linearmodels panelOLS: Regression output with stars

Question

I'm using the linearmodels package to estimate a Panel-OLS. As an example see:

import numpy as np
from statsmodels.datasets import grunfeld
data = grunfeld.load_pandas().data
data.year = data.year.astype(np.int64)
# MultiIndex, entity - time
data = data.set_index(['firm','year'])
from linearmodels import PanelOLS
mod = PanelOLS(data.invest, data[['value','capital']], entity_effect=True)
res = mod.fit(cov_type='clustered', cluster_entity=True)

I want to export the regression's output in a .tex file. Is there a convenient way of formatting the output with confidence stars and without the other information like the CIs? The question has been asked in the context of a standard OLS in here but this does not apply for a 'PanelEffectsResults' object, since I get the following error:

'PanelEffectsResults' object has no attribute 'bse'

Thanks in advance.

Answer 1

A bit late but here is what I use. In the example above I calculated two fixed effects regressions with their results stored in fe_res_VS and fe_res_CVS:

pd.set_option('precision', 4)
pd.options.display.float_format = '{:,.4f}'.format
Reg_Output_FAmount= pd.DataFrame()


#1) 
Table1 = pd.DataFrame(fe_res_VS.params)
Table1['id'] = np.arange(len(Table1))#create numerical index for pd.DataFrame
Table1 = Table1.reset_index().set_index(keys = 'id')#set numercial index as new index
Table1 = Table1.rename(columns={"index":"parameter", "parameter":"coefficient 1"})

P1 = pd.DataFrame(fe_res_VS.pvalues)
P1['id'] = np.arange(len(P1))#create numerical index for pd.DataFrame
P1 = P1.reset_index().set_index(keys = 'id')#set numercial index as new index
P1 = P1.rename(columns={"index":"parameter"})

Table1 = pd.merge(Table1, P1, on='parameter')
Table1['significance 1'] = np.where(Table1['pvalue'] <= 0.01, '***',\
       np.where(Table1['pvalue'] <= 0.05, '**',\
       np.where(Table1['pvalue'] <= 0.1, '*', '')))
Table1.rename(columns={"pvalue": "pvalue 1"}, inplace=True) 


SE1 = pd.DataFrame(fe_res_VS.std_errors)
SE1['id'] = np.arange(len(SE1))#create numerical index for pd.DataFrame
SE1 = SE1.reset_index().set_index(keys = 'id')#set numercial index as new index
SE1 = SE1.rename(columns={"index":"parameter", "std_error":"coefficient 1"})
SE1['parameter'] =  SE1['parameter'].astype(str) + '_SE'
SE1['significance 1'] = ''
SE1 = SE1.round(4)

SE1['coefficient 1'] = '(' + SE1['coefficient 1'].astype(str) + ')'
Table1 = Table1.append(SE1)
Table1 = Table1.sort_values('parameter')
Table1.replace(np.nan,'', inplace=True)
del P1
del SE1


#2) 
Table2 = pd.DataFrame(fe_res_CVS.params)
Table2['id'] = np.arange(len(Table2))#create numerical index for pd.DataFrame
Table2 = Table2.reset_index().set_index(keys = 'id')#set numercial index as new index
Table2 = Table2.rename(columns={"index":"parameter", "parameter":"coefficient 2"})

P2 = pd.DataFrame(fe_res_CVS.pvalues)
P2['id'] = np.arange(len(P2))#create numerical index for pd.DataFrame
P2 = P2.reset_index().set_index(keys = 'id')#set numercial index as new index
P2 = P2.rename(columns={"index":"parameter"})

Table2 = pd.merge(Table2, P2, on='parameter')
Table2['significance 2'] = np.where(Table2['pvalue'] <= 0.01, '***',\
       np.where(Table2['pvalue'] <= 0.05, '**',\
       np.where(Table2['pvalue'] <= 0.1, '*', '')))
Table2.rename(columns={"pvalue": "pvalue 2"}, inplace=True) 

