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st: German Stata User Group Meeting - repost
This is a repost, because the email by Ulrich Kohler concerning the SUG
Programm was difficult to read for some reason.
Fifth Stata-User-Meeting in Essen**
The 5th German Stata Users Group Meeting will be held on Monday, 2nd
April 2007 in Essen at the RWI (Rheinisch-Westfälisches Institut für
Wirtschaftsforschung). 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, University of Mannheim (<[email protected]>),
John P. Haisken-DeNew, RWI Essen (<[email protected]>), and
Ulrich Kohler, WZB (<[email protected]>). 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, and Poland (http://www.dpc.de).
_Program Schedule_*__*
8:30 - 9:00 Reception
9:00 - 9:10 Welcome
John P. HaiskenDeNew, RWI Essen
_Key Notes_
9:10 - 9:50 Why should you become a Stata programmer?
Kit Baum <[email protected]>, Boston College Economics
9:50 - 10:30 Making regression tables simplified
Ben Jann <[email protected]>, ETH Zurich
_Abstract_/__/
-estout-, introduced by Jann (2005), is a useful tool for producing
regression tables from stored estimates. However, its syntax is
relatively complex and commands may turn out lengthy even for simple
tables. Furthermore, having to store the estimates beforehand can be a
bit cumbersome. To facilitate the production of regression tables, I
therefore present two new commands called -esto- and -esta-. -esto- is a
wrapper for official Stata's -estimates store- and simplifies the
storing of estimation results for tabulation. For example, -esto- does
not require the user to provide names for the stored estimation sets.
-esta-, on the other hand, is a wrapper for -estout- and simplifies
compiling nice-looking tables from the stored estimates without much
typing. Basic applications of the commands and usage of -esta- with
external software such as LaTeX, Word, or Excel will be illustrated by a
range of examples.
10:30 - 10:45 Coffee
_General Statistics_
10:45 - 11:15 Assessing the reasonableness of an imputation model
Maarten L. Buis <[email protected]>, Vrije Universiteit Amsterdam
_Abstract_/__/
Multiple imputation is a popular way of dealing with missing values
under the Missing At Random (MAR) assumption. Imputation models can
become quite complicated, for instance when the model of substantive
interest contains many interactions, or when the data originate from a
nested design. This paper will discuss two methods to assess how
plausible the results are. The first method consists of comparing the
point estimates obtained by multiple imputation with point estimates
obtained by another method for controlling for bias due to missing data.
Second the changes in standard error between the model that ignores the
missing cases and the multiple imputation model are decomposed into
three components: changes due to changes in 'sample size', changes due
to uncertainty in the imputation model used in multiple imputation, and
changes due to changes in the estimates that underlie the standard
error. This decomposition helps in assessing the reasonableness of the
change in standard error. These two methods will be illustrated with two
new user written Stata commands.
11:15 - 11:45 The influence of categorizing survival time on parameter
estimates in a Cox model
Anika Buchholz, Willi Sauerbrei, Patrick Royston
University of Freiburg, University Medical Center Freiburg, MRC Clinical
Trials Unit, London
_Abstract _/__/
With longer follow-up times the proportional hazards assumption is
questionable in the Cox model. Cox suggested to include an interaction
between a covariate and a function of time. To estimate such a function
in Stata a substantial enlargement of the data is required. This may
cause severe computational problems. We will consider categorizing
survival time, which raises issues as to the number of cutpoints, their
position, the increased number of ties and the loss of information, to
handle this problem. Sauerbrei et al. (2007) proposed a new selection
procedure to model potential time-varying effects. They investigate a
large data set (N=2982) with 20 years follow-up, for which the Stata
command stsplit creates about 2.2 million records. Categorizing the data
in 6 month intervals gives 35747 records. We will systematically
investigate the influence of the length of categorization intervals and
the four methods of handling ties in Stata. The results of our
categorization approach are promising, showing a sensible way to handle
time-varying effects even in simulation studies.
References: Sauerbrei, W., Royston, P. and Look, M. (2007). A new
proposal for multivariable modelling of time-varying effects in
survival data based on fractional polynomial time-transformation.
Biometrical Journal, in press
11:45 - 12:15 Oaxaca/Blinder Decompositions for Non-Linear Models
Matthias Sinning <[email protected]> and Markus Hahn
<[email protected]>, RWI Essen, University of Bochum
_Abstract_/__/
This paper describes the estimation of a general Blinder-Oaxaca
decomposition of the mean outcome differential of linear and non-linear
regression models. Departing from this general model, it is shown how it
can be applied to different models with discrete and limited dependent
variables.
12:15 - 13:15 Lunch
13:15 - 13:45 Estimating Double-Hurdle Models with Dependent Errors and
Heteroscedasticity"
Julian A. Fennema <[email protected]>, Heriot-Watt University, Edinburgh
_Abstract_/__/
This paper describes the estimation of the parameters of a double hurdle
model in Stata. It is shown that the independent double-hurdle model can
be estimated using a combination of existing commands. Likelihood
evaluators to be used with Stata's ml facilities are derived to
illustrate how to fit independent and dependent inverse hyperbolic sine
double-hurdle models with heteroscedasticity."
_Distributions_
13:45 - 14:15 Measuring Richness
Andreas Peichl <[email protected]> University of Cologne
_Abstract_/__/
In this paper, we describe richness, a Stata program for the calculation
of richness indices. Peichl, Schaefer and Scheicher (2006) propose a new
class of richness measures to contribute to the debate how to deal with
the financing problems that European welfare states face as a result of
global economic competition. In contrast to the often used headcount,
these new measures are sensitive to changes in rich persons’ income.
