diff --git a/lectures/kesten_processes.md b/lectures/kesten_processes.md
index 4dfb235a..37548d37 100644
--- a/lectures/kesten_processes.md
+++ b/lectures/kesten_processes.md
@@ -56,7 +56,6 @@ import jax
 import jax.numpy as jnp
 from jax import random
 from jax import lax
-from quantecon import tic, toc
 from typing import NamedTuple
 from functools import partial
 ```
@@ -235,17 +234,15 @@ Let's try running the code and generating a cross-section.
 
 ```{code-cell} ipython3
 firm = Firm()
-tic()
-data = generate_cross_section(firm).block_until_ready()
-toc()
+with qe.Timer():
+    data = generate_cross_section(firm).block_until_ready()
 ```
 
 We run the function again so we can see the speed without compile time.
 
 ```{code-cell} ipython3
-tic()
-data = generate_cross_section(firm).block_until_ready()
-toc()
+with qe.Timer():
+    data = generate_cross_section(firm).block_until_ready()
 ```
 
 Let's produce the rank-size plot and check the distribution:
@@ -314,15 +311,13 @@ def generate_cross_section_lax(
 Let's see if we get any speed gain
 
 ```{code-cell} ipython3
-tic()
-data = generate_cross_section_lax(firm).block_until_ready()
-toc()
+with qe.Timer():
+    data = generate_cross_section_lax(firm).block_until_ready()
 ```
 
 ```{code-cell} ipython3
-tic()
-data = generate_cross_section_lax(firm).block_until_ready()
-toc()
+with qe.Timer():
+    data = generate_cross_section_lax(firm).block_until_ready()
 ```
 
 Here we produce the same rank-size plot:
@@ -393,15 +388,13 @@ def generate_cross_section_lax(
 Here are the run times.
 
 ```{code-cell} ipython3
-tic()
-data = generate_cross_section_lax(firm).block_until_ready()
-toc()
+with qe.Timer():
+    data = generate_cross_section_lax(firm).block_until_ready()
 ```
 
 ```{code-cell} ipython3
-tic()
-data = generate_cross_section_lax(firm).block_until_ready()
-toc()
+with qe.Timer():
+    data = generate_cross_section_lax(firm).block_until_ready()
 ```
 
 This method might or might not be faster.