update wording around error model

Author: drbenvincent

docs/source/notebooks/graded_intervention_time_series_single_channel_ols.ipynb

@@ -21,7 +21,7 @@
     "  - **Saturation transforms** (optional): Model diminishing returns using Hill, logistic, or Michaelis-Menten functions when appropriate\n",
     "- **Baseline controls**: Include confounders and natural trends in the regression\n",
     "- **{term}`Counterfactual` analysis**: Estimate causal effects by zeroing or scaling interventions\n",
-    "- **HAC standard errors**: Robust inference accounting for autocorrelation and heteroskedasticity\n",
+    "- **Error model for autocorrelation**: Time series data typically exhibits autocorrelation (temporal dependence in residuals), which requires special handling for valid inference. This can be addressed through multiple approaches including HAC (Newey-West) standard errors or ARIMAX error models (see detailed discussion below).\n",
     "\n",
     "Transfer functions can be as simple as a distributed lag (adstock only) or can combine multiple transformations (e.g., saturation followed by adstock). The key is to match the functional form to the expected dynamics of the intervention.\n",
     "\n",