diff --git a/lectures/lake_model_intro.md b/lectures/lake_model_intro.md
index 7a02138b..282a0c8e 100644
--- a/lectures/lake_model_intro.md
+++ b/lectures/lake_model_intro.md
@@ -93,7 +93,7 @@ We are interested in how $u_t$ and $e_t$ evolve over time.
 
 What long-run unemployment rate and employment rate should we expect?
 
-Do long-run outcomes depend on the initial values $(u_0, e_o)$?
+Do long-run outcomes depend on the initial values $(u_0, e_0)$?
 
 ### Visualising the long-run outcomes
 
@@ -335,7 +335,7 @@ def plot_time_paths(lm, x0=None, T=1000, ax=None):
 
         # Plot time series
         for x in x0:
-            x_ts = lm.simulate_path(x0=x)
+            x_ts = lm.simulate_path(x0=x, T=T)
 
             ax.scatter(x_ts[0, :], x_ts[1, :], s=4,)
 
diff --git a/lectures/long_run_growth.md b/lectures/long_run_growth.md
index b999eedf..2a570f40 100644
--- a/lectures/long_run_growth.md
+++ b/lectures/long_run_growth.md
@@ -141,7 +141,7 @@ We create a variable `color_mapping` to store a map between country codes and co
 ```{code-cell} ipython3
 :tags: [hide-input]
 
-country_names = data['countrycode']
+country_names = data['countrycode'].unique()
 
 # Generate a colormap with the number of colors matching the number of countries
 colors = cm.tab20(np.linspace(0, 0.95, len(country_names)))
diff --git a/lectures/time_series_with_matrices.md b/lectures/time_series_with_matrices.md
index 9817e1e1..560ceed0 100644
--- a/lectures/time_series_with_matrices.md
+++ b/lectures/time_series_with_matrices.md
@@ -467,11 +467,13 @@ Let's begin by generating $N$ time realizations of $y$ plotting them together wi
 # Plot mean
 N = 100
 
+ys = series_process.sample_y(N, rng=rng)
+
 for i in range(N):
     col = cm.viridis(rng.random())  # Choose a random color from viridis
-    ys = series_process.sample_y(N, rng=rng)
     plt.plot(ys[i,:], lw=0.5, color=col)
-    plt.plot(μ_y, color='red')
+
+plt.plot(μ_y, color='red')
 
 plt.xlabel('t')
 plt.ylabel('y')