Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5
sigma = 0.1
A = [[phi_1, phi_2, phi_3, phi_4],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]
C = [[sigma], [0], [0], [0]]
G = [1, 0, 0, 0]
T0 = 10
T1 = 50
T2 = 75
T4 = 100
ar = LinearStateSpace(A, C, G, mu_0=np.ones(4))
ymin, ymax = -0.8, 1.25
fig, ax = plt.subplots(figsize=(8, 5))
ax.grid(alpha=0.4)
ax.set_ylim(ymin, ymax)
ax.set_ylabel(r'$y_t$', fontsize=16)
ax.vlines((T0, T1, T2), -1.5, 1.5)
ax.set_xticks((T0, T1, T2))
ax.set_xticklabels((r"$T$", r"$T'$", r"$T''$"), fontsize=14)
sample = []
for i in range(80):
rcolor = random.choice(('c', 'g', 'b'))
x, y = ar.simulate(ts_length=T4)
import matplotlib.pyplot as plt
from quantecon import LinearStateSpace
import random
phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5
sigma = 0.1
A = [[phi_1, phi_2, phi_3, phi_4],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]
C = [[sigma], [0], [0], [0]]
G = [1, 0, 0, 0]
T = 30
ar = LinearStateSpace(A, C, G, mu_0=np.ones(4))
ymin, ymax = -0.8, 1.25
fig, axes = plt.subplots(1, 2, figsize=(8, 3))
for ax in axes:
ax.grid(alpha=0.4)
ax = axes[0]
ax.set_ylim(ymin, ymax)
ax.set_ylabel(r'$y_t$', fontsize=16)
ax.vlines((T,), -1.5, 1.5)
ax.set_xticks((T,))
ax.set_xticklabels((r'$T$',))
import matplotlib.pyplot as plt
from scipy.stats import norm
from quantecon import LinearStateSpace
phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5
sigma = 0.1
A = [[phi_1, phi_2, phi_3, phi_4],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]
C = [[sigma], [0], [0], [0]]
G = [1, 0, 0, 0]
T = 30
ar = LinearStateSpace(A, C, G)
ymin, ymax = -0.8, 1.25
fig, ax = plt.subplots(figsize=(8, 4))
ax.set_xlim(ymin, ymax)
ax.set_xlabel(r'$y_t$', fontsize=16)
x, y = ar.replicate(T=T, num_reps=100000)
mu_x, mu_y, Sigma_x, Sigma_y = ar.stationary_distributions()
f_y = norm(loc=float(mu_y), scale=float(np.sqrt(Sigma_y)))
y = y.flatten()
ax.hist(y, bins=50, normed=True, alpha=0.4)
ygrid = np.linspace(ymin, ymax, 150)
import matplotlib.pyplot as plt
from scipy.stats import norm
from quantecon import LinearStateSpace
phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5
sigma = 0.1
A = [[phi_1, phi_2, phi_3, phi_4],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]
C = [[sigma], [0], [0], [0]]
G = [1, 0, 0, 0]
T = 30
ar = LinearStateSpace(A, C, G)
ymin, ymax = -0.8, 1.25
fig, ax = plt.subplots(figsize=(8, 4))
ax.set_xlim(ymin, ymax)
ax.set_xlabel(r'$y_t$', fontsize=16)
x, y = ar.replicate(T=T, num_reps=100000)
mu_x, mu_y, Sigma_x, Sigma_y = ar.stationary_distributions()
f_y = norm(loc=float(mu_y), scale=float(np.sqrt(Sigma_y)))
y = y.flatten()
ax.hist(y, bins=50, normed=True, alpha=0.4)
ygrid = np.linspace(ymin, ymax, 150)
phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5
sigma = 0.1
A = [[phi_1, phi_2, phi_3, phi_4],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]
C = [[sigma], [0], [0], [0]]
G = [1, 0, 0, 0]
T0 = 10
T1 = 50
T2 = 75
T4 = 100
ar = LinearStateSpace(A, C, G, mu_0=np.ones(4))
ymin, ymax = -0.8, 1.25
fig, ax = plt.subplots(figsize=(8, 5))
ax.grid(alpha=0.4)
ax.set_ylim(ymin, ymax)
ax.set_ylabel(r'$y_t$', fontsize=16)
ax.vlines((T0, T1, T2), -1.5, 1.5)
ax.set_xticks((T0, T1, T2))
ax.set_xticklabels((r"$T$", r"$T'$", r"$T''$"), fontsize=14)
sample = []
for i in range(80):
rcolor = random.choice(('c', 'g', 'b'))
x, y = ar.simulate(ts_length=T4)
import matplotlib.pyplot as plt
from quantecon import LinearStateSpace
import random
phi_1, phi_2, phi_3, phi_4 = 0.5, -0.2, 0, 0.5
sigma = 0.1
A = [[phi_1, phi_2, phi_3, phi_4],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0]]
C = [[sigma], [0], [0], [0]]
G = [1, 0, 0, 0]
T = 30
ar = LinearStateSpace(A, C, G, mu_0=np.ones(4))
ymin, ymax = -0.8, 1.25
fig, axes = plt.subplots(1, 2, figsize=(8, 3))
for ax in axes:
ax.grid(alpha=0.4)
ax = axes[0]
ax.set_ylim(ymin, ymax)
ax.set_ylabel(r'$y_t$', fontsize=16)
ax.vlines((T,), -1.5, 1.5)
ax.set_xticks((T,))
ax.set_xticklabels((r'$T$',))