GERMAN STATA CONFERENCE 2024: Announcement and program
Overview
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Date/Venue/Cost
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Date: Conference: June 7, 2024, 9am-6pm
Workshop: June 6, 2024, 10am-5pm
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Venue: GESIS—Leibniz-Institut für Sozialwissenschaften
B6, 4–5
68159 Mannheim
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Costs: Conference only: 49.99 EUR (students: 35 EUR)
Workshop only: 65 EUR (students: 50 EUR)
Conference and workshop: 85 EUR (students: 70 EUR)
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Web: <https://www.stata.com/meeting/germany24/>
<https://dpc-software.de/2024-german-stata-conference/>
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Deadline for Registration: June 4th 2024
Meeting
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The 21st German Stata Conference will be held on Friday, June 7th 2024
in Mannheim at GESIS—Leibniz Institute for the Social Sciences. 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 (Humboldt University
Berlin), Ulrich Kohler (University of Potsdam), and Reinhard Pollak
(GESIS). 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 DPC Software GmbH, distributor of Stata in several
countries including Germany, The Netherlands, Austria, the Czech Republic
and Hungary (<http://www.dpc-software.de>).
Workshop
────────
On the day before the conference, there will be a one-day workshop on
"DID estimation using Stata" by Felix Knau; see the detailed
the description below the program.
Time table
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8:15–8:45 Registration
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8:45–9:00 Welcome
Reinhard Pollak
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9:15–10:15 Recent developments in the fitting and assessment
of flexible parametric survival models
Paul Lambert
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10:15–10:45 Coffee
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10:45–11:15 `cfbinout' and `xtdhazard': Control-Function
Estimation of Binary-Outcome Models and the
Discrete-Time Hazard Model
Harald Tauchmann and Elena Yurkevich
11:15–11:45 Multi-dimensional well-being, deprivation, and
inequality
Peter Krause
11:45–12:00 How to assess the fit of choice models with Stata?
Wolfgang Langer
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12:00–13:00 Lunch Break
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13:00–14:15 Customizable tables
Kristin MacDonald
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14:15–14:45 Coffee
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14:45–15:15 `geoplot': A new command to draw maps
Ben Jann
15:15–15:45 `repreport': Facilitating reproducible research in
Stata
Daniel Krähmer
15:45–16:15 `mkproject' and boilerplate: automate the beginning
Maarten Buis
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16:15–16:45 Coffee
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16:45–17:15 Data structures in Stata
Daniel Schneider
17:15–18:00 Open panel discussion with Stata developers
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18:00 End of meeting
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Conference venue
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GESIS—Leibniz-Institute for the Social Sciences
B6, 4–5
68159 Mannheim
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How to get to the venue
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The venue is within walking distance from Mannheim's central station
(1.3 km). Alternatively, you can take any of the Trams *1* (Schönau),
*4* (Bad Dürkheim), *5* (Weinheim via Heidelberg), and *6*
(Rheingönheim). In all cases, exit at /Schloss/ (2 stations) and walk
the remaining 450m.
Note very carefully that Mannheim's city centre does not have street
names. Instead, city blocks are named with an index similar to the
fields on a chess board. The venue is in field B5. The houses in all
Blocks A to K are numbered in ascending order when you
circumnavigate the blocks counter-clockwise, starting at the corner
closest to the castle. This all has its logic, but you can also ask
Google to bring you to the entrance door.
Registration and accommodations
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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 on Friday evening.
You can enrol by emailing Natascha Hütter
([email protected]) by writing, phoning, or faxing to
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Natascha Hütter
DPC Software GmbH
Prinzenstraße 2
42697 Solingen
Germany
Tel: +49 (0)212 / 22 47 16 -21
www.dpc-software.de
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Workshop: DID Estimation Using Stata
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Date and Place
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Thursday, June 6, 2024
10am–5pm
GESIS—Leibniz-Institute for the Social Sciences
B6, 4–5
68159 Mannheim
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Topics
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• Short introduction DID und TWFE: How to apply those in Stata (simple
examples, `didregress', `xtreg', `reghdfe')
• What does TWFE identify (and when this may be problematic): brief
introduction to `twowayfeweights'
• New DID methods robust to heterogeneous treatment effects: Static
case, dynamic case (and maybe continuous treatment)
• More extensive (interactive) session on implementing corresponding
commands: `did_multiplegt_dyn', `csdid', `eventstudyinteract',
`did_imputation' (with focus on `did_multiplegt_dyn')
Presenter
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Felix Knau
SciencesPo, Paris
Department of Economics
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Felix Knau is a Research Assistant working on Clément de
Chaisemartin's ERC CoG project Completing the revolution: Enhancing
the reality, the principles, and the impact of economics' credibility
revolution (REALLYCREDIBLE).
