What is Synthetic Difference in Differences (SDiD) in panel data and how to run it using STATA

🎓 Synthetic Difference-in-Differences (SDID) Explained + Full Stata Tutorial | Econometrics with Dr. Minhas Struggling to understand Synthetic DiD or how to run it in Stata? This lecture breaks down one of the most powerful modern causal inference methods — step by step, from theory to hands-on Stata implementation. Synthetic Difference-in-Differences (SDID) fixes a major weakness of traditional DiD: the strict "parallel trends" assumption. Instead of assuming your control group naturally mirrors your treated group, SDID builds a custom synthetic control by optimally weighting both control units and pre-treatment time periods — giving you a more credible, robust causal estimate. 📌 What You'll Learn: ✅ What SDID is and the intuition behind it (in plain English) ✅ How SDID builds a "synthetic twin" using unit weights (ω) and time weights (λ) ✅ The origins of SDID — the 2021 American Economic Review paper by Arkhangelsky, Athey, Hirshberg, Imbens & Wager (Guido Imbens: 2021 Nobel Laureate in Economics) ✅ Traditional DiD vs. SDID — a full side-by-side comparison ✅ Why SDID is considered a superior estimator (double robustness, outlier resistance, time heterogeneity) 💻 Stata Implementation Covered: 🔹 Running the baseline sdid command 🔹 SDID with bootstrap standard errors 🔹 SDID with jackknife standard errors 🔹 SDID placebo tests for inference 🔹 Generating the SDID visual graph (treated vs. synthetic control trends with weights) Synthetic Difference-in-Differences. American Economic #Econometrics #SyntheticDiD #DifferenceInDifferences #Stata #CausalInference #AppliedEconometrics #PolicyEvaluation #PanelData #EconometricsWithDrMinhas