igerber
diff --git a/‎diff_diff/estimators.py‎
Lines changed: 5 additions & 2 deletions b/‎diff_diff/estimators.py‎
Lines changed: 5 additions & 2 deletions
diff --git a/‎diff_diff/results.py‎
Lines changed: 1 addition & 2 deletions b/‎diff_diff/results.py‎
Lines changed: 1 addition & 2 deletions
@@ -5,6 +5,7 @@
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
+from numpy.linalg import LinAlgError
 import pandas as pd
 from scipy import stats
 
@@ -90,7 +91,7 @@ class DifferenceInDifferences:
     def __init__(
         self,
         robust: bool = True,
-        cluster: str = None,
+        cluster: Optional[str] = None,
         alpha: float = 0.05
     ):
         self.robust = robust
@@ -248,6 +249,7 @@ def fit(
             k = X.shape[1]
             mse = np.sum(residuals ** 2) / (n - k)
             # Use solve() instead of inv() for numerical stability
+            # solve(A, B) computes X where AX=B, so this yields (X'X)^{-1} * mse
             vcov = np.linalg.solve(X.T @ X, mse * np.eye(k))
 
         # Extract ATT (coefficient on interaction term)
@@ -963,6 +965,7 @@ def fit(
             k = X.shape[1]
             mse = np.sum(residuals ** 2) / (n - k)
             # Use solve() instead of inv() for numerical stability
+            # solve(A, B) computes X where AX=B, so this yields (X'X)^{-1} * mse
             vcov = np.linalg.solve(X.T @ X, mse * np.eye(k))
 
         # Degrees of freedom
@@ -1528,7 +1531,7 @@ def _bootstrap_se(
                 )
                 bootstrap_estimates.append(tau)
 
-            except (ValueError, np.linalg.LinAlgError, KeyError):
+            except (ValueError, LinAlgError, KeyError):
                 # Skip failed bootstrap iterations (e.g., singular matrices,
                 # missing data in resampled units, or invalid weight computations)
                 continue
 
@@ -56,9 +56,8 @@ class DiDResults:
 
     def __repr__(self) -> str:
         """Concise string representation."""
-        sig = "***" if self.p_value < 0.001 else "**" if self.p_value < 0.01 else "*" if self.p_value < 0.05 else ""
         return (
-            f"DiDResults(ATT={self.att:.4f}{sig}, "
+            f"DiDResults(ATT={self.att:.4f}{self.significance_stars}, "
             f"SE={self.se:.4f}, "
             f"p={self.p_value:.4f})"
         )