|Venue:||University of Sydney
|Dates:||June 29–July 3, 2015|
You may attend any one or any combination of the following days:
Monday, June 29:
Working efficiently with Stata by Demetris Christodoulou, MEAFA General Convener
This day assumes no previous knowledge of Stata 14. It describes the environment of Stata and its core syntactic features. It demonstrates ways of working efficiently with Stata, including the use of logs and do-files. It discusses key principles and presents tools for developing work that is reproducible and verifiable. It explains how Stata understands data and related precision and the limitations of working with large data. The day is of interest to those who are new to Stata 14 or have limited experience with earlier versions of Stata. It is also useful to more experienced users who wish to attain a more structural understanding of Stata from first principles.
Tuesday, June 30:
Introduction to programming by Demetris Christodoulou, MEAFA General Convener
This day assumes working knowledge of Stata 14 but no knowledge of programming with Stata or any other software. By the end of this day, you will be able to produce efficient, tractable and automated routines for data management, conduct statistical analysis and estimation, and create tables and graphs. The day covers key programming tools (saved results, stored results, macros, scalars, loops) and the fundamentals of building your own commands in Stata (programs or ado-files). This day is appropriate to those who wish to attain a deeper knowledge of Stata and achieve the aforementioned attributes in their work. This day assumes knowledge of the material presented in Day 1.
Wednesday–Thursday, July 1–2:
Treatment effects by David Drukker, Director of Econometrics, StataCorp LP
These two days discuss available methods in Stata that use observational data to estimate treatment effects and average treatment effects on the treated subjects (patients, companies, other “treated” entities). After presenting the potential-outcome framework and the parameters estimated, we discuss six estimators: (1) the regression-adjustment estimator, (2) the inverse-probability-weighted (IPW) estimator, (3) the augmented IPW estimator, (4) the IPW regression-adjustment estimator, (5) the nearest-neighbor matching estimator, and (6) the propensity-score matching estimator. We then explain how these estimators are used to estimate average treatment effects for binary, count, and survival-time outcomes. Next, we cover the double-robustness property of the augmented IPW and IPW regression-adjustment estimators, followed by multivalued treatments. We then discuss interpreting and estimating quantile treatment effects. We finish by covering some estimators when the treatment is endogenous. All topics are discussed using a combination of math and applications with real datasets. These two days assume working knowledge of Stata and, at minimum, of the material presented in Day 1.
Friday, July 3:
Programming estimation commands by David Drukker, Director of Econometrics, StataCorp LP
This day demonstrates how to write an estimation command using Stata. After providing an introduction to the Stata ado-file language, we cover the development of basic and advanced programs. Next, we cover an introduction to Mata (the byte-compiled matrix language that is part of Stata). Then, we show how to implement linear and nonlinear statistical estimation models using Stata/Mata programs. Finally, we apply Monte Carlo simulations to test the implementation. This day assumes reasonable knowledge of the material presented in Days 1–2, but no prior Mata programming experience is required.
For more information, including a detailed program, or to register, visit sydney.edu.au/business/research/meafa/activities/pdworkshop/june2015.