Fees
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Workshop only: 65 EUR (students 50 EUR) Workshop and Meeting: 85 EUR
(students 70 EUR)
Registration
────────────
Please register for the conference and/or the workshop by writing an
E-mail to Natasch Hütter ([email protected])
Abstracts
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9:15–10:15 Recent developments in the fitting and assessment of flexible parametric survival models
Paul Lambert (University of Leicester, UK and Karolinska Institutet,
Sweden)
/Abstract:/ Flexible parametric survival models are an alternative to
the Cox proportional hazards model and more standard parametric models
for the modelling of survival (time-to-event) data. They are flexible
in that spline functions are used to model the baseline and
potentially complex time-dependent effects. I will give a brief
overview of the models and the advantages over the Cox model. However,
I will concentrate on some recent developments. This will include the
motivation for developing a new command to fit the models (`stpm3'),
which makes it much simpler to fit more complex models with non-linear
functions, non-proportional hazards and interactions and simplifies
and extends postestimation predictions, particularly marginal
(standardized) predictions. I will also describe some new
postestimation tools that help in the evaluation of model fit and
validation in prognostic models.
10:45–11:15 `cfbinout' and `xtdhazard': Control-Function Estimation of Binary-Outcome Models and the Discrete-Time Hazard Model
Harald Tauchmann (FAU Erlangen-Nurenberg) and Elena Yurkevich (FAU
Erlangen-Nurenberg)
/Abstract:/ We introduce the new community-contributed Stata commands
`cfbinout' and `xtdhazard'. The former generalizes `ivprobit, twostep'
by allowing discrete endogenous regressors and different link
functions than the normal link, specifically logit and cloglog. In
terms of the underlying econometric theory, `cfbinout' is guided by
Wooldridge (2015). In terms of the implementation in Stata and Mata,
respectively, `cfbinout' follows Terza (2017). `xtdhazard' is
essentially a wrapper for either `cfbinout' or alternatively
`ivregress 2sls'. When calling `ivregress 2sls', `xtdhazard'
implements the linear first-differences (or higher-order differences)
instrumental variables estimator suggested by Farbmacher & Tauchmann
(2023) for dealing with time-invariant unobserved heterogeneity in the
discrete-time hazard model. When calling `cfbinout', `xtdhazard'
implements—depending on the specified link function—several nonlinear
counterparts of this estimator that are briefly discussed in the
online supplement to Farbmacher & Tauchmann (2023). Using
`xtdhazard'—rather than directly using `ivregress 2sls', `ivprobit,
twostep', or `cfbinout'—simplifies the implementation of these
estimators, as generating the numerous instruments required can be
cumbersome, especially when using factor-variables syntax. In
addition, `xtdhazard' performs several checks that may prevent
`ivregress 2sls' and `ivprobit, twostep', respectively, from failing
and reports issues like perfect first-stage predictions. An (extended)
replication of Cantoni (2012) illustrates the use of cfbinout and
xtdhazard in applied empirical work.
• Cantoni, D. (2012). Adopting a new religion: The case of
Protestantism in 16th century Germany, The Economic Journal 122,
502-531.
• Farbmacher, H. and Tauchmann, H. (2023). Linear fixed-effects
estimation with nonrepeated outcomes, Econometric Reviews 42(8):
635–654.
• Terza, J. (2017). Two-stage residual inclusion estimation: A
practitioners guide to Stata implementation, Stata Journal 17(4):
916–938.
• Wooldridge, J. M. (2015). Control function methods in applied
econometrics, The Journal of Human Resources 50(2): 420–445.
11:15–11:45 Multi-dimensional well-being, deprivation, and inequality
Peter Krause (DIW Berlin, SOEP)
/Abstract:/ The presentation offers a brief summary for a set of Stata
programs for extended multidimensional applications on well-being,
deprivation, and inequality. The first section illustrates the
underlaying motivation by some empirical examples on decomposed multi-
dimensional results. The second section on multi-dimensional
well-being and deprivation measurement illustrates the conceptual
background—based on the Alkire/Foster MPI framework (and CPI,
N. Rippin)—which is also applied to well-being measurement, and
extended by a parameter driven fixed-fuzzy approach—with several
illustrations and further details on the options offered in the Stata
deprivation and well-being programs. The third section on multi-
dimensional inequalities refers to a multidimensional Gini-based
row-first measurement framework with a special emphasize on multiple
within- and between-group-inequalities—including conceptual extensions
on horizontal between-group applications and further details on the
options offered in the Stata inequality program. Section four
summarizes and opens up for advice and discussion.
