MAINT: update njit to jit (#182)

* update njit to jit

* fix typo

---------

Co-authored-by: kp992 <kpl992@outlook.com>

Author: mmcky

lectures/cake_eating_numerical.md

@@ -323,7 +323,7 @@ for the purpose of comparing the results of JAX implementation.
 
 ```{code-cell} ipython3
 import numpy as np
-from numba import prange, njit
+from numba import prange, jit
 from quantecon.optimize import brent_max
 ```
 
@@ -344,13 +344,13 @@ def create_cake_eating_model_numba(β=0.96,           # discount factor
 
 ```{code-cell} ipython3
 # Utility function
-@njit
+@jit
 def u_numba(c, cem):
     return (c ** (1 - cem.γ)) / (1 - cem.γ)
 ```
 
 ```{code-cell} ipython3
-@njit
+@jit
 def state_action_value_numba(c, x, v_array, cem):
     """
     Right hand side of the Bellman equation given x and c.
@@ -363,7 +363,7 @@ def state_action_value_numba(c, x, v_array, cem):
 ```
 
 ```{code-cell} ipython3
-@njit
+@jit
 def T_numba(v, ce):
     """
     The Bellman operator.  Updates the guess of the value function.
@@ -424,7 +424,7 @@ numba_time = time.time() - in_time
 
 ```{code-cell} ipython3
 ratio = numba_time/jax_time
-print(f"JAX implementation is {ratio} times faster than NumPy.")
+print(f"JAX implementation is {ratio} times faster than Numba.")
 print(f"JAX time: {jax_time}")
 print(f"Numba time: {numba_time}")
 ```

lectures/inventory_ssd.md

@@ -174,7 +174,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 from collections import namedtuple
 import time
-from numba import njit, prange
+from numba import jit, prange
 ```
 
 Let's check the GPU we are running
@@ -395,11 +395,11 @@ plot_ts()
 Let's try the same operations in Numba in order to compare the speed.
 
 ```{code-cell} ipython3
-@njit
+@jit
 def demand_pdf_numba(p, d):
     return (1 - p)**d * p
 
-@njit
+@jit
 def B_numba(x, i_z, a, v, model):
     """
     The function B(x, z, a, v) = r(x, a) + β(z) Σ_x′ v(x′) P(x, a, x′).
@@ -415,7 +415,7 @@ def B_numba(x, i_z, a, v, model):
     return profit + z * cv
 
 
-@njit(parallel=True)
+@jit(parallel=True)
 def T_numba(v, model):
     """The Bellman operator."""
     c, κ, p, z_vals, Q = model
@@ -428,7 +428,7 @@ def T_numba(v, model):
     return new_v
 
 
-@njit(parallel=True)
+@jit(parallel=True)
 def get_greedy_numba(v, model):
     """Get a v-greedy policy.  Returns a zero-based array."""
     c, κ, p, z_vals, Q = model