修复了Pandas或Statsmodels中的效果

Question

是否有现有函数来估算Pandas或Statsmodels的固定效应(单向或双向).

以前在Statsmodels中有一个函数,但似乎已经停止了.在Pandas中,有一些叫做的东西plm,但是我无法导入或运行它pd.plm().

Answer 1

如评论中所述,自版本0.20.0起,PanelOLS已从Pandas中删除.所以你真的有三个选择:

1. 如果您使用Python 3,您可以使用linearmodels最近的答案中指定:https://stackoverflow.com/a/44836199/3435183

2. 只需在您的statsmodels规范中指定各种虚拟对象,例如使用pd.get_dummies.如果固定效果的数量很大,则可能不可行.

3. 或者做一些基于组的贬低然后使用statsmodels(如果你估计很多固定效果,这将有效).这是你可以为单向固定效果做的准系统版本:

   def areg(formula,data=None,absorb=None,cluster=None): 
   
       y,X = patsy.dmatrices(formula,data,return_type='dataframe')
   
       ybar = y.mean()
       y = y -  y.groupby(data[absorb]).transform('mean') + ybar
   
       Xbar = X.mean()
       X = X - X.groupby(data[absorb]).transform('mean') + Xbar
   
       reg = sm.OLS(y,X)
       # Account for df loss from FE transform
       reg.df_resid -= (data[absorb].nunique() - 1)
   
       return reg.fit(cov_type='cluster',cov_kwds={'groups':data[cluster].values})
   

以下是使用旧版本时可以执行的操作Pandas:

使用pandas' PanelOLS(在plm模块中)使用时间固定效果的示例.注意,导入PanelOLS:

>>> from pandas.stats.plm import PanelOLS
>>> df

                y    x
date       id
2012-01-01 1   0.1  0.2
           2   0.3  0.5
           3   0.4  0.8
           4   0.0  0.2
2012-02-01 1   0.2  0.7 
           2   0.4  0.5
           3   0.2  0.3
           4   0.1  0.1
2012-03-01 1   0.6  0.9
           2   0.7  0.5
           3   0.9  0.6
           4   0.4  0.5

注意,数据帧必须具有多索引集; 根据索引panelOLS确定time和entity效果:

>>> reg  = PanelOLS(y=df['y'],x=df[['x']],time_effects=True)
>>> reg

-------------------------Summary of Regression Analysis-------------------------

Formula: Y ~ <x>

Number of Observations:         12
Number of Degrees of Freedom:   4

R-squared:         0.2729
Adj R-squared:     0.0002

Rmse:              0.1588

F-stat (1, 8):     1.0007, p-value:     0.3464

Degrees of Freedom: model 3, resid 8

-----------------------Summary of Estimated Coefficients------------------------
      Variable       Coef    Std Err     t-stat    p-value    CI 2.5%   CI 97.5%
--------------------------------------------------------------------------------
             x     0.3694     0.2132       1.73     0.1214    -0.0485     0.7872
---------------------------------End of Summary--------------------------------- 

文档字符串:

PanelOLS(self, y, x, weights = None, intercept = True, nw_lags = None,
entity_effects = False, time_effects = False, x_effects = None,
cluster = None, dropped_dummies = None, verbose = False,
nw_overlap = False)

Implements panel OLS.

See ols function docs

这是另一个功能(如fama_macbeth),我相信计划是将此功能移动到statsmodels.

Answer 2

有一个名为linearmodels(https://pypi.org/project/linearmodels/)的软件包具有相当完整的固定效果和随机效果实现,包括集群标准错误.它不使用高维OLS来消除效果,因此可以与大型数据集一起使用.

# Outer is entity, inner is time
entity = list(map(chr,range(65,91)))
time = list(pd.date_range('1-1-2014',freq='A', periods=4))
index = pd.MultiIndex.from_product([entity, time])
df = pd.DataFrame(np.random.randn(26*4, 2),index=index, columns=['y','x'])

from linearmodels.panel import PanelOLS
mod = PanelOLS(df.y, df.x, entity_effects=True)
res = mod.fit(cov_type='clustered', cluster_entity=True)
print(res)

这会产生以下输出:

                          PanelOLS Estimation Summary                           
================================================================================
Dep. Variable:                      y   R-squared:                        0.0029
Estimator:                   PanelOLS   R-squared (Between):             -0.0109
No. Observations:                 104   R-squared (Within):               0.0029
Date:                Thu, Jun 29 2017   R-squared (Overall):             -0.0007
Time:                        23:52:28   Log-likelihood                   -125.69
Cov. Estimator:             Clustered                                           
                                        F-statistic:                      0.2256
Entities:                          26   P-value                           0.6362
Avg Obs:                       4.0000   Distribution:                    F(1,77)
Min Obs:                       4.0000                                           
Max Obs:                       4.0000   F-statistic (robust):             0.1784
                                        P-value                           0.6739
Time periods:                       4   Distribution:                    F(1,77)
Avg Obs:                       26.000                                           
Min Obs:                       26.000                                           
Max Obs:                       26.000                                           

                             Parameter Estimates                              
==============================================================================
            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI
------------------------------------------------------------------------------
x              0.0573     0.1356     0.4224     0.6739     -0.2127      0.3273
==============================================================================

F-test for Poolability: 1.0903
P-value: 0.3739
Distribution: F(25,77)

Included effects: Entity

它还有一个类似于statsmodels的公式接口,

mod = PanelOLS.from_formula('y ~ x + EntityEffects', df)