Fix PT-All baseline misstatement: period 1 is not a valid baseline

The tutorial incorrectly stated "periods 1, 2, 3 can all serve as valid
baselines". Period 1 is the universal Y_1 reference and is excluded per
REGISTRY.md and efficient_did_weights.py. Corrected to "periods 2 and 3".

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: igerber

docs/tutorials/15_efficient_did.ipynb

@@ -63,7 +63,7 @@
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-   "source": "## What Makes EDiD Different?\n\nConsider a staggered adoption design with cohorts treated at periods 3, 5, and 7, plus a never-treated group. To estimate ATT(g=5, t=6), **Callaway-Sant'Anna** uses a single 2x2 comparison:\n\n> *Compare the outcome change from period 4 to 6 for cohort 5 versus the never-treated group.*\n\nBut under **PT-All** (parallel trends across all pre-treatment periods), there are *additional* valid comparisons. Cohort 7 is also untreated at period 6, so it can serve as a comparison group too. And periods 1, 2, 3 can all serve as valid baselines, not just period 4.\n\nEach of these comparisons provides an unbiased estimate of ATT(g=5, t=6), but with different variances. **EDiD finds the optimal linear combination** --- the one that minimizes variance --- by computing the inverse covariance matrix of these \"generated outcomes\" (the paper calls this $\\Omega^*$).\n\nThe result: **matching post-treatment ATT(g,t) with CS under PT-Post**, but **tighter standard errors under PT-All** because EDiD exploits the overidentification.\n\n> **Key equation (for the curious):** The efficient weight vector is $w^* = \\frac{\\mathbf{1}' \\Omega^{*-1}}{\\mathbf{1}' \\Omega^{*-1} \\mathbf{1}}$, where $\\Omega^*$ is the covariance matrix of the generated outcomes across all valid (comparison group, baseline) pairs. This is the classic GLS optimal weighting. See REGISTRY.md or the paper for full derivations."
+   "source": "## What Makes EDiD Different?\n\nConsider a staggered adoption design with cohorts treated at periods 3, 5, and 7, plus a never-treated group. To estimate ATT(g=5, t=6), **Callaway-Sant'Anna** uses a single 2x2 comparison:\n\n> *Compare the outcome change from period 4 to 6 for cohort 5 versus the never-treated group.*\n\nBut under **PT-All** (parallel trends across all pre-treatment periods), there are *additional* valid comparisons. Cohort 7 is also untreated at period 6, so it can serve as a comparison group too. And periods 2 and 3 can serve as additional valid baselines beyond CS's default period 4. (Period 1 is excluded --- it is the fixed $Y_1$ reference used in every comparison's differencing, so using it as a baseline adds no information.)\n\nEach of these comparisons provides an unbiased estimate of ATT(g=5, t=6), but with different variances. **EDiD finds the optimal linear combination** --- the one that minimizes variance --- by computing the inverse covariance matrix of these \"generated outcomes\" (the paper calls this $\\Omega^*$).\n\nThe result: **matching post-treatment ATT(g,t) with CS under PT-Post**, but **tighter standard errors under PT-All** because EDiD exploits the overidentification.\n\n> **Key equation (for the curious):** The efficient weight vector is $w^* = \\frac{\\mathbf{1}' \\Omega^{*-1}}{\\mathbf{1}' \\Omega^{*-1} \\mathbf{1}}$, where $\\Omega^*$ is the covariance matrix of the generated outcomes across all valid (comparison group, baseline) pairs. This is the classic GLS optimal weighting. See REGISTRY.md or the paper for full derivations."
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