11:45–12:00 How to assess the fit of choice models with Stata?
Wolfgang Langer (Martin-Luther-University Halle-Wittenberg)
/Abstract:/ McFadden developed the conditional multinomial logit model
in 1974 using it for rational choice modeling. In 1993 Stata
introduced it in version 3. In 2007 Stata extended this model to the
asclogit or ascprobit being able to estimate the effects of
alternative-specific and case-specific exogenous variables on the
choice probability of the discrete alternatives. In 2021, Stata added
the class of choice models extending it to random-effect (mixed) and
panel models. As it stands, Stata only provides an post-estimation
Wald chi-square test to assess the overall model. However, although
McFadden developed an pseudo r-square to assess the fit of the
conditonal logit model already in 1974, Stata still does not provide
it even in version 18. Thus, I developed `fit_cmlogit' to calculate
the McFadden pseudo r-square using a zero model with
alternative-specific constants to correct the uneven distribution of
alternatives. Furthermore, it calculates the corresponding
Likelihood-Ratio-chi-square test which is more reliable / conservative
as the Wald test. The program uses the formulas of Hensher & Johnson
(1981) and Ben-Akiva & Lerman (1985) for the McFdden pseudo-r square
to correct the number of exogenous variables and faced
alternatives. Train (2003) discussed these characteristics of the
McFadden pseudo r-square in detail. Additionally it calculates the
log-likelihood-based pseudo r-squares developed by Maddala (1983,
1988), Cragg & Uhler (1970) and Aldrich & Nelson (1984). The latter
uses the correction formula proposed by Veall & Zimmermann (1994). An
empirical example of predicting voting behavior in the German federal
election study of 1990 demonstrates the usefulness of the program to
assess the fit of logit choice models with alternative-specific and
case-specific exogenous variables.
• Aldrich, J.H. & Nelson, F.D. (1984): Linear probability, logit and
probit models. Beverly Hills, CA: Sage
• Ben-Akiva, M. & Lerman, S.R. (1985): Discrete choice analysis:
Theory and application to travel demand. Cambridge, MA: MIT Press
• Cragg, G. & Uhler, R. (1970): The demand of automobiles. Canadian
Journal of Economics, 3, pp.386-406
• Hensher, D.A. & Johnson, L.W. (1981): Applied discrete choice
modelling. London: Croom Helm/Wiley
• Domencich, T.A. & McFadden, D. (1975): Urban travel demand. A
behavioral analysis. Amsterdam u. Oxford: North Holland Publishing
Company
• Maddala, G.S. (1983): Limited-dependent and qualitative variables in
econometrics. Cambridge, U.K.: Cambridge University Press
• Maddala, G.S. (1992² (1988)): Introduction to Econometrics. New
York, N.Y.: Maxwell
• MacmillanMcFadden, D. (1974): Conditional logit analysis of
qualitative choice behavior. In: Frontiers of
econometrics. Ed. P. Zarembka,, pp. 105-142. New York: Academic
Press
• McFadden, D. (1979): Quantitative methods for analysing travel
behaviour of individuals: some recent developments. In: Hensher,
D.A.& Stopher, P.R.: (eds): Behavioural travel modelling. London:
Croom Helm, pp. 279-318
• Train, K.E. (2003): Discrete choice methods with
Simulations. Cambridge, U.K.: Cambridge University Press
• Veall, M.R. & Zimmermann, K.F. (1994): Evaluating Pseudo-R2's for
binary probit models. Quality&Quantity, 28, pp. 151- 164
13:00–14:15 Customizable tables
Kristin MacDonald (StataCorp)
/Abstract:/ Presenting results effectively is a crucial step in
statistical analyses, and creating tables is an important part of this
step. Whether you need to create a cross-tabulation, a Table 1
reporting summary statistics, a table of regression results, or a
highly customized table of results returned by multiple Stata
commands, the tables features introduced in Stata 17 and Stata 18
provide ease and flexibility for you to create, customize, and export
your tables. In this presentation, I will demonstrate how to use the
`table', `dtable', and `etable' commands to easily create a variety of
tables. I will also show how to use the `collect' suite to build and
customize tables and to create table styles with your favorite
customizations that you can apply to any tables you create in the
future. Finally, I will demonstrate how to export individual tables to
Word, Excel, LaTeX, PDF, Markdown, and HTML and how to incorporate
your tables into complete reports containing formatted text, graphs,
and other Stata results.
