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From |
Ulrich Kohler <kohler@wzb.eu> |

To |
statalist <statalist@hsphsun2.harvard.edu> |

Subject |
st: 6th German Stata Users' Group Meeting - Final Announcement andProgram |

Date |
Mon, 19 May 2008 16:24:08 +0200 |

6. German Stata User's Group Meeting ==================================== The 6th German Stata Users Group Meeting will be held on Friday, 27th June 2008 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 (johannes.giesecke@wzb.eu), and Ulrich Kohler (kohler@wzb.eu), both at the WZB. 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 ----------------- 9:45 - 10:15 Registration 10:15 - 10:30 Welcome 10:30 - 11:15 Using instrumental variables techniques in economics and finance Christopher F. Baum, kitbaum@mac.com Boston College Economics and DIW Berlin I will discuss the usefulness of instrumental variables (IV) techniques in addressing research questions in economics and finance. IV methods provide workable solutions to problems of endogeneity, measurement error and proxy variables, but are easily misused. A wide array of diagnostic techniques that should be employed to validate the use of IV in a particular context will be presented. I will also discuss the advantages of employing the Generalized Method of Moments form of IV (IV-GMM) and the Continuously Updated Estimator (GMM-CUE), and display some newly developed code that efficiently employs Stata's Mata programming language to implement the GMM-CUE estimator. 11:15 - 12:00 Ordinal regression models: Problems, solutions, and problems with the solutions Richard Williams, Notre Dame Dept of Sociology Ordered logit/probit models are among the most popular ordinal regression techniques. These models often have serious problems, however. The proportional odds/parallel lines assumptions made by these methods are often violated. Further, because of the way these models are identified, they have many of the same limitations as are encountered when analyzing standardized coefficients in OLS regression, e.g. interaction terms and cross-population comparisons of effects can be highly misleading. This paper shows how generalized ordered logit/probit models (estimated via gologit2) and heterogeneous choice/location scale models (estimated via oglm) can often address these concerns in ways that are more parsimonious and easier to interpret than is the case with other suggested alternatives. At the same time, the paper cautions that these methods sometimes raise their own concerns that researchers need to be aware of and know how to deal with. First, mis-specified models can create worse problems than the ones these methods were designed to solve. Second, estimates are sometimes implausible, suggesting that the data are being spread too thin and/or yet another method is needed. Third, multiple and very different interpretations of the same results are often possible and plausible. Guidelines for identifying and dealing with each of these problems are presented. 12:00 - 13:00 Lunch 13:00 - 13:30 Charts for comparing results between many categories Ulrich kohler, kohler@wzb.eu WZB Charts are useful tools for comparing a statistic between groups defined by a categorical variable with many different categories. It has turned out from a number of postings on Statalist that Stata's standard implementation of these graphs with -graph dot- and -graph bar- often limits the the users in their ambition to design such graph. However, in most cases users' design wishes can be satisfied by reverting to the low level command -graph twoway-. This tutorial talk demonstrates the construction of charts with -graph twoway-. It starts by re-constructing a simple bar-chart with -graph twoway- and than moves to a number of extensions that are possible when using -graph twoway-. I will illustrate some trickery with stored results and local macros, as well as a number of useful user written programs. 13:30 - 14:15 Graph Editing Vince Wiggins, vwiggins@stata.com StataCorp We will take a quick tour of the Graph Editor, covering the basic concepts: adding text, lines, and markers; changing the defaults for added objects; changing properties; working quickly by combining the contextual toolbars with the more complete object dialogs; and using the object browser effectively. Leveraging these concepts, we'll discuss how and when to use the grid editor and techniques for combined and by-graphs. Finally, we will look at some tricks and features that aren't apparent at first blush. 14:15 - 14:45 Relative Distribution Methods in Stata Ben Jann, jannb@ethz.ch ETH Zurich The concept of the relative density seems like a fruitful nonparametric approach to studying distributional differences between groups (Handcock and Morris 1999), yet it appears that the technique has gone more or less unnoticed in applied social science research. A scarcity of canned software might be one of the reasons the method is underutilized. Therefore, I present a new Stata command called -reldist- to plot the relative density, decompose distributional differences into location and shape effects, and compute relative distribution summary measures. The command is illustrated by an application comparing earnings by sex. Reference: Handcock, Mark S., and Martina Morris (1999). Relative Distribution Methods in the Social Sciences. New York: Springer. 