Enrique Pinzón holds a master’s degree in economics from the Universidad de los Andes and a Ph.D. from the University of Wisconsin–Madison. A former researcher in tax, trade, and monetary policy for the Colombian government, he is now a Senior Econometrician at StataCorp.
Topic: Dealing with Endogeneity using Stata
Stata has multiple estimators that account for endogeneity. I will briefly discuss these estimators and their assumptions. My main focus however, will be to talk about estimators that account for endogeneity that are not in Stata and can be implemented using -gsem- and -gmm-.
Ian Watson is a freelance researcher specialising in labour market analysis. He holds visiting academic positions at Macquarie University and SPRC, UNSW. He has been using Stata since 1997 and wrote the first version of tabout in 2005. His website is ianwatson.com.au.
Topic: Publication Quality Tables in Stata using tabout
Ian Watson presents a comprehensive overview of his tabout module, a Stata ado program for the batch production of publication quality tables. He explains the philosophy behind the program, touching on issues of aesthetics, functionality and reproducible research. Ian demonstrates the use of tabout to show how easy it is to produce publication quality multidimensional tables in a number of different formats and styles. tabout does not cover estimation tables. Extending tabout by incorporating more advanced Stata features---such as macros and loops---is also explained and Stata users are encouraged to extend their skills in this area.
Demetris Christodoulou is General Convenor of the research network Methodological and Empirical Advances in Financial Analysis (MEAFA) at the University of Sydney Business School. He is the architect of the MEAFA Professional Development Workshops on Quant Analysis Using Stata with extensive consulting experience on Stata in academia, industry and government..
Topic: Workflow for data visualisation: Bringing structure to graph syntax
Stata boasts an impressive graphics engine with an extensive suite of visualisation capabilities. The challenging aspect of this richness is its overwhelming syntax. The workflow for data visualisation brings structure to the vast syntax by organising graph code consistent with graphics theory.
Bill is Director of Educational Services at StataCorp, a position he has had since 2007. He gives trainings, organizes users' meetings and helps authors with their Stata usage. Prior to working at StataCorp, Bill was in the Biostatistics departments of the University of Louisville and at Johns Hopkins, where he taught Mathematical Statistics, Measure Theory and Stata. He has been a Stata user since 1992. He has a PhD in Mathematics, and is interested in reproducible research
Dynamic documents in Stata: Many routes to the same goal
Do you suffer from the tedium of moving statistical results by hand from Stata into your research documents or reports? Have you ever had the nightmare of updating a document because of changes to your analysis only to find that you missed some results? Have you ever dreamt of automating production of otherwise brain-numbing standarized reports? If so, you need dynamic documents. Dynamic documents get their name from their ability to update their statistical results when they are created, ensuring complete reproducibility and mimimal maintenance. In the world of Stata, there are quite a few user-written packages for creating dynamic documents, both from within Stata and from within other applications which call back to Stata. In this talk, I'll briefly demonstrate a few different packages, each with their own strengths. You can then choose your package, get more done, and sleep more easily at night.
As an urban/economic geographer I research a range of topics in social science including geographies of ‘happiness’, migration, population, housing and labour issues. In recent years I have drawn on unit record files to address attitudes to income inequality (drawing on the World Values Survey and International Social Science Programme surveys), internal migration (using the Statistics New Zealand Survey of the dynamics and motivations for migration), and dimensions of subjective wellbeing (using the New Zealand Quality of Life Survey).
Estimating contextual effects in social science. Multi-level modelling in Stata.
My presentation will introduce the capabilities of two commands, mixed-effects linear regression (mixed) and mixed-effects binary regression (melogit). Special attention will be paid to post-estimation and the graphical representation of intercept and slope effects including the use of margins. I will reflect how much additional information about specific behaviours I have learned by applying these applications in Stata14 in my home discipline of human geography.
Introductory training, via interactive lecturing and practical exercises, covering the basics of causal inference, including: propensity scores; Marginal Structural Models (MSMs); Causal Mediation Analysis and G-Estimation. This course assumes a basic knowledge of how to operate Stata. Participants should have completed a first course in statistics for non-specialists and at least be familiar with multivariable regression models.
Trainer: A/Prof Lyle Gurrin (Associate Professor of Biostatistics, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health)
Associate Professor Lyle C. Gurrin is a teaching and research academic in biostatistics at the Melbourne School of Population and Global Health, which he joined in 2003. Prior to that he held senior biostatistician positions in Perth at large public hospitals and associated medical research institutes devoted to women’s and children’s health. He is a Chief or Principal Investigator on several large, international, multidisciplinary studies of health and disease in both early life (infant food allergy, childhood adversity and wellbeing) and later years (hereditary haemochromatosis, men’s health). He promotes the sound practice of statistical reasoning by teaching short courses and classes of postgraduate students, and has methodological interests in the analysis of longitudinal and correlated data, and causal inference in observational studies.
Trainer: Dr Jessica Kasza (Department of Epidemiology and Preventive Medicine at Monash University)
Dr Jessica Kasza is a biostatistician in the Department of Epidemiology and Preventive Medicine at Monash University. After completing a PhD in 2010 at the University of Adelaide, she spent time at the University of Copenhagen, before returning to the University of Adelaide. She has been at Monash University since April 2013. Her research interests include causal inference methodology for the comparison of treatments, and methodology for the comparison of the performance of healthcare providers. She has a strong interest in the translation and dissemination of complex statistical methodology.
Bayesian analysis provides a theoretically more intuitive approach to statistical inference and model selection, as well as providing practical computational advantages in implementing complex statistical models. This course provides a basic overview of Bayesian statistics and its implementation in Stata. Lectures will cover an introduction to basic Bayesian models (one parameter and normal models), Bayesian implementation of linear and generalised linear models implementation, and a few examples of complex extensions (including change point models, variable selection, multivariate and multilevel regression, measurement models and structural equations, latent class and mixture models, etc.). Labs will focus on the implementation of these methods with the new Bayesian commands introduced in Stata 14 and include coverage of available user written commands, examples of direct implementation in Mata, and analysis of Bayesian simulation output produced from other programs.
Trainer: Dr Shawn Treier (School of Politics & International Relations, ANU)
Shawn Treier is a Lecturer at the School of Politics and International Relations at the Australian National University and received his Ph.D. from Stanford University. His research involves the application of Bayesian measurement models to the study of political institutions, political behaviour and public opinion, and the measurement of democracy.
His work has appeared in the American Journal of Political Science, Political Analysis, Journal of Politics, Public Opinion Quarterly, American Politics Research and Legislative Studies Quarterly.