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Last updated: 9 January 2008

2007 Washington, DC: Seminars on Stata

2 November 2007

Washington, DC

Residence Inn® by Marriott® Arlington Pentagon City
550 Army Navy Drive
Arlington, VA 22202


Statistical computing

Finis Welch
Economist, cofounder and board member
Statistical computing has an interesting history, spanning from the punch cards to personal computers. This talk will focus on the early developments in statistical computing and their effect on modern software.

Survival analysis

Roberto G. Gutierrez
Director of statistics
Stata’s capabilities in survival analysis are vast, ranging from simple Kaplan–Meier curves to complex models of time to failure, such as the Cox model with group-level random effects. Stata is unique in its ability to separate the tasks of survival data declaration (time variables, censoring, truncation, delayed entry, etc.) from the tasks of the actual analysis. The former are accomplished with stset; the latter, with other members of the st family of commands, providing nonparametric, semiparametric (Cox), and fully parametric analysis.

Longitudinal and panel data

David Drukker
Director of econometrics
This talk shows how to use Stata to manage and analyze longitudinal/panel data. The talk covers estimators for the parameters of linear and nonlinear fixed-effects and random-effects models, as well as dynamic models.

Analyzing complex survey data

Roberto G. Gutierrez
Director of statistics
Most of Stata’s estimation commands (including those for survival data) are equipped to handle data from complex surveys. That is, estimates and their standard errors are adjusted for the stratification, clustering (primary sampling units), and weighted sampling that can occur with survey data. The adjustment is automatic, provided that the survey aspects of the data are properly declared using svyset. Learning to use svyset is therefore the key to making full use of Stata’s features for survey data.

Extensibility of Stata

William Gould
President and head developer
You can add new features to Stata using the same tools the developers at StataCorp use, and the result can be indistinguishable from Stata’s built-in capabilities. Stata provides ways to share and find user-added features over the web. So even if you never develop new features yourself, you can easily find and use features added by others. This brief talk gives an overview of how to do this in Stata.

Time series

David Drukker
Director of econometrics
Stata has a large repertoire of tools for dealing with time series. This talk shows how to use these Stata tools to manage and analyze time-series data and discusses methods for univariate and multivariate models for stationary series and models for vector-cointegrating series.

Multilevel mixed-effects modeling

Roberto G. Gutierrez
Director of statistics
Mixed-effects models contain both fixed and random effects. Fixed effects are analogous to regression coefficients, whereas random effects are summarized according to their variance components. Random effects can also have a nested structure, resulting in a multilevel model. Stata’s commands for fitting such models are xtmixed for continuous responses, xtmelogit for binary responses, and xtmepoisson for count responses. All three commands share a similar syntax, both for model specification and for postestimation analysis.

Statistical matrix programming

William Gould
President and head developer
This talk shows how to program statistical methods in Mata, the fast matrix-programming language in Stata. After demonstrating the simple methods for putting data into matrices and basic matrix-programming techniques, the talk illustrates the power of the language by quickly implementing a Pearson chi-squared goodness-of-fit statistic, a multivariate regression estimator, and a nonlinear generalized method-of-moments estimator.

Logistics organizers

Chris Farrar, StataCorp

Gretchen Farrar, StataCorp





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