The classic two-group, two-period DiD is insufficient for modern staggered treatment designs. Stata 18’s new did command implements the estimator, which is robust to treatment effect heterogeneity across time and groups. It automatically handles "not-yet-treated" vs. "never-treated" control groups.
The synth command now includes placebo tests in the main syntax and produces publication-ready graphs of treatment vs. synthetic control with gap plots. Stata 18
All Bayesian commands benefit from improved samplers, which converge faster than traditional MCMC for multimodal posterior distributions. The classic two-group, two-period DiD is insufficient for
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? "never-treated" control groups
Stata 18 is not merely an incremental update. It solidifies Stata’s position as a leader in applied econometrics and biostatistics by integrating state-of-the-art causal methods, Bayesian techniques, and modern data engineering formats (Parquet) into a point-and-click and command-driven environment. While its licensing model remains premium, the addition of StataNow ensures that subscribers receive continuous value. For users who prioritize reproducibility, peer-reviewed statistical methods, and a gentle learning curve, Stata 18 is a compelling and robust choice.
void myreg::new(real matrix X, real matrix y) this.X = X this.y = y