14:45–15:15 `geoplot': A new command to draw maps
Ben Jann (University of Bern)
/Abstract:/ `geoplot' is a new command for drawing maps from shape
files and other datasets. Multiple layers of elements such as regions,
borders, lakes, roads, labels, and symbols can be freely combined and
the look of elements (e.g. color) can be varied depending on the
values of variables. Compared to previous solutions in Stata,
`geoplot' provides more user convenience, more functionality, and more
flexibility. In this talk I will introduce the basic components of the
command and illustrate its use with examples.
15:15–15:45 `repreport': Facilitating reproducible research in Stata
Daniel Krähmer
(Ludwig-Maximilians-Universität München)
In theory, Stata provides a stable computational environment and
includes commands (i.e., `version') that are specifically designed to
ensure reproducibility. In practice, however, users often lack the
time or the knowledge to exploit this potential. Insights from an
ongoing research project on reproducibility in the social sciences
show that computational reproducibility is regularly impeded by
researchers being unaware what files (i.e., datasets, do-files),
software components (i.e., ados), infrastructure (i.e., directories),
and information (i.e., ReadMe files) is needed to enable reproduction.
This presentation introduces the new Stata command `repreport' as a
potential remedy. The command works like a log, with one key
difference: Instead of logging the entire analysis, repreport extracts
specific pieces of information pertinent to reproduction (e.g., names
and locations of datasets, ados, etc.) and compiles them into a
concise reproduction report. Furthermore, the command includes an
option for generating a full-fledged reproduction package containing
all components needed for push-button reproducibility. While
`repreport' adds little value for researchers whose workflow is
already perfectly reproducible, it constitutes a powerful tool for
those who strive to make their research in Stata more reproducible at
(almost) no additional cost.
15:45–16:15 mkproject and boilerplate: automate the beginning
Maarten L. Buis (University of Konstanz)
There is usually a set of commands that are included in every .do file
a person makes, like `clear all' or `log using'. What those commands
are can differ from person to person, but most persons have such a
standard set. Similarly, a project usually has a standard set of
directories and files. Starting a new .do file or a new project thus
involves a number of steps that could easily be automated. Automating
has the advantage of reducing the amount of work you need to
do. However, the more important advantage of automating the start of a
.do file or project is that it makes it easier to maintain your own
workflow: it is so easy to start "quick and dirty" and promise to
yourself that you will fix that "later". If the start is automated,
then you don't need to fix it.
The `mkproject' command automates the beginning of a project. It comes
with a set of templates I find useful. A template contains all the
actions (like create sub-directories, create files, run other Stata
commands) that `mkproject' will take when it creates a new
project. Since everybody's workflow is different, `mkproject' allows
users to create their own template. Similarly, the `boilerplate'
command creates a new .do file with boilerplate code in it. It comes
with a set of templates, but the user can create their own.
This talk will illustrate the use of both `mkproject' and `boilerpate'
and in particular how to create your own templates.
16:45–17:15 Data structures in Stata
Daniel C. Schneider
(Max Planck Institute for Demographic Research, Rostock)
This presentation starts out by enumerating and describing the main
data structures in Stata (e.g., data sets / frames, matrices) and Mata
(e.g., string and numeric matrices, objects like associative
arrays). It analyzes ways in which data can be represented and coerced
from one data container into another. After assessing the strengths
and limitations of existing data containers, it muses on potential
additions of new data structures and on enriching the functionality of
existing data structures and their interplay. Moreover, data
structures from other languages, such as Python lists, are described
and examined for their potential introduction into Stata / Mata. The
goal of the presentation is to stimulate a discussion among Stata
users and developers about ways in which the capabilities of Stata's
data structures could be enhanced in order to ease and open up new
possibilities for data management and analysis.
17:15–18:00 Open panel discussion with Stata developers
/Abstract/ Contribute to the Stata community by sharing your feedback
with StataCorp's developers. From feature improvements to bug fixes
and new ways to analyze data, we want to hear how Stata can be made
better for you.
Scientific Organizers
═════════════════════
The academic program of the conference is being organized by Johannes
Giesecke (HU Berlin), Ulrich Kohler (University of Potsdam), and
Reinhard Pollak (GESIS—Leibniz-Instute for the Social Sciences)
Logistics organizers
════════════════════
The logistics are being organized by DPC Software GmbH, the
distributor of Stata in several countries including Germany, The
Netherlands, Austria, Czech Republic and Hungary
(<http://www.dpc.de>).