This allows for a more sophisticated analysis of richness, namely the
question whether the gap between rich and poor is widening. We propose
to use our new measures in addition to the headcount index for a more
comprehensive analysis of richness.
14:15 - 14:45 Robust income distribution analysis
Philippe Van Kerm <[email protected]>, CEPS/INSTEAD, Luxembourg
_Abstract_/__/
Extreme data are known to be highly influential when measuring income
inequality from micro-data. Similarly, Lorenz curves and dominance
criteria are very sensitive to data contamination in the tails of the
distribution. In this presentation, I intend to introduce a set of
user-written packages that implement robust statistical methods for
income distribution analysis. These methods are based on the estimation
of parametric models (Pareto, Singh-Maddala) using "optimal B-robust"
estimators rather than maximum likelihood. Empirical examples show how
robust inequality estimates and dominance checks can be derived from
these models.
14:45 - 15:00 Coffee
_Data Management_
15:00 - 15:25 PanelWhiz: A Stata Interface for Large Scale Panel Data Sets
John P. Haisken-DeNew <[email protected]>, RWI Essen
_Abstract_/__/
This paper outlines a panel data retrieval program written for Stata/SE
or better, which allows easier accessing of the household panel data
sets. Using a drop-down menu system, the researcher selects variables
from any and all available years of the panel. The data is automatically
retrieved and merged to form a "long file", which can be directly used
by the Stata panel estimators. The system implements modular data
cleaning programs called "plugins". Yearly updates to the data
retrievals can be made automatically. Projects can be stored in
libraries allowing modular administration and appending. PanelWhiz is
available for SOEP, IAB-Betriebspanel, HILDA, CPS-NBER, CPS-CEPR. Other
popular data sets will be supported soon.
15:25 - 15:50 PanelWhiz Plugins: Automatic Vector-Oriented Data Cleaning
for Large Scale Panel Data Sets
Markus Hahn <[email protected]>, RWI Essen and University of Bochum
_Abstract_/__/
PanelWhiz "plugins" are modular data cleaning programs for specific
items in PanelWhiz. Each plugin is designed to recode, deflate, change
existing variables being extracted in a panel-data retrieval.
Furthermore new variables can be generated on the fly. The PanelWhiz
plugin system is a "macro language" that uses new-style dialog boxes and
Stata's modularized "class" system, allowing a vector orientation for
data cleaning. The PanelWhiz plugins can even be generated using a
PanelWhiz plugin front-end, allowing users to create plugins but not
have to write Stata code themselves. The system is set up to allow data
cleaning of ANY PanelWhiz supported data set.
15:50 - 16:15 A model for transferring variables between different
data-sets based on imputation of individual scores
Bojan Todosijevic <[email protected]>, University of Twente
_Abstract_/__/
It is an often encountered problem that variables of interest are
scattered in different data sets. Given the two methodologically similar
surveys, a question not asked in one survey could be seen as a special
case of missing data problem (Gelman et al., 1998). The paper presents a
model for transferring variables between different data-sets applying
the procedures for multiple imputation of missing values. The
feasibility of this approach was assessed using two Dutch surveys:
Social and Cultural Developments in The Netherlands (SOCON 2000) and the
Dutch Election Study (NKO 2002). An imputation model for the left-right
ideological self-placement was developed based on the SOCON survey. In
the next step, left-right scores were imputed to the respondents from
the NKO study. The outcome of the imputation was evaluated, first, by
comparing the imputed variables with the left-right scores collected in
three waves of the NKO study. Second, the imputed and the original NKO
left-right variables are compared in terms of their associations with a
broad set of attitudinal variables from the NKO data set. The results
show that one would reach similar conclusions using the original or
imputed variable, albeit with the increased risk of making Type II errors.
16:15 - 16:30 Coffee
16:30 - 17:00 Two Issues on Remote data access
Peter Jacobebbinghaus <[email protected]>, IAB
At the Research Data Centre of the BA at the IAB, researchers can send
in Stata programs to be processed there with the log files sent back to
them after a disclosure limitation review. This method of data access is
called remote data access and the reason we do this is data
confidentiality. Remote data access has two non-standard requirements:
Efficient use of the computer resources and automation of parts of the
disclosure limitation review. I would like to talk about how we deal
with these requirements and discuss ways to improve them.
17:00 - 17:30 Report to the Users
Bill Rising, StataCorp
17:30 - 18:00 Wishes and Grumbles
Participants are asked to travel at their own expense. There will be a
small conference fee to cover costs for coffee, teas, and luncheons.
There will also be an optional informal meal at a restaurant in Essen on
Monday evening at additional cost.
You can enroll by contacting Anke Mrosek by email or by writing, phoning, or faxing to
Anke Mrosek
Dittrich & Partner Consulting GmbH
Kieler Str. 17
42697 Solingen
Tel: +49 (0) 212 260 66-24
Fax:+49 (0) 212 260 66 -66
[email protected].
We look forward to seeing you in Essen on April 2nd where you can help us to make this an exciting and interesting event.
The conference venue is:
RWI Essen
Hohenzollernstr. 1-3
45128 Essen
(see http://www.rwi-essen.de)
Johannes Giesecke, John P. Haisken-DeNew, Ulrich Kohler
--
Dr. Johannes Giesecke
Universität Mannheim
Fakultät für Sozialwissenschaften
Lehrstuhl für Methoden der empirischen Sozialforschung und angewandte Soziologie
Seminargebäude A5, Bauteil A
68159 Mannheim
Tel.: (0621) 181 2045
Fax: (0621) 181 2048
[email protected]
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