SE2 = pd.DataFrame(fe_res_CVS.std_errors)
SE2['id'] = np.arange(len(SE2))#create numerical index for pd.DataFrame
SE2 = SE2.reset_index().set_index(keys = 'id')#set numercial index as new index
SE2 = SE2.rename(columns={"index":"parameter", "std_error":"coefficient 2"})
SE2['parameter'] =  SE2['parameter'].astype(str) + '_SE'
SE2['significance 2'] = ''
SE2 = SE2.round(4)

SE2['coefficient 2'] = '(' + SE2['coefficient 2'].astype(str) + ')'
Table2 = Table2.append(SE2)
Table2 = Table2.sort_values('parameter')
Table2.replace(np.nan,'', inplace=True)
del P2
del SE2


#Merging Tables and adding Stats
Reg_Output_FAmount= pd.merge(Table1, Table2, on='parameter', how='outer')

Reg_Output_FAmount = Reg_Output_FAmount.append(pd.DataFrame(np.array([["observ.", fe_res_VS.nobs, '', fe_res_CVS.nobs, '']]), columns=['parameter', 'pvalue 1', 'significance 1', 'pvalue 2', 'significance 2']), ignore_index=True)
Reg_Output_FAmount = Reg_Output_FAmount.append(pd.DataFrame(np.array([["Rsquared", "{:.4f}".format(fe_res_VS.rsquared), '',  "{:.4f}".format(fe_res_CVS.rsquared), '']]), columns=['parameter', 'pvalue 1', 'significance 1', 'pvalue 2', 'significance 2']), ignore_index=True)
Reg_Output_FAmount= Reg_Output_FAmount.append(pd.DataFrame(np.array([["Model type", fe_res_VS.name, '', fe_res_CVS.name, '']]), columns=['parameter', 'pvalue 1', 'significance 1', 'pvalue 2', 'significance 2']), ignore_index=True)
Reg_Output_FAmount = Reg_Output_FAmount.append(pd.DataFrame(np.array([["DV", fe_res_VS.model.dependent.vars[0], '', fe_res_CVS.model.dependent.vars[0], '']]), columns=['parameter', 'pvalue 1', 'significance 1', 'pvalue 2', 'significance 2']), ignore_index=True)

Reg_Output_FAmount.fillna('', inplace=True)

resulting in a nice regression output looking like that:

    parameter   coefficient 1   pvalue 1    significance 1  coefficient 2   pvalue 2   significance 2
0   IV          0.0676          0.2269                      0.0732          0.1835  
1   IV_SE       (0.0559)                                    (0.055)     
2   Control     0.3406          0.0125      **              0.3482          0.0118      **
3   Control_SE  (0.1363)        0.1383)     
4   const       0.2772          0.0000      ***             0.2769          0.0000      ***
5   const_SE    (0.012)         (0.012)     
6   observ.                     99003                                       99003   
7   Rsquared                    0.12                                        0.14    
8   Model type                  PanelOLS                                    PanelOLS    
9   DV                          FAmount                                     FAmount

Answer 2

Have been struggling with the same problem for a few days. Very excited to share with my peers a very easy way to do it: include the significance stars, remove CIs. Here it is:

Step 1: install linearmodels package.

Step 2: import compare function from linearmodels.panel

from linearmodels.panel import compare

Step3: Use compare function and specify the arguments as you want in compare. For instance, specifying stars = True will give you significance stars. Very convenient!

compare({'model_A_name': results of model_A, 'model_B_name': results of model_B, }, stars = True)

This small function saved my life! Enjoy it.

One more thing, please know that the stars are based on the p-value of the coefficient where 1, 2 and 3-stars correspond to p-values of 10%, 5% and 1%, respectively. I am not sure whether there is a way to make a customized stars measurement, like 1, 2 and 3-stars correspond to p-values of 5%, 1% and 0.1%.

The credit goes to the fantastic package developer and maintainer. Thank you all! Please see the file and get more information at: ~/opt/anaconda3/lib/python3.7/site-packages/linearmodels/panel/results.py