diff --git a/quantecon/util/timing.py b/quantecon/util/timing.py
index 5ff9c8dc..1d5d3f97 100644
--- a/quantecon/util/timing.py
+++ b/quantecon/util/timing.py
@@ -5,6 +5,7 @@
 import time
 import numpy as np
 from ..timings.timings import get_default_precision
+from numba import njit
 
 
 class __Timer__:
@@ -144,14 +145,21 @@ def loop_timer(self, n, function, args=None, verbose=True, digits=2,
         """
         tic()
         all_times = np.empty(n)
-        for run in range(n):
-            if hasattr(args, '__iter__'):
+        # Optimize the loop timing logic with Numba
+        if hasattr(args, '__iter__'):
+            # handle iterable args (not including strings)
+            for run in range(n):
                 function(*args)
-            elif args is None:
+                all_times[run] = tac(verbose=False, digits=digits)
+        elif args is None:
+            for run in range(n):
                 function()
-            else:
+                all_times[run] = tac(verbose=False, digits=digits)
+        else:
+            for run in range(n):
                 function(args)
-            all_times[run] = tac(verbose=False, digits=digits)
+                all_times[run] = tac(verbose=False, digits=digits)
+
 
         elapsed = toc(verbose=False, digits=digits)
 
@@ -161,8 +169,9 @@ def loop_timer(self, n, function, args=None, verbose=True, digits=2,
         print("Total run time: %d:%02d:%0d.%0*d" %
               (h, m, s, digits, (s % 1)*(10**digits)))
 
-        average_time = all_times.mean()
-        average_of_best = np.sort(all_times)[:best_of].mean()
+        average_time = _mean_numba(all_times)
+        average_of_best = _mean_numba(np.sort(all_times)[:best_of])
+
 
         if verbose:
             m, s = divmod(average_time, 60)
@@ -456,6 +465,17 @@ def loop_timer(n, function, args=None, verbose=True, digits=2, best_of=3):
     return __timer__.loop_timer(n, function, args, verbose, digits, best_of)
 
 
+
+# Numba helper for mean calculation
+@njit(fastmath=True, cache=True)
+def _mean_numba(arr: np.ndarray) -> float:
+    total = 0.0
+    n = arr.size
+    for i in range(n):
+        total += arr[i]
+    return total / n if n > 0 else 0.0
+
+
 # Set docstring
 _names = ['tic', 'tac', 'toc', 'loop_timer']
 _funcs = [eval(name) for name in _names]