calculate t statistics and p-values for coefficients in Linear Model in python, using scikit-learn framework.

linear_model.py

from sklearn import linear_model
from scipy import stats
import numpy as np


class LinearRegression(linear_model.LinearRegression):
    """
    LinearRegression class after sklearn's, but calculate t-statistics
    and p-values for model coefficients (betas).
    Additional attributes available after .fit()
    are `t` and `p` which are of the shape (y.shape[1], X.shape[1])
    which is (n_features, n_coefs)
    This class sets the intercept to 0 by default, since usually we include it
    in X.
    """

    def __init__(self, *args, **kwargs):
        if not "fit_intercept" in kwargs:
            kwargs['fit_intercept'] = False
        super(LinearRegression, self)\
                .__init__(*args, **kwargs)

    def fit(self, X, y, n_jobs=1):
        self = super(LinearRegression, self).fit(X, y, n_jobs)

        sse = np.sum((self.predict(X) - y) ** 2, axis=0) / float(X.shape[0] - X.shape[1])
        se = np.array([
            np.sqrt(np.diagonal(sse[i] * np.linalg.inv(np.dot(X.T, X))))
                                                    for i in range(sse.shape[0])
                    ])

        self.t = self.coef_ / se
        self.p = 2 * (1 - stats.t.cdf(np.abs(self.t), y.shape[0] - X.shape[1]))
        return self

In the code below I am running time-series regression for the last 36 periods. I can find coefficients through 'model.coef_' but how do I find p-value for each of these coefficients?

for c in range(36,167): Port = df2.iloc[range(c-36,c),3] Var_x = df2.iloc[range(c-36,c),[0,4,5]] y = Port.to_numpy() x = Var_x.to_numpy().reshape(-1,3) model = LinearRegression() model.fit(x, y) model = LinearRegression().fit(x, y) r_sq = model.score(x, y) print(model.coef_)

You can try the following:

from sklearn.feature_selection import f_regression

f_statistic, p_value = f_regression(X, y)