import numpy as np
import pandas as pd
import statsmodels.api as sm
from import arch_model
# 定义GARCH-MIDAS模型
def garch_midas(y, x, lags, p, q):
# 计算MIDAS权重
w = np.zeros((lags, p))
for i in range(lags):
for j in range(p):
w[i, j] = (i + 1) **
w[i, :] = w[i, :] / np.sum(w[i, :])
# 计算MIDAS变量
x_midas = np.zeros((len(x) - lags, p))
for i in range(lags, len(x)):
for j in range(p):
x_midas[i - lags, j] = np.sum(x[i - lags:i, j] * w[:, j])
# 拟合GARCH模型
model = arch_model(y, x=x_midas, mean='', vol='GARCH', p=p, q=q)
result = model.fit(disp='off')
return result
# 读取数据
data = pd.read_csv('data.csv', index_col=0)
y = data['y']
x = data[['x1', 'x2', 'x3']]
lags = 12
p = 1
q = 1
# 拟合GARCH-MIDAS模型
result = garch_midas(y, x, lags, p, q)
# 输出结果
print(result.summary())
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