The new eteffects command allows users to estimate treatment effects while controlling for unobserved panel-level effects. Unlike standard models that might be biased due to time-invariant unobserved heterogeneity, this command implements endogenous treatment-effects models for panel data.
Conversely, in a Jupyter Notebook or Python script, you can initialize a Stata session: Stata 18
import stata
stata.run("regress mpg weight")
stata.get_return("r(table)")
This is revolutionary for reproducible research pipelines that combine Stata’s robust survey methods with Python’s deep learning. The new eteffects command allows users to estimate
Why it matters: Many organizations have legacy Stata code and modern Python needs. Stata 18 bridges that gap without requiring a full rewrite. Why it matters : Many organizations have legacy
Meta-analysts rejoice. bayes: meta allows you to combine evidence from multiple studies with full control over prior distributions. This is particularly useful in pharmaceutical research, where regulatory agencies increasingly expect Bayesian synthesis of evidence.
Why it matters: The Bayes paradigm is moving from academic statistics to applied research. Stata 18 lowers the barrier to entry with intuitive syntax, graphical convergence diagnostics (trace plots, autocorrelation), and easy model comparison using Bayes factors and DIC.