在statsmodels中有滚动OLS。
样本代码：

import pandas as pd
import numpy as np

from statsmodels.regression.rolling import RollingOLS

# Read data & adding "intercept" column
df = pd.read_csv('sample_rolling_regression_OLS.csv')
df['intercept'] = 1

# Groupby then apply RollingOLS
df.groupby('name')[['y', 'intercept', 'x']].apply(lambda g: RollingOLS(g['y'], g[['intercept', 'x']], window=6).fit().params)

样本数据：或可从以下网址下载：https://www.dropbox.com/s/zhklsg5cmfksufm/sample_rolling_regression_OLS.csv?dl=0

name     y    x  intercept
0     a  13.7  7.8          1
1     a -14.7 -9.7          1
2     a  -3.4 -0.6          1
3     a   7.4  3.3          1
4     a  -5.3 -1.9          1
5     a  -8.3 -2.3          1
6     a   8.9  3.7          1
7     a  10.0  7.9          1
8     a   1.8 -0.4          1
9     a   6.7  3.1          1
10    a  17.4  9.9          1
11    a   8.9  7.7          1
12    a  -3.1 -1.5          1
13    a -12.2 -7.9          1
14    a   7.6  4.9          1
15    a   4.2  2.3          1
16    a -15.3 -5.6          1
17    a   9.9  6.7          1
18    a  11.0  5.2          1
19    a   5.7  5.1          1
20    a  -0.3 -0.6          1
21    a -15.0 -8.7          1
22    a -10.6 -5.7          1
23    a -16.0 -9.1          1
24    b  16.7  8.5          1
25    b   9.2  8.2          1
26    b   4.7  3.4          1
27    b -16.7 -8.7          1
28    b  -4.8 -1.5          1
29    b  -2.6 -2.2          1
30    b  16.3  9.5          1
31    b  15.8  9.8          1
32    b -10.8 -7.3          1
33    b  -5.4 -3.4          1
34    b  -6.0 -1.8          1
35    b   1.9 -0.6          1
36    b   6.3  6.1          1
37    b -14.7 -8.0          1
38    b -16.1 -9.7          1
39    b -10.5 -8.0          1
40    b   4.9  1.0          1
41    b  11.1  4.5          1
42    b -14.8 -8.5          1
43    b  -0.2 -2.8          1
44    b   6.3  1.7          1
45    b -14.1 -8.7          1
46    b  13.8  8.9          1
47    b  -6.2 -3.0          1

下面是一个使用statmodels的RollingOLS的工作示例，灵感来自answer to this question on Rolling OLS Regressions and Predictions by Group。
这可以很容易地为您的面板数据进行修改，以执行滚动窗口回归。model.params将为您提供系数，特别是每月截距基金明智的输出。

from statsmodels.regression.rolling import RollingOLS
from statsmodels.tools.tools import add_constant
import statsmodels.api as sm
import pandas as pd
import numpy as np

data = sm.datasets.grunfeld.load()
df_grunfeld = pd.DataFrame(data.data)
df_grunfeld.set_index(['firm'], append=True, inplace=True)

# Simple Model
# $$invest = \beta_0 + \beta_1 value$$

def invest_params(df_gf, intercept=False):

    """
    Function to operate on the data of a single firm.
    Assumes df_gf has the columns 'invest' and 'value' available.
    Returns a dataframe containing model parameters
    """

    # we should have at least k + 1 observations
    min_obs = 3 if intercept else 2
    wndw = 8 

    # if there are less than min_obs rows in df_gf, RollingOLS will throw an error
    # Instead, handle this case separately

    if df_gf.shape[0] < min_obs:
        cols = ['coef_intercept', 'coef_value'] if intercept else ['coef_value']
        return pd.DataFrame(index=df_gf.index, columns=cols)

    y = df_gf['invest']
    x = add_constant(df_gf['value']) if intercept else df_gf['value']

    model = RollingOLS(y, x, expanding=True, min_nobs=min_obs, window=wndw).fit()

    parameters = model.params
    params_shifted = model.params.shift(1)
    mse = model.mse_resid
    parameters['invest_hat'] = (parameters.mul(add_constant(df_gf['value']), axis=0)\
                                .sum(axis=1, min_count=1)).to_frame('invest_hat')

    parameters['invest_hat_shift'] = (params_shifted.mul(add_constant(df_gf['value']), axis=0)\
                                      .sum(axis=1, min_count=1)).to_frame('invest_hat_shift')

    parameters['mse'] = mse
    parameters['rmse'] = np.sqrt(mse)
    parameters['nobs'] = model.nobs
    parameters['ssr'] = model.ssr
    parameters['t_const'] = model.tvalues['const']
    parameters['t_value'] = model.tvalues['value']
    parameters.rename(columns = {'const' : 'b0', 'value' : 'b1'}, inplace = True)
    parameters['r2_adj'] = model.rsquared_adj
    
    return parameters

grouped = df_grunfeld.groupby('firm')
df_params = grouped.apply(lambda x: invest_params(x, True))
df_grunfeld_output = df_grunfeld.join(df_params, rsuffix='_coef')