Fix tutorial 07: use correct post_periods for event study

The notebook was passing all periods (pre and post) as post_periods
to MultiPeriodDiD, causing HonestDiD to fail with "No pre-period
effects found" since the results had no pre-period classification.

Fix: pass only actual post-treatment periods [5-9] to post_periods.
MultiPeriodDiD automatically estimates pre-period coefficients for
the event study, and HonestDiD can now correctly identify them.

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

Author: igerber

docs/tutorials/07_pretrends_power.ipynb

@@ -131,28 +131,7 @@
    "id": "cell-6",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Fit event study with ALL periods (pre and post) relative to reference period\n",
-    "# For pre-trends power analysis, we need coefficients for pre-periods too\n",
-    "mp_did = MultiPeriodDiD()\n",
-    "\n",
-    "# Use period 4 as the reference period (last pre-period, excluded from estimation)\n",
-    "# Estimate coefficients for all other periods: 0, 1, 2, 3 (pre) and 5, 6, 7, 8, 9 (post)\n",
-    "all_estimation_periods = [0, 1, 2, 3, 5, 6, 7, 8, 9]  # All except reference period 4\n",
-    "\n",
-    "event_results = mp_did.fit(\n",
-    "    df,\n",
-    "    outcome='outcome',\n",
-    "    treatment='treated',\n",
-    "    time='period',\n",
-    "    post_periods=all_estimation_periods  # Include all periods for full event study\n",
-    ")\n",
-    "\n",
-    "# Note: For standard DiD analysis, we'd normally use post_periods=[5,6,7,8,9]\n",
-    "# But for pre-trends power analysis, we need pre-period coefficients too\n",
-    "\n",
-    "print(event_results.summary())"
-   ]
+   "source": "# Fit event study with ALL periods (pre and post) relative to reference period\n# For pre-trends power analysis, we need coefficients for pre-periods too\nmp_did = MultiPeriodDiD()\n\n# Use period 4 as the reference period (last pre-period, excluded from estimation)\n# Specify post_periods as the actual post-treatment periods; MultiPeriodDiD\n# automatically estimates pre-period coefficients for the event study.\nevent_results = mp_did.fit(\n    df,\n    outcome='outcome',\n    treatment='treated',\n    time='period',\n    post_periods=[5, 6, 7, 8, 9]\n)\n\nprint(event_results.summary())"
   },
   {
    "cell_type": "code",
@@ -199,24 +178,7 @@
    "id": "cell-10",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Create a PreTrendsPower object\n",
-    "pt = PreTrendsPower(\n",
-    "    alpha=0.05,      # Significance level for pre-trends test\n",
-    "    power=0.80,      # Target power for MDV calculation\n",
-    "    violation_type='linear'  # Type of violation to consider\n",
-    ")\n",
-    "\n",
-    "# Define the actual pre-treatment periods (those before treatment starts at period 5)\n",
-    "# These are the periods we want to analyze for pre-trends power\n",
-    "pre_treatment_periods = [0, 1, 2, 3]\n",
-    "\n",
-    "# Fit to the event study results, specifying which periods are pre-treatment\n",
-    "# This is needed because we estimated all periods as post_periods in the event study\n",
-    "pt_results = pt.fit(event_results, pre_periods=pre_treatment_periods)\n",
-    "\n",
-    "print(pt_results.summary())"
-   ]
+   "source": "# Create a PreTrendsPower object\npt = PreTrendsPower(\n    alpha=0.05,      # Significance level for pre-trends test\n    power=0.80,      # Target power for MDV calculation\n    violation_type='linear'  # Type of violation to consider\n)\n\n# Define the actual pre-treatment periods (those before treatment starts at period 5)\n# These are the periods we want to analyze for pre-trends power\npre_treatment_periods = [0, 1, 2, 3]\n\n# Fit to the event study results, specifying which periods are pre-treatment\npt_results = pt.fit(event_results, pre_periods=pre_treatment_periods)\n\nprint(pt_results.summary())"
