Oceania Stata Conference 2022 – Virtual

10 February 2022

Oceania Stata Conference 2024

Oceania Stata Conference Presentations

John P de New, The University of Melbourne and The Melbourne Institute

Marginal Unit Interpretation of Unconditional Quantile Regression and Recentered Influence Functions by Fernando Rios-Avila and John P de New

Unconditional quantile regressions, as introduced by Firpo, Fortin, and Lemieux (2009), is a special case of Recentered Influence Functions (RIF) Regressions that can be used to relate how small changes in the distributions of explanatory variables affect an unconditional distribution statistic of interest. While there is general understanding with regards to the analysis and interpretation of changes in continuous variables, difficulties remain when understanding and interpreting dummies that describe qualitative characteristics. On the one hand, the implicit inter-relationship among binary variables is usually ignored, and on the other hand, that standard RIF regressions only capture effects at the margins, not distributional treatment effects. This paper suggests the use of restricted least squares regression analysis (Haisken-DeNew and Schmidt, 1997), combined with the use of centered continuous variables, and re- scaling, to isolate the constant cleanly as the distributional statistic of interest and better interpret the results of RIF-regressions in the presence of dummy variables. A Stata ADO implements this methodology.
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James Hurley, University of Melbourne and Ballarat Health Services

Herd effects of topical antibiotic prophylaxis among ICU patients. Simulating a cluster randomized trial using published studies

This presentation extends from a presentation to the STATA 2021 on line conference [Structural equation modeling the ICU patient microbiome and risk of bacteremia; https://www.stata.com/meeting/us21/]. In this presentation will demonstrate how Stata has been useful in data analysis not just in providing results but also visualizing the results using graphics.
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Stephen Jenkins, London School of Economics and Political Science

Finite mixture models for linked survey and administrative data: estimation and post-estimation, Stephen Jenkins and Fernando Rios-Avila

This talk is based on our paper: IZA Discussion Paper 14404, with Stata programs at SSC (ssc describe ky_fit). For our substantive application to UK data: see IZA Discussion Paper 14405.
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Jan Kabatek, The University of Melbourne

Efficient commands for data visualization in large datasets

In this presentation I discuss the series of custom Stata commands (PLOT) for efficient visualization of information. The PLOT family of commands is particularly useful for visual analyses of admin data, enabling users to produce a variety of highly customizable plots in a fraction of time required by Stata's native graphing commands. Benchmarking of the graphs show that PLOT commands can prove more than 100-times faster than the native commands, with the efficiency gains growing with sample size.
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Asjad Naqvi, Vienna University of Economics and Business (WU)

Advanced Visualizations with Stata II: Complex data structures

This presentation will showcase a new suite Stata graphs that can be utilized to visualize complex data structures (hierarchical, networks, relational).
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Jeff Pitblado, StataCorp

Custom Estimation Tables

In this presentation, I build custom tables from one or more estimation commands. I demonstrate how to add custom labels for significant coefficients and how to make targeted style edits to cells in the table. I conclude with a simple workflow for you to build your own custom tables from estimation commands.
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Mathias Sinning, Australian National University

Increasing computational speed by combining Stata and Python

In this presentation, I will discuss ways to increase Stata’s computational speed by combining it with Python. Examples include the comparison of Stata’s ktau command, which requires a calculation time of O(n2) to obtain Kendall’s tau between two variables with sample size n, to my own user-written Stata command py_ktau, which implements Python’s algorithm to compute Kendall’s tau with a calculation time of O(nlog(n)). I will also discuss how to use Stata’s profiler and timer commands and provide examples for how to set a seed in Python when running Python code in Stata. Time permitting, I will talk about applications to the search for random permutations.
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Laura Whiting, Survey Design and Analysis Services

Using dialog boxes in Stata to collect user parameters for use in a Stata user written command

Stata users often share do / ado files making them available for other users to run the exact same code. However, it is often left for the receiving user to update any specific parameters to make the code work for their needs. In this presentation we show how we created a Stata program which incorporated Python and then built a Stata dialog box around it to allow the end user to be able to quickly and easily update the parameters.
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