estout 0.0.2

Collect regression results and export them to LaTex and pdf

estout

Install

pip install estout

How to use

Set up an example dataset and run a few regressions to showcase the functions in this module.

import numpy as np
import pandas as pd
import statsmodels.api as sm
from linearmodels import PanelOLS
import estout

np.random.seed(123)
df = pd.DataFrame(np.random.rand(9,3), 
                  columns=['y','x','z'],
                  index = pd.MultiIndex.from_product([[1,2,3],[1,2,3]], names=['firmid','time'])
                  ).assign(cons = 1)
sm1 = sm.OLS(df['y'], df[['cons','x']]).fit()
sm2 = sm.OLS(df['y'], df[['cons','x','z']]).fit().get_robustcov_results(cov_type='HAC', maxlags=2)
lmres = PanelOLS(df['y'],  df[['cons','x','z']], entity_effects=True
                 ).fit(cov_type='clustered', cluster_entity=True)

Extracting statistics after fitting a model

Below, we collect just the default set of statistics from the sm1 object. These are given by the functions implemented in the statsmodels_results module (since sm1 was generated by the statsmodels package).

estout.collect_stats(sm1)

{'package': 'statsmodels',
 'ynames': ['y'],
 'xnames': ['cons', 'x'],
 'params': cons    0.507852
 x       0.345003
 dtype: float64,
 'tstats': cons    3.905440
 x       1.292246
 dtype: float64,
 'pvalues': cons    0.005858
 x       0.237293
 dtype: float64,
 'covmat':           cons         x
 cons  0.016910 -0.030531
 x    -0.030531  0.071278,
 'se': cons    0.130037
 x       0.266979
 dtype: float64,
 'nobs': 9,
 'r2': 0.19260886185799475}

Collect statistics by specifying the name of their attribute in the results object (using the add_stats parameter):

estout.collect_stats(sm1, get_default_stats=False, add_stats={'xnames': 'model.exog_names',
                                                              'Adj. R2': 'rsquared_adj'})

{'package': 'statsmodels',
 'xnames': ['cons', 'x'],
 'Adj. R2': 0.07726727069485118}

Add scalar statistics not available as attributes of the results object (using the add_literals paramter):

estout.collect_stats(sm1, get_default_stats=False, add_literals={'Fixed Effects': 'No', 
                                                                 'Nr observations': 123})

{'package': 'statsmodels', 'Fixed Effects': 'No', 'Nr observations': 123}

Combining model results into a DataFrame

Start by collecting stats from each model and combining them in a list.

allmodels = []
for res in [sm1, sm2, lmres]:
    allmodels.append(estout.collect_stats(res))

Then export them to a DataFrame.

estout.to_df(allmodels)

		0	1	2
cons	params	0.51***	0.70***	0.73***
	tstats	(3.91)	(21.48)	(167.36)
x	params	0.35	0.57**	0.64*
	tstats	(1.29)	(2.85)	(2.26)
z	params		-0.64**	-0.77**
	tstats		(-3.55)	(-2.91)
r2		0.193	0.487	0.352
nobs		9	9	9

We can choose to report only a subset of the regressors.

estout.to_df(allmodels, which_xvars=['x','z'])

		0	1	2
x	params	0.35	0.57**	0.64*
	tstats	(1.29)	(2.85)	(2.26)
z	params		-0.64**	-0.77**
	tstats		(-3.55)	(-2.91)
r2		0.193	0.487	0.352
nobs		9	9	9

Report other statistics under the parameter values.

estout.to_df(allmodels, stats_body=['params','se','pvalues'], which_xvars=['x'])

		0	1	2
x	params	0.35	0.57**	0.64*
	se	(0.27)	(0.20)	(0.28)
	pvalues	(0.237)	(0.029)	(0.086)
r2		0.193	0.487	0.352
nobs		9	9	9

Change the statistics reported at the bottom of the table

estout.to_df(allmodels, stats_bottom=['r2'],  which_xvars=['x'])

		0	1	2
x	params	0.35	0.57**	0.64*
	tstats	(1.29)	(2.85)	(2.26)
r2		0.193	0.487	0.352

Change the formatting for any of the statistics reported.

estout.to_df(allmodels, add_formats={'params':'{:.3}','r2':'{:.2f}'}, which_xvars=['x'])

		0	1	2
x	params	0.345	0.571**	0.643*
	tstats	(1.29)	(2.85)	(2.26)
r2		0.19	0.49	0.35
nobs		9	9	9

Replace names of regressor (or bottom stats) with labels.

estout.to_df(allmodels, labels={'cons':'Intercept', 'nobs':'Observations'}, which_xvars=['cons'])

		0	1	2
Intercept	params	0.51***	0.70***	0.73***
	tstats	(3.91)	(21.48)	(167.36)
r2		0.193	0.487	0.352
Observations		9	9	9

Since the output of to_df is a pd.DataFrame, it is easy to add more information at the bottom of the table without having to re-run collect_stats.

df = estout.to_df(allmodels)
df.loc['Fixed effects',:] = ['No','No','Entity']
df

		0	1	2
cons	params	0.51***	0.70***	0.73***
	tstats	(3.91)	(21.48)	(167.36)
x	params	0.35	0.57**	0.64*
	tstats	(1.29)	(2.85)	(2.26)
z	params		-0.64**	-0.77**
	tstats		(-3.55)	(-2.91)
r2		0.193	0.487	0.352
nobs		9	9	9
Fixed effects		No	No	Entity

Exporting to LaTex

With the estout.to_tex function, we can combine one or more DataFrames into a single LaTex table (each DataFrame will be a separate panel in the LaTex table).

In the example below, we just return the tex code as a string, but the function also takes an outfile parameter that allows us to store the output in a .tex file. Either the file path or the string can be used in the estout.to_pdf function to create a PDF out of this tex code.

tbl = estout.to_tex([df,df], panel_title=['Panel A: Some title', 'