问题描述
我正在尝试/未能在 Python 中重现 VAR 模型构建的“流行”示例，但在格兰杰因果关系测试中陷入困境。我使用提供的数据集和代码，但出现以下错误。感谢您让我知道您是否认为您了解这里发生的事情！最好
文件“/Applications/anaconda3/lib/python3.8/site-packages/statsmodels/tsa/tsatools.py”，第524行，在lagmat2ds中 raise ValueError('只支持一维和二维数据。') ValueError：仅支持一维和二维数据。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Import Statsmodels
from statsmodels.tsa.api import VAR
from statsmodels.tsa.stattools import adfuller
from statsmodels.tools.eval_measures import rmse,aicfrom
filepath = 'https://raw.githubusercontent.com/selva86/datasets/master/Raotbl6.csv'
df = pd.read_csv(filepath,parse_dates=['date'],index_col='date')
print(df.shape) # (123,8)
df.tail()
from statsmodels.tsa.stattools import grangercausalitytests
maxlag=12
test = 'ssr_chi2test'
def grangers_causation_matrix(data,variables,test='ssr_chi2test',verbose=False):
"""Check Granger Causality of all possible combinations of
the Time series.
The rows are the response variable,columns are predictors.
The values in the table
are the P-Values. P-Values lesser than the significance
level (0.05),implies
the Null Hypothesis that the coefficients of the
corresponding past values is
zero,that is,the X does not cause Y can be rejected.
data : pandas dataframe containing the time series
variables
variables : list containing names of the time series
variables.
"""
df = pd.DataFrame(np.zeros((len(variables),len(variables))),columns=variables,index=variables)
for c in df.columns:
for r in df.index:
test_result = grangercausalitytests(data[[r,c]],maxlag=maxlag,verbose=False)
p_values = [round(test_result[i+1][0][test][1],4) for i in
range(maxlag)]
if verbose: print(f'Y = {r},X = {c},P Values =
{p_values}')
min_p_value = np.min(p_values)
df.loc[r,c] = min_p_value
df.columns = [var + '_x' for var in variables]
df.index = [var + '_y' for var in variables]
return df
grangers_causation_matrix(df,variables = df.columns)
解决方法
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