@@ -225,7 +225,7 @@ First, we store parameters in a `namedtuple`:
225
225
226
226
```{code-cell} ipython3
227
227
# Create the rational expectation version of Cagan model in finite time
228
- CaganREE = namedtuple("ConsumptionSmoothing ",
228
+ CaganREE = namedtuple("CaganREE ",
229
229
["m0", "T", "μ_seq", "α", "δ", "π_end"])
230
230
231
231
def create_cagan_model(m0, α, T, μ_seq):
@@ -255,8 +255,8 @@ Now we can solve the model to compute $\pi_t$, $m_t$ and $p_t$ for $t =1, \ldots
255
255
256
256
```{code-cell} ipython3
257
257
def solve(model):
258
- m0, T, π_end, μ_seq, α, δ = model.m0, model.T, model.π_end, model.μ_seq, model.α, model.δ
259
-
258
+ model_params = model.m0, model.T, model.π_end, model.μ_seq, model.α, model.δ
259
+ m0, T, π_end, μ_seq, α, δ = model_params
260
260
A1 = np.eye(T+1, T+1) - δ * np.eye(T+1, T+1, k=1)
261
261
A2 = np.eye(T+1, T+1) - np.eye(T+1, T+1, k=-1)
262
262
@@ -451,22 +451,24 @@ T_seq = range(T+2)
451
451
fig, ax = plt.subplots(2, 3, figsize=[10,5], dpi=200)
452
452
453
453
ax[0,0].plot(T_seq[:-1], μ_seq_2)
454
+ ax[0,0].set_ylabel(r'$\mu$')
455
+
454
456
ax[0,1].plot(T_seq, π_seq_2)
457
+ ax[0,1].set_ylabel(r'$\pi$')
458
+
455
459
ax[0,2].plot(T_seq, m_seq_2_regime1 - p_seq_2_regime1)
460
+ ax[0,2].set_ylabel(r'$m - p$')
461
+
456
462
ax[1,0].plot(T_seq, m_seq_2_regime1,
457
463
label='Smooth $m_{T_1}$')
458
464
ax[1,0].plot(T_seq, m_seq_2_regime2,
459
465
label='Jumpy $m_{T_1}$')
466
+ ax[1,0].set_ylabel(r'$m$')
467
+
460
468
ax[1,1].plot(T_seq, p_seq_2_regime1,
461
469
label='Smooth $m_{T_1}$')
462
470
ax[1,1].plot(T_seq, p_seq_2_regime2,
463
471
label='Jumpy $m_{T_1}$')
464
-
465
-
466
- ax[0,0].set_ylabel(r'$\mu$')
467
- ax[0,1].set_ylabel(r'$\pi$')
468
- ax[0,2].set_ylabel(r'$m - p$')
469
- ax[1,0].set_ylabel(r'$m$')
470
472
ax[1,1].set_ylabel(r'$p$')
471
473
472
474
for i in range(2):
0 commit comments