Python线性模型PanelOLS和Stata的结果不同

Question

对于固定效果模型，我计划从Stata的areg切换到Python的linearmodels.panel.PanelOLS。

但结果是不同的。在Stata中，我得到了R-squared = 0.6047，在Python中，我得到了R-squared = 0.1454。

为什么我得到的R平方与下面的命令如此不同？

Stata命令和结果：

use ./linearmodels_datasets_wage_panel.dta, clear
areg lwage expersq union married hours, vce(cluster nr) absorb(nr)

Linear regression, absorbing indicators             Number of obs     =  4,360
Absorbed variable: nr                               No. of categories =    545
                                                    F(4, 544)         =  84.67
                                                    Prob > F          = 0.0000
                                                    R-squared         = 0.6047
                                                    Adj R-squared     = 0.5478
                                                    Root MSE          = 0.3582

                                   (Std. err. adjusted for 545 clusters in nr)
------------------------------------------------------------------------------
             |               Robust
       lwage | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
     expersq |   .0039509   .0002554    15.47   0.000     .0034492    .0044526
       union |   .0784442   .0252621     3.11   0.002      .028821    .1280674
     married |   .1146543   .0234954     4.88   0.000     .0685014    .1608072
       hours |  -.0000846   .0000238    -3.56   0.000    -.0001313   -.0000379
       _cons |   1.565825   .0531868    29.44   0.000     1.461348    1.670302
------------------------------------------------------------------------------

Python命令和结果：

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

data = wage_panel.load()

mod_entity = PanelOLS.from_formula(
    "lwage ~ 1 + expersq + union + married + hours + EntityEffects",
    data=data.set_index(["nr", "year"]),
)

result_entity = mod_entity.fit(
    cov_type='clustered',
    cluster_entity=True,
)

print(result_entity)

                          PanelOLS Estimation Summary                           
================================================================================
Dep. Variable:                  lwage   R-squared:                        0.1454
Estimator:                   PanelOLS   R-squared (Between):             -0.0844
No. Observations:                4360   R-squared (Within):               0.1454
Date:                Wed, Feb 02 2022   R-squared (Overall):              0.0219
Time:                        12:23:24   Log-likelihood                   -1416.4
Cov. Estimator:             Clustered                                           
                                        F-statistic:                      162.14
Entities:                         545   P-value                           0.0000
Avg Obs:                       8.0000   Distribution:                  F(4,3811)
Min Obs:                       8.0000                                           
Max Obs:                       8.0000   F-statistic (robust):             96.915
                                        P-value                           0.0000
Time periods:                       8   Distribution:                  F(4,3811)
Avg Obs:                       545.00                                           
Min Obs:                       545.00                                           
Max Obs:                       545.00                                           
                                                                                
                             Parameter Estimates                              
==============================================================================
            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI
------------------------------------------------------------------------------
Intercept      1.5658     0.0497     31.497     0.0000      1.4684      1.6633
expersq        0.0040     0.0002     16.550     0.0000      0.0035      0.0044
hours       -8.46e-05   2.22e-05    -3.8101     0.0001     -0.0001  -4.107e-05
married        0.1147     0.0220     5.2207     0.0000      0.0716      0.1577
union          0.0784     0.0236     3.3221     0.0009      0.0321      0.1247
==============================================================================

F-test for Poolability: 9.4833
P-value: 0.0000
Distribution: F(544,3811)

Included effects: Entity

Answer 1

天啊。你好吗？

您正在尝试运行吸收回归(.areg)。具体来说，你试图运行一个“线性回归吸收一个分类因素”。要做到这一点，只需运行以下模型linearmodels.iv.absorbing.AbsorbingLS(endog_variable, exog_variables, categorical_variable_absorb)

见下面的例子：

import pandas as pd
import statsmodels as sm
from linearmodels.iv import absorbing

dta = pd.read_csv('http://www.math.smith.edu/~bbaumer/mth247/labs/airline.csv')

dta.rename(columns={'I': 'airline', 
                    'T': 'year', 
                    'Q': 'output', 
                    'C': 'cost', 
                    'PF': 'fuel', 
                    'LF ': 'load'}, inplace=True)

接下来，将吸收变量转换为分类变量(在本例中，我将使用airline变量)：

cats = pd.DataFrame({'airline': pd.Categorical(dta['airline'])})

然后，运行模型：

exog_variables = ['output', 'fuel', 'load']
endog_variable = ['cost']

exog = sm.tools.tools.add_constant(dta[exog_variables])
endog = dta[endog_variable]

model = absorbing.AbsorbingLS(endog, exog, absorb=cats, drop_absorbed=True)
model_res = model.fit(cov_type='unadjusted', debiased=True)

print(model_res.summary)

下面是相同模型在python和stata中的结果(使用命令.areg cost output fuel load, absorb(airline))

Python：

                         Absorbing LS Estimation Summary                          
==================================================================================
Dep. Variable:                   cost   R-squared:                          0.9974
Estimator:               Absorbing LS   Adj. R-squared:                     0.9972
No. Observations:                  90   F-statistic:                        3827.4
Date:                Thu, Oct 27 2022   P-value (F-stat):                   0.0000
Time:                        20:58:04   Distribution:                      F(3,81)
Cov. Estimator:            unadjusted   R-squared (No Effects):             0.9926
                                        Varaibles Absorbed:                 5.0000
                             Parameter Estimates                              
==============================================================================
            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI
------------------------------------------------------------------------------
const          9.7135     0.2229     43.585     0.0000      9.2701      10.157
output         0.9193     0.0290     31.691     0.0000      0.8616      0.9770
fuel           0.4175     0.0148     28.303     0.0000      0.3881      0.4468
load          -1.0704     0.1957    -5.4685     0.0000     -1.4599     -0.6809
==============================================================================

斯塔塔：

Linear regression, absorbing indicators Number of obs = 90
 F( 3, 81) = 3604.80
 Prob > F = 0.0000
 R-squared = 0.9974
 Adj R-squared = 0.9972
 Root MSE = .06011
------------------------------------------------------------------------------
 cost | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
 output | .9192846 .0298901 30.76 0.000 .8598126 .9787565
 fuel | .4174918 .0151991 27.47 0.000 .3872503 .4477333 
 load | -1.070396 .20169 -5.31 0.000 -1.471696 -.6690963
 _cons | 9.713528 .229641 42.30 0.000 9.256614 10.17044
-------------+----------------------------------------------------------------
 airline | F(5, 81) = 57.732 0.000 (6 categories)