我正在运行一列值的 OLS 摘要。 OLS 的一部分是 Durbin-Watson 和 Jarque-Bera (JB) 统计数据，我想直接提取这些值，因为它们已经被计算出来，而不是像我现在使用 durbinwatson 那样将这些步骤作为额外步骤运行。
这是我的代码：
import pandas as pd
import statsmodels.api as sm
csv = mydata.csv
df = pd.read_csv(csv)
var = df[variable]
year = df['Year']
model = sm.OLS(var,year)
results = model.fit()
summary = results.summary()
print summary
#print dir(results)
residuals = results.resid
durbinwatson = statsmodels.stats.stattools.durbin_watson(residuals, axis=0)
print durbinwatson
Results:
OLS Regression Results
==============================================================================
Dep. Variable: LST R-squared: 1.000
Model: OLS Adj. R-squared: 1.000
Method: Least Squares F-statistic: 3.026e+05
Date: Fri, 10 Nov 2017 Prob (F-statistic): 2.07e-63
Time: 20:37:03 Log-Likelihood: -82.016
No. Observations: 32 AIC: 166.0
Df Residuals: 31 BIC: 167.5
Df Model: 1
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Year 0.1551 0.000 550.069 0.000 0.155 0.156
==============================================================================
Omnibus: 1.268 Durbin-Watson: 1.839
Prob(Omnibus): 0.530 Jarque-Bera (JB): 1.087
Skew: -0.253 Prob(JB): 0.581
Kurtosis: 2.252 Cond. No. 1.00
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
我通过打印发现
dir(results)
我可以获得 OLS 摘要元素的列表，并且可以像我在这里那样提取测试的残差（或 R 平方等），但我不能仅提取 durbin watson 或 Jarque贝拉。我试过这个：
print results.wald_test
但我刚刚收到错误：
<bound method OLSResults.wald_test of <statsmodels.regression.linear_model.OLSResults object at 0x0D05B3F0>>
而且我什至在summary的目录下都找不到jarque bera test。有什么帮助吗？
import pandas as pd
import statsmodels.api as sm
from statsmodels.stats.stattools import durbin_watson #add this import
csv = mydata.csv
df = pd.read_csv(csv)
var = df[variable]
year = df['Year']
model = sm.OLS(var,year)
results = model.fit()
summary = results.summary()
dw = float(durbin_watson(results.resid)) # this line will access the durbin watson score
print(dw)
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