线性模型中的自定义F检验(Python)

Question

我已经拟合了linearmodels.PanelOLS模型，并将其存储在m中。现在我想测试一下，某些系数是否同时等于零。

一个合适的linearmodels.PanelOLS对象是否有一个F测试函数，在这里我可以传递我自己的限制矩阵？

我在找像statsmodels' f_test method这样的东西。

下面是一个最小可重现性的例子。

# Libraries
from linearmodels.panel import PanelOLS
from linearmodels.datasets import wage_panel

# Load data and set index
df = wage_panel.load()
df = df.set_index(['nr','year'])

# Add constant term
df['const'] = 1

# Fit model
m = PanelOLS(dependent=df['lwage'], exog=df[['const','expersq','married']])
m = m.fit(cov_type='clustered', cluster_entity=True)

# Is there an f_test method for m???
m.f_test(r_mat=some_matrix_here) # Something along these lines?

Answer 1

您可以使用wald_test (在协方差的某些假设下，标准F检验与Walkd检验数值相同)。

# Libraries
from linearmodels.panel import PanelOLS
from linearmodels.datasets import wage_panel

# Load data and set index
df = wage_panel.load()
df = df.set_index(['nr','year'])

# Add constant term
df['const'] = 1

# Fit model
m = PanelOLS(dependent=df['lwage'], exog=df[['const','expersq','married']])
m = m.fit(cov_type='clustered', cluster_entity=True)

然后是测试

import numpy as np

# Use matrix notation RB - q = 0 where R is restr and q is value
# Restrictions: expersq = 0.001 & expersq+married = 0.2
restr = np.array([[0,1,0],[0,1,1]])
value = np.array([0.01, 0.2])
m.wald_test(restr, value)

这会返回

Linear Equality Hypothesis Test
H0: Linear equality constraint is valid
Statistic: 0.2608
P-value: 0.8778
Distributed: chi2(2)
WaldTestStatistic, id: 0x2271cc6fdf0

如果使用公式来定义模型，也可以使用公式语法，这样可以更容易地对模型进行编码。

fm = PanelOLS.from_formula("lwage~ 1 + expersq + married", data=df)
fm = fm.fit(cov_type='clustered', cluster_entity=True)
fm.wald_test(formula="expersq = 0.001,expersq+married = 0.2")

结果与上述相同。