════════
Date/Venue/Cost
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Date: Conference: June 7, 2024, 9am-6pm
Workshop: June 6, 2024, 10am-5pm
────────────────────────────────────────────────── ───────────────
Venue: GESIS—Leibniz-Institut für Sozialwissenschaften
B6, 4–5
68159 Mannheim
────────────────────────────────────────────────── ───────────────
Costs: Conference only: 49.99 EUR (students: 35 EUR)
Workshop only: 65 EUR (students: 50 EUR)
Conference and workshop: 85 EUR (students: 70 EUR)
────────────────────────────────────────────────── ───────────────
Web: <https://www.stata.com/meeting/germany24/>
<https://dpc-software.de/2024-german-stata-conference/>
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Deadline for Registration: June 4th 2024
Meeting
───────
The 21st German Stata Conference will be held on Friday, June 7th 2024
in Mannheim at GESIS—Leibniz Institute for the Social Sciences. 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 (Humboldt University
Berlin), Ulrich Kohler (University of Potsdam), and Reinhard Pollak
(GESIS). 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 DPC Software GmbH, distributor of Stata in several
countries including Germany, The Netherlands, Austria, the Czech Republic
and Hungary (<http://www.dpc-software.de>).
Workshop
────────
On the day before the conference, there will be a one-day workshop on
"DID estimation using Stata" by Felix Knau; see the detailed
the description below the program.
Time table
──────────
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ━━━━━━━━━━━━━━
8:15–8:45 Registration
────────────────────────────────────────────────── ──────────────
8:45–9:00 Welcome
Reinhard Pollak
────────────────────────────────────────────────── ──────────────
9:15–10:15 Recent developments in the fitting and assessment
of flexible parametric survival models
Paul Lambert
────────────────────────────────────────────────── ──────────────
10:15–10:45 Coffee
────────────────────────────────────────────────── ──────────────
10:45–11:15 `cfbinout' and `xtdhazard': Control-Function
Estimation of Binary-Outcome Models and the
Discrete-Time Hazard Model
Harald Tauchmann and Elena Yurkevich
11:15–11:45 Multi-dimensional well-being, deprivation, and
inequality
Peter Krause
11:45–12:00 How to assess the fit of choice models with Stata?
Wolfgang Langer
────────────────────────────────────────────────── ────────────────
12:00–13:00 Lunch Break
────────────────────────────────────────────────── ────────────────
13:00–14:15 Customizable tables
Kristin MacDonald
────────────────────────────────────────────────── ────────────────
14:15–14:45 Coffee
────────────────────────────────────────────────── ────────────────
14:45–15:15 `geoplot': A new command to draw maps
Ben Jann
15:15–15:45 `repreport': Facilitating reproducible research in
Stata
Daniel Krähmer
15:45–16:15 `mkproject' and boilerplate: automate the beginning
Maarten Buis
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16:15–16:45 Coffee
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16:45–17:15 Data structures in Stata
Daniel Schneider
17:15–18:00 Open panel discussion with Stata developers
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18:00 End of meeting
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Conference venue
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GESIS—Leibniz-Institute for the Social Sciences
B6, 4–5
68159 Mannheim
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How to get to the venue
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The venue is within walking distance from Mannheim's central station
(1.3 km). Alternatively, you can take any of the Trams *1* (Schönau),
*4* (Bad Dürkheim), *5* (Weinheim via Heidelberg), and *6*
(Rheingönheim). In all cases, exit at /Schloss/ (2 stations) and walk
the remaining 450m.
Note very carefully that Mannheim's city centre does not have street
names. Instead, city blocks are named with an index similar to the
fields on a chess board. The venue is in field B5. The houses in all
Blocks A to K are numbered in ascending order when you
circumnavigate the blocks counter-clockwise, starting at the corner
closest to the castle. This all has its logic, but you can also ask
Google to bring you to the entrance door.
Registration and accommodations
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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 on Friday evening.
You can enrol by emailing Natascha Hütter
([email protected]) by writing, phoning, or faxing to
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Natascha Hütter
DPC Software GmbH
Prinzenstraße 2
42697 Solingen
Germany
Tel: +49 (0)212 / 22 47 16 -21
www.dpc-software.de
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Workshop: DID Estimation Using Stata
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Date and Place
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Thursday, June 6, 2024
10am–5pm
GESIS—Leibniz-Institute for the Social Sciences
B6, 4–5
68159 Mannheim
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Topics
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• Short introduction DID und TWFE: How to apply those in Stata (simple
examples, `didregress', `xtreg', `reghdfe')
• What does TWFE identify (and when this may be problematic): brief
introduction to `twowayfeweights'
• New DID methods robust to heterogeneous treatment effects: Static
case, dynamic case (and maybe continuous treatment)
• More extensive (interactive) session on implementing corresponding
commands: `did_multiplegt_dyn', `csdid', `eventstudyinteract',
`did_imputation' (with focus on `did_multiplegt_dyn')
Presenter
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Felix Knau
SciencesPo, Paris
Department of Economics
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Felix Knau is a Research Assistant working on Clément de
Chaisemartin's ERC CoG project Completing the revolution: Enhancing
the reality, the principles, and the impact of economics' credibility
revolution (REALLYCREDIBLE).