   },
   {
    "cell_type": "markdown",
@@ -558,46 +520,7 @@
    "id": "cell-30",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# Typical workflow for pre-trends power analysis\n",
-    "\n",
-    "# Step 1: Estimate event study with ALL periods (pre and post) relative to reference\n",
-    "# For pre-trends power analysis, we need pre-period coefficients\n",
-    "mp_did = MultiPeriodDiD()\n",
-    "\n",
-    "# Reference period is 4 (last pre-period)\n",
-    "# Estimate coefficients for periods 0, 1, 2, 3 (pre) and 5, 6, 7, 8, 9 (post)\n",
-    "all_estimation_periods = [0, 1, 2, 3, 5, 6, 7, 8, 9]\n",
-    "pre_treatment_periods = [0, 1, 2, 3]  # Define which are pre-treatment\n",
-    "\n",
-    "results = mp_did.fit(\n",
-    "    df, \n",
-    "    outcome='outcome',\n",
-    "    treatment='treated', \n",
-    "    time='period',\n",
-    "    post_periods=all_estimation_periods\n",
-    ")\n",
-    "\n",
-    "# Step 2: Assess power of the pre-trends test  \n",
-    "print(\"Step 2: Pre-Trends Power Analysis\")\n",
-    "pt = PreTrendsPower(alpha=0.05, power=0.80, violation_type='linear')\n",
-    "pt_results = pt.fit(results, pre_periods=pre_treatment_periods)\n",
-    "print(f\"MDV (80% power): {pt_results.mdv:.3f}\")\n",
-    "print(\"\")\n",
-    "\n",
-    "# Step 3: Interpret\n",
-    "print(\"Step 3: Interpretation\")\n",
-    "print(f\"Your pre-trends test could only detect violations >= {pt_results.mdv:.3f}\")\n",
-    "print(f\"Violations smaller than this would likely go undetected.\")\n",
-    "print(\"\")\n",
-    "\n",
-    "# Step 4: Connect to Honest DiD for robust inference\n",
-    "print(\"Step 4: Robust Inference with Honest DiD\")\n",
-    "honest = HonestDiD(method='smoothness', M=pt_results.mdv)\n",
-    "honest_results = honest.fit(results)\n",
-    "print(f\"Robust 95% CI (M=MDV): [{honest_results.ci_lb:.3f}, {honest_results.ci_ub:.3f}]\")\n",
-    "print(f\"Conclusion: {'Effect is robust' if honest_results.is_significant else 'Effect may not be robust'}\")"
-   ]
+   "source": "# Typical workflow for pre-trends power analysis\n\n# Step 1: Estimate event study with proper pre/post period classification\nmp_did = MultiPeriodDiD()\n\n# Specify actual post-treatment periods; pre-period coefficients are\n# estimated automatically by MultiPeriodDiD for the event study\npre_treatment_periods = [0, 1, 2, 3]  # Define which are pre-treatment\n\nresults = mp_did.fit(\n    df, \n    outcome='outcome',\n    treatment='treated', \n    time='period',\n    post_periods=[5, 6, 7, 8, 9]\n)\n\n# Step 2: Assess power of the pre-trends test  \nprint(\"Step 2: Pre-Trends Power Analysis\")\npt = PreTrendsPower(alpha=0.05, power=0.80, violation_type='linear')\npt_results = pt.fit(results, pre_periods=pre_treatment_periods)\nprint(f\"MDV (80% power): {pt_results.mdv:.3f}\")\nprint(\"\")\n\n# Step 3: Interpret\nprint(\"Step 3: Interpretation\")\nprint(f\"Your pre-trends test could only detect violations >= {pt_results.mdv:.3f}\")\nprint(f\"Violations smaller than this would likely go undetected.\")\nprint(\"\")\n\n# Step 4: Connect to Honest DiD for robust inference\nprint(\"Step 4: Robust Inference with Honest DiD\")\nhonest = HonestDiD(method='smoothness', M=pt_results.mdv)\nhonest_results = honest.fit(results)\nprint(f\"Robust 95% CI (M=MDV): [{honest_results.ci_lb:.3f}, {honest_results.ci_ub:.3f}]\")\nprint(f\"Conclusion: {'Effect is robust' if honest_results.is_significant else 'Effect may not be robust'}\")"
   },
   {
    "cell_type": "markdown",
@@ -693,4 +616,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}