14:45 - 15:00 Coffee 15:00 - 15:30 Direct and Indirect effects in a logit model Maarten Buis, M.Buis@fsw.vu.nl Vrije Universiteit Amsterdam In this presentation I discuss a method by Erikson et al. (2005) for decomposing a total effect in a logit model into direct and indirect effects and proposes a generalization of this method. Consider an example where social class has an indirect effect on attending college through academic performance in high school. The indirect effect is obtained by comparing the proportion of lower class students that attend college with the counterfactual proportion of lower class students if they had the distribution of performance of the higher class students. This captures the association between class and attending college due to differences in performance, i.e. the indirect effect. The direct effect of class is obtained by comparing the proportion of higher class students with the counterfactual proportion of lower class students if they had the same distribution of performance as the higher class students. This way the variable performance is kept constant, and thus result in the direct effect. If these comparisons are carried out in the form of log odds ratios than the total effect will equal the sum of the direct and indirect effects. In its original form this method assumes that the variable through which the indirect effect occurs is normally distributed. In this article the method is generalized by allowing this variable to have any distribution, which has the added advantage of simplifying the method. 15:30 - 16:00 Multiple Imputation using ICE: A Simulation Study on a Binary Response Jochen Hardt, jochen.hardt@gmx.de and Kai Görgen (Mathematical Statistics, Chalmers University, Göteborg, Sweden; Masters Programme, Bernstein Center for Computational Neurocience, Berlin) Background: Various methods for multiple imputations of missing values are available in statistical software. They have been shown to work well when small proportions of missings were to be imputed. However, some researchers start to impute large proportions of missings. Method: A simulation using ice was performed on datasets of 50/100/200/400 cases and 4/11/25 variables. A varying proportion of data (3 – 63 %) were set missing completely at random and subsequently substituted by multiple imputation. Results: (1) It is shown when and how the algorithm breaks down by decreasing n of cases and increasing number of variables in the model. (2) Some unexpected results are demonstrated, i.e. flawed coefficients. (3) Compared to the second programme that performs multiple imputations by chained equations, i.e. “mice” in “R”, the stata programme “ice” results in a slightly higher precision of the estimates by generally very similar features of the programmes. Conclusion: The imputation of missings by chained equations is a useful tool for imputing small to moderate proportions of missings. The replacement of larger amounts however can be critical. 16:00 - 16:30 Using Stata for a memory saving fixed effects estimation of the three-way error component model Thomas Cornelissen, cornelissen@ewifo.uni-hannover.de Leibniz Universität Hannover Researchers trying to estimate tens or houndreds of thousands of fixed effects for two or more groups (workers and firms, pupils, teachers and schools,etc.) in data sets with high numbers of observations are often limited by the size of the computer memory available. Such a model is commonly estimated by sweeping out one of the effects by the fixed effects transformation (time-demeaning) and by including the remaining effects as dummy variables. If K is the number of fixed effects to be included as dummy variables and N is the number of observations, then the design matrix is of dimension N x K (neglecting any remaining right-hand side regressors). The time-demeaned dummies have to be stored as “float” variables consuming 8 bytes per cell in Stata. For example, with 2 million observations (N) and 10 thousand fixed effects (K), the memory requirement would be 160 gigabytes. This paper describes how the memory requirement can be reduced to store only a K x K matrix, which in the given example reduces the memory requirement to below 1 gigabyte. The paper also describes the Stata program felsdvreg.ado which implements the method in Mata. Besides implementing the memory-saving estimation method, the program also takes care of checking the identification of the effects and provides useful summary statistics. 16:30 - 16:45 Coffee 16:45 - 17:30 Report to the users Alan Riley, riley@stata.com StataCorp 17:30 - 18:00 Wishes and grumbles 18:00 End of the meeting Participants are asked to travel at their own expense. There will be a small conference fee to cover costs for coffee, teas, and luncheons (35 Euro; Students: 15 Euro). There will also be an optional informal meal at a restaurant in Berlin on Friday 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 anke.mrosek@dpc.de. We look forward to seeing you in Berlin on Friday the 27th where you can help us to make this an exciting and interesting event. The conference venue is: Wissenschaftszentrum Berlin Reichpietschufer 50 10785 Berlin (see http://www.wzb.eu) Johannes Giesecke, Ulrich Kohler -- kohler@wzb.eu 030 25491-361 * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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