Fix typos

lectures/arellano.md

@@ -403,7 +403,7 @@ class Arellano_Economy:
         self.y_grid = jax.device_put(y_grid)
         self.P = jax.device_put(P)
 
-        # Output recieved while in default, with same shape as y_grid
+        # Output received while in default, with same shape as y_grid
         self.def_y = jnp.minimum(def_y_param * jnp.mean(self.y_grid), self.y_grid)
 
     def params(self):

lectures/ifp_egm.md

@@ -317,7 +317,7 @@ Notice in the code below that
 ```{code-cell} ipython3
 def K_egm(a_in, σ_in, constants, sizes, arrays):
     """
-    The vectorzied operator K using EGM.
+    The vectorized operator K using EGM.
 
     """
 
@@ -598,7 +598,7 @@ plt.show()
 
 ### Timing
 
-Now let's compare excution time of the two methods
+Now let's compare execution time of the two methods
 
 ```{code-cell} ipython3
 qe.tic()

lectures/jax_intro.md

@@ -515,7 +515,7 @@ Try writing a version of this operation for JAX, using all the same
 parameters.
 
 If you are running your code on a GPU, you should be able to achieve
-significantly faster exection.
+significantly faster execution.
 
 
 ```{exercise-end}

lectures/markov_asset.md

@@ -205,7 +205,7 @@ $$
 $$
 
 Here $\{\epsilon_{c, t}\}$ and $\{\epsilon_{d, t}\}$ are IID and standard
-normal, and independent of eachother.
+normal, and independent of each other.
 
 We can think of $\{X_t\}$ as an aggregate shock that affects both consumption
 growth and firm profits (and hence dividends).

lectures/mle.md

@@ -347,7 +347,7 @@ $$
     \beta_2 = 0.5
 $$
 
-Try to obtain the approximate values of $\beta_0,\beta_1,\beta_2$, by simulating a Poission Regression Model such that
+Try to obtain the approximate values of $\beta_0,\beta_1,\beta_2$, by simulating a Poisson Regression Model such that
 
 $$
       y_t \sim {\rm Poisson}(\lambda_t)
@@ -391,7 +391,7 @@ We compute $\lambda$ using {eq}`lambda_mle`
 λ = jnp.exp(β_0 + β_1 * x + β_2 * x**2)
 ```
 
-Let's define $y_t$ by sampling from a Poission distribution with mean as $\lambda_t$.
+Let's define $y_t$ by sampling from a Poisson distribution with mean as $\lambda_t$.
 
 ```{code-cell} ipython3
 y = jax.random.poisson(key, λ, shape)

lectures/newtons_method.md

@@ -105,7 +105,7 @@ for this particular question.
 
 ### A High-Dimensional Version
 
-Let's now shift to a linear algebra formulation, which alllows us to handle
+Let's now shift to a linear algebra formulation, which allows us to handle
 arbitrarily many goods.
 
 The supply function remains unchanged,
@@ -325,7 +325,7 @@ initLs = [jnp.ones(3),
 ```
 
 
-Then define the multivariate version of the formula for the [law of motion of captial](https://python.quantecon.org/newton_method.html#solow)
+Then define the multivariate version of the formula for the [law of motion of capital](https://python.quantecon.org/newton_method.html#solow)
 
 ```{code-cell} ipython3
 def multivariate_solow(k, A=A, s=s, α=α, δ=δ):