Fees
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Workshop only: 65 EUR (students 50 EUR) Workshop and Meeting: 85 EUR
(students 70 EUR)
Registration
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Please register for the conference and/or the workshop by writing an
E-mail to Natasch Hütter ([email protected])
Abstracts
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9:15–10:15 Recent developments in the fitting and assessment of flexible parametric survival models
Paul Lambert (University of Leicester, UK and Karolinska Institutet,
Sweden)
/Abstract:/ Flexible parametric survival models are an alternative to
the Cox proportional hazards model and more standard parametric models
for the modelling of survival (time-to-event) data. They are flexible
in that spline functions are used to model the baseline and
potentially complex time-dependent effects. I will give a brief
overview of the models and the advantages over the Cox model. However,
I will concentrate on some recent developments. This will include the
motivation for developing a new command to fit the models (`stpm3'),
which makes it much simpler to fit more complex models with non-linear
functions, non-proportional hazards and interactions and simplifies
and extends postestimation predictions, particularly marginal
(standardized) predictions. I will also describe some new
postestimation tools that help in the evaluation of model fit and
validation in prognostic models.
10:45–11:15 `cfbinout' and `xtdhazard': Control-Function Estimation of Binary-Outcome Models and the Discrete-Time Hazard Model
Harald Tauchmann (FAU Erlangen-Nurenberg) and Elena Yurkevich (FAU
Erlangen-Nurenberg)
/Abstract:/ We introduce the new community-contributed Stata commands
`cfbinout' and `xtdhazard'. The former generalizes `ivprobit, twostep'
by allowing discrete endogenous regressors and different link
functions than the normal link, specifically logit and cloglog. In
terms of the underlying econometric theory, `cfbinout' is guided by
Wooldridge (2015). In terms of the implementation in Stata and Mata,
respectively, `cfbinout' follows Terza (2017). `xtdhazard' is
essentially a wrapper for either `cfbinout' or alternatively
`ivregress 2sls'. When calling `ivregress 2sls', `xtdhazard'
implements the linear first-differences (or higher-order differences)
instrumental variables estimator suggested by Farbmacher & Tauchmann
(2023) for dealing with time-invariant unobserved heterogeneity in the
discrete-time hazard model. When calling `cfbinout', `xtdhazard'
implements—depending on the specified link function—several nonlinear
counterparts of this estimator that are briefly discussed in the
online supplement to Farbmacher & Tauchmann (2023). Using
`xtdhazard'—rather than directly using `ivregress 2sls', `ivprobit,
twostep', or `cfbinout'—simplifies the implementation of these
estimators, as generating the numerous instruments required can be
cumbersome, especially when using factor-variables syntax. In
addition, `xtdhazard' performs several checks that may prevent
`ivregress 2sls' and `ivprobit, twostep', respectively, from failing
and reports issues like perfect first-stage predictions. An (extended)
replication of Cantoni (2012) illustrates the use of cfbinout and
xtdhazard in applied empirical work.
• Cantoni, D. (2012). Adopting a new religion: The case of
Protestantism in 16th century Germany, The Economic Journal 122,
502-531.
• Farbmacher, H. and Tauchmann, H. (2023). Linear fixed-effects
estimation with nonrepeated outcomes, Econometric Reviews 42(8):
635–654.
• Terza, J. (2017). Two-stage residual inclusion estimation: A
practitioners guide to Stata implementation, Stata Journal 17(4):
916–938.
• Wooldridge, J. M. (2015). Control function methods in applied
econometrics, The Journal of Human Resources 50(2): 420–445.
11:15–11:45 Multi-dimensional well-being, deprivation, and inequality
Peter Krause (DIW Berlin, SOEP)
/Abstract:/ The presentation offers a brief summary for a set of Stata
programs for extended multidimensional applications on well-being,
deprivation, and inequality. The first section illustrates the
underlaying motivation by some empirical examples on decomposed multi-
dimensional results. The second section on multi-dimensional
well-being and deprivation measurement illustrates the conceptual
background—based on the Alkire/Foster MPI framework (and CPI,
N. Rippin)—which is also applied to well-being measurement, and
extended by a parameter driven fixed-fuzzy approach—with several
illustrations and further details on the options offered in the Stata
deprivation and well-being programs. The third section on multi-
dimensional inequalities refers to a multidimensional Gini-based
row-first measurement framework with a special emphasize on multiple
within- and between-group-inequalities—including conceptual extensions
on horizontal between-group applications and further details on the
options offered in the Stata inequality program. Section four
summarizes and opens up for advice and discussion.
