

Abstract: Tables of estimated regression coefficients, usually accompanied 
by additional information such as standard errors, t-statistics, 
p-values, confidence intervals or significance stars, have long been the 
preferred way of communicating results from statistical models. In 
recent years, however, the limits of this form of exposition have been 
increasingly recognized. For example, interpretation of regression 
tables can be very challenging in the presence of complications such as 
interaction effects, categorical variables, or nonlinear functional 
forms. Furthermore, while these issues might still be manageable in the 
case of linear regression, interpretational difficulties can be 
overwhelming in nonlinear models such as logistic regression. To 
facilitate sensible interpretation of such models it is often necessary 
to compute additional results such as marginal effects, predictive 
margins, or contrasts. Moreover, smart graphical displays of results can 
be very valuable in making complex relations accessible. A number of 
helpful commands geared at supporting these tasks have been recently 
introduced in Stata, making elaborate interpretation and communication 
of regression results possible without much extra effort. Examples of 
such commands are margins, contrasts, and marginsplot. In my talk, I 
will discuss the capabilities of these commands and present a range of 
examples illustrating their use.

16:45–17:00 Coffee

17:00–17:45 Report to the Users
Bill Rising

Abstract: Bill Rising, Director of Educational Services, talk about 
developments at Stata.

17:45–18:30 Wishes & Grumbles

Workshop
========

Advanced Do-File programming and introduction to Ado-file programs
Ulrich Kohler


Description
-----------

The workshop covers two types of Stata programming: do-file programming 
and adofile programming. It starts by presenting programming tools such 
as local macros, extended macro functions, inline macro expansion, 
loops, and branches and gives examples of their use in everyday data 
analysis. The second part of the course explains the nuts and bolds of 
defining a “program” in Stata. Finally the course provides a step-by-step 
tutorial for implementing a user-written command into Stata 
(“Ado-File”). Participants should be already familiar Stata. They should 
know how to start a do-file from the command-line. Morover, command of 
the data manipulation tools generate and replace, basic data-analysis 
tools such as tabulate, summarize, and regress, and some experiences 
with graph are necessary prerequisites. 1. Advanced do-file programming 
Local macros Extended macro functions Scalars Loops 2. Ado-file 
programming Program definition Parsing user input to programs 
Step-by-step example Programming style


Date and Place
--------------

Thursday, June 6 2013, 9:00 – 17:00
University of Potsdam Building Nr. 6, Room S12
Campus Griebnitzsee
August-Bebel-Str. 89
14482 Potsdam
http://www.uni-potsdam.de/wiso_dekanat/


Presenter
---------

Prof. Kohler holds the chair for Methods of Empirical Social Research at 
the University of Potsdam. He is co-author of "Data Analysis Using 
Stata" and author of several user written Stata commands.

Fees
----

65 Euro (Workshop and Conference: 85 Euro)

Register
--------

You can enroll by contacting Anke Mrosek (anke.mrosek@dpc.de) by email 
or by writing, phoning, or faxing to

Anke Mrosek
Dittrich & Partner Consulting GmbH
Prinzenstrasse 2
42697 Solingen
Germany

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

Description
-----------

The workshop covers two types of Stata programming: do-file programming 
and adofile programming. It starts by presenting programming tools such 
as local macros, extended macro functions, inline macro expansion, 
loops, and branches and gives examples of their use in everyday data 
analysis. The second part of the course explains the nuts and bolds of 
defining a "program" in Stata. Finally the course provides a step-by-step 
tutorial for implementing a user-written command into Stata 
("Ado-File"). Participants should be already familiar Stata. They should 
know how to start a do-file from the command-line. Moreover, command of 
the data manipulation tools generate and replace, basic data-analysis 
tools such as tabulate, summarize, and regress, and some experiences 
with graph are necessary prerequisites.

1. Advanced do-file programming
- Local macros
- Extended macro functions
- Scalars
- Loops
2. Ado-file programming
- Program definition
- Parsing user input to programs
- Step-by-step example
- Programming style





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