import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
sys.path.append("..")
import correlation_coefficient
import dgp
np.random.seed(2)
n = 1000
t = 100
plist = []
for i in range(n):
x = dgp.random_walk(0, 1, 0, t)
y = dgp.random_walk(0, 1, 0, t)
s = correlation_coefficient.corrcoef(x, y)
plist.append(s)
plt.figure()
plt.hist(plist, bins=40, facecolor="w", edgecolor="black")
plt.title(
'Frequency distribution of the correlation coefficient between I(1) series.'
)
plt.show()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
sys.path.append("..")
import correlation_coefficient
import dgp
np.random.seed(2)
n = 1000
t = 100
plist = []
for i in range(n):
y = dgp.random_walk(0, 1, 0, t)
v = np.random.normal(0, 1, t)
s = correlation_coefficient.corrcoef(y, v)
plist.append(s)
plt.figure()
plt.hist(plist, bins=32, facecolor="w", edgecolor="black")
plt.title(
'Frequency distribution of the correlation coefficient between I(0) and I(1)'
)
plt.show()
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
sys.path.append("..")
import correlation_coefficient
import dgp
np.random.seed(2)
n = 1000
t = 100
plist = []
for i in range(n):
z = dgp.autoregressive([2, -1], 1, [0, 0], 0, t)
w = dgp.autoregressive([2, -1], 1, [0, 0], 0, t)
s = correlation_coefficient.corrcoef(z, w)
plist.append(s)
plt.figure()
plt.hist(plist, bins=40, facecolor="w", edgecolor="black")
plt.title(
'Frequency distribution of the correlation coefficient between I(2) series.'
)
plt.show()