11:45–12:00 How to assess the fit of choice models with Stata?
Wolfgang Langer (Martin-Luther-University Halle-Wittenberg)
/Abstract:/ McFadden developed the conditional multinomial logit model
in 1974 using it for rational choice modeling. In 1993 Stata
introduced it in version 3. In 2007 Stata extended this model to the
asclogit or ascprobit being able to estimate the effects of
alternative-specific and case-specific exogenous variables on the
choice probability of the discrete alternatives. In 2021, Stata added
the class of choice models extending it to random-effect (mixed) and
panel models. As it stands, Stata only provides an post-estimation
Wald chi-square test to assess the overall model. However, although
McFadden developed an pseudo r-square to assess the fit of the
conditonal logit model already in 1974, Stata still does not provide
it even in version 18. Thus, I developed `fit_cmlogit' to calculate
the McFadden pseudo r-square using a zero model with
alternative-specific constants to correct the uneven distribution of
alternatives. Furthermore, it calculates the corresponding
Likelihood-Ratio-chi-square test which is more reliable / conservative
as the Wald test. The program uses the formulas of Hensher & Johnson
(1981) and Ben-Akiva & Lerman (1985) for the McFdden pseudo-r square
to correct the number of exogenous variables and faced
alternatives. Train (2003) discussed these characteristics of the
McFadden pseudo r-square in detail. Additionally it calculates the
log-likelihood-based pseudo r-squares developed by Maddala (1983,
1988), Cragg & Uhler (1970) and Aldrich & Nelson (1984). The latter
uses the correction formula proposed by Veall & Zimmermann (1994). An
empirical example of predicting voting behavior in the German federal
election study of 1990 demonstrates the usefulness of the program to
assess the fit of logit choice models with alternative-specific and
case-specific exogenous variables.
• Aldrich, J.H. & Nelson, F.D. (1984): Linear probability, logit and
probit models. Beverly Hills, CA: Sage
• Ben-Akiva, M. & Lerman, S.R. (1985): Discrete choice analysis:
Theory and application to travel demand. Cambridge, MA: MIT Press
• Cragg, G. & Uhler, R. (1970): The demand of automobiles. Canadian
Journal of Economics, 3, pp.386-406
• Hensher, D.A. & Johnson, L.W. (1981): Applied discrete choice
modelling. London: Croom Helm/Wiley
• Domencich, T.A. & McFadden, D. (1975): Urban travel demand. A
behavioral analysis. Amsterdam u. Oxford: North Holland Publishing
Company
• Maddala, G.S. (1983): Limited-dependent and qualitative variables in
econometrics. Cambridge, U.K.: Cambridge University Press
• Maddala, G.S. (1992² (1988)): Introduction to Econometrics. New
York, N.Y.: Maxwell
• MacmillanMcFadden, D. (1974): Conditional logit analysis of
qualitative choice behavior. In: Frontiers of
econometrics. Ed. P. Zarembka,, pp. 105-142. New York: Academic
Press
• McFadden, D. (1979): Quantitative methods for analysing travel
behaviour of individuals: some recent developments. In: Hensher,
D.A.& Stopher, P.R.: (eds): Behavioural travel modelling. London:
Croom Helm, pp. 279-318
• Train, K.E. (2003): Discrete choice methods with
Simulations. Cambridge, U.K.: Cambridge University Press
• Veall, M.R. & Zimmermann, K.F. (1994): Evaluating Pseudo-R2's for
binary probit models. Quality&Quantity, 28, pp. 151- 164
13:00–14:15 Customizable tables
Kristin MacDonald (StataCorp)
/Abstract:/ Presenting results effectively is a crucial step in
statistical analyses, and creating tables is an important part of this
step. Whether you need to create a cross-tabulation, a Table 1
reporting summary statistics, a table of regression results, or a
highly customized table of results returned by multiple Stata
commands, the tables features introduced in Stata 17 and Stata 18
provide ease and flexibility for you to create, customize, and export
your tables. In this presentation, I will demonstrate how to use the
`table', `dtable', and `etable' commands to easily create a variety of
tables. I will also show how to use the `collect' suite to build and
customize tables and to create table styles with your favorite
customizations that you can apply to any tables you create in the
future. Finally, I will demonstrate how to export individual tables to
Word, Excel, LaTeX, PDF, Markdown, and HTML and how to incorporate
your tables into complete reports containing formatted text, graphs,
and other Stata results.
