The collect command, which allows users to capture, combine, and format results from multiple commands, has been enhanced for better performance and flexibility in producing custom tables and graphs. 3. Improved Integration: PyStata and Data Handling
command, specifically designed to create publication-quality tables of descriptive statistics—often called "Table 1" in research papers. : Access it via
The integration between (introduced in version 16/17) is even tighter in Stata 18. You can call Python libraries like Pandas, NumPy, or Scikit-learn directly from the Stata interface and pass data back and forth in memory. This "best of both worlds" approach allows you to use Stata for econometrics while leveraging Python for machine learning or web scraping. Conclusion: Is Stata 18 Worth the Upgrade? Stata 18
Data presentation has historically been a manual, time-consuming process in Stata. Stata 18 introduces the highly anticipated tables command, a flexible system for creating publication-quality tables.
Modern data science increasingly relies on Python’s ecosystem of machine learning and data visualization libraries. Stata 18’s Python integration allows you to leverage these capabilities while maintaining access to Stata’s statistical expertise. The integration works in both directions: you can call Python from within Stata, and you can call Stata from within Python. The collect command, which allows users to capture,
Which and Stata edition (BE, SE, or MP) do you currently use?
| Feature | Description | Use Case | |---------|-------------|----------| | | Run .md files as dynamic documents, code chunks | Reproducible reports without separate tools | | frame meta-data | frame put + frame rename + frame drop _all | Safer multi-frame workflows | | pystata integration | Run Python in Stata, exchange data via sfi module | ML, string processing, APIs | | Bayesian multilevel | bayes: melogit etc. | Hierarchical models with full Bayes | | Local projections | lpirf for IRFs, lp for general local projections | Panel time series, Jorda’s method | | dtable | Descriptive table with built-in balancing tests | Publication-ready Table 1 | | collect enhancements | collect layout + collect style | Custom table/figure templates | : Access it via The integration between (introduced
The software’s continued commitment to reproducibility—through integrated version control, backward compatibility, and dynamic reporting—ensures that analyses conducted in Stata 18 will remain valid and reproducible for years to come. At the same time, the evolution toward continuous releases with StataNow signals that StataCorp is adapting to modern expectations of software delivery.
For users working with billions of observations, Stata/MP 18 unlocks deeper multi-threading capabilities. Feature Area Speed Improvement (Stata 17 vs Stata 18) Core Optimization Type 2x – 4x Faster Parallel Radix Sort Collapsing ( collapse ) 1.5x – 3x Faster Optimized Multi-Threaded Hashing Reshaping ( reshape ) Up to 2x Faster Memory Mapping Redesign 🛠️ Integration with Python and R
In previous versions, survival analysis (time-to-event data) required exact failure times or right-censoring. However, in many medical and biological studies, the exact time of an event is unknown; researchers only know it occurred between two examination points (e.g., a tumor was absent at Visit 1 but present at Visit 2).