14:45–15:15 `geoplot': A new command to draw maps
Ben Jann (University of Bern)
/Abstract:/ `geoplot' is a new command for drawing maps from shape
files and other datasets. Multiple layers of elements such as regions,
borders, lakes, roads, labels, and symbols can be freely combined and
the look of elements (e.g. color) can be varied depending on the
values of variables. Compared to previous solutions in Stata,
`geoplot' provides more user convenience, more functionality, and more
flexibility. In this talk I will introduce the basic components of the
command and illustrate its use with examples.
15:15–15:45 `repreport': Facilitating reproducible research in Stata
Daniel Krähmer
(Ludwig-Maximilians-Universität München)
In theory, Stata provides a stable computational environment and
includes commands (i.e., `version') that are specifically designed to
ensure reproducibility. In practice, however, users often lack the
time or the knowledge to exploit this potential. Insights from an
ongoing research project on reproducibility in the social sciences
show that computational reproducibility is regularly impeded by
researchers being unaware what files (i.e., datasets, do-files),
software components (i.e., ados), infrastructure (i.e., directories),
and information (i.e., ReadMe files) is needed to enable reproduction.
This presentation introduces the new Stata command `repreport' as a
potential remedy. The command works like a log, with one key
difference: Instead of logging the entire analysis, repreport extracts
specific pieces of information pertinent to reproduction (e.g., names
and locations of datasets, ados, etc.) and compiles them into a
concise reproduction report. Furthermore, the command includes an
option for generating a full-fledged reproduction package containing
all components needed for push-button reproducibility. While
`repreport' adds little value for researchers whose workflow is
already perfectly reproducible, it constitutes a powerful tool for
those who strive to make their research in Stata more reproducible at
(almost) no additional cost.
15:45–16:15 mkproject and boilerplate: automate the beginning
Maarten L. Buis (University of Konstanz)
There is usually a set of commands that are included in every .do file
a person makes, like `clear all' or `log using'. What those commands
are can differ from person to person, but most persons have such a
standard set. Similarly, a project usually has a standard set of
directories and files. Starting a new .do file or a new project thus
involves a number of steps that could easily be automated. Automating
has the advantage of reducing the amount of work you need to
do. However, the more important advantage of automating the start of a
.do file or project is that it makes it easier to maintain your own
workflow: it is so easy to start "quick and dirty" and promise to
yourself that you will fix that "later". If the start is automated,
then you don't need to fix it.
The `mkproject' command automates the beginning of a project. It comes
with a set of templates I find useful. A template contains all the
actions (like create sub-directories, create files, run other Stata
commands) that `mkproject' will take when it creates a new
project. Since everybody's workflow is different, `mkproject' allows
users to create their own template. Similarly, the `boilerplate'
command creates a new .do file with boilerplate code in it. It comes
with a set of templates, but the user can create their own.
This talk will illustrate the use of both `mkproject' and `boilerpate'
and in particular how to create your own templates.
16:45–17:15 Data structures in Stata
Daniel C. Schneider
(Max Planck Institute for Demographic Research, Rostock)
This presentation starts out by enumerating and describing the main
data structures in Stata (e.g., data sets / frames, matrices) and Mata
(e.g., string and numeric matrices, objects like associative
arrays). It analyzes ways in which data can be represented and coerced
from one data container into another. After assessing the strengths
and limitations of existing data containers, it muses on potential
additions of new data structures and on enriching the functionality of
existing data structures and their interplay. Moreover, data
structures from other languages, such as Python lists, are described
and examined for their potential introduction into Stata / Mata. The
goal of the presentation is to stimulate a discussion among Stata
users and developers about ways in which the capabilities of Stata's
data structures could be enhanced in order to ease and open up new
possibilities for data management and analysis.
17:15–18:00 Open panel discussion with Stata developers
/Abstract/ Contribute to the Stata community by sharing your feedback
with StataCorp's developers. From feature improvements to bug fixes
and new ways to analyze data, we want to hear how Stata can be made
better for you.
Scientific Organizers
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The academic program of the conference is being organized by Johannes
Giesecke (HU Berlin), Ulrich Kohler (University of Potsdam), and
Reinhard Pollak (GESIS—Leibniz-Instute for the Social Sciences)
Logistics organizers
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The logistics are being organized by DPC Software GmbH, the
distributor of Stata in several countries including Germany, The
Netherlands, Austria, Czech Republic and Hungary
(<http://www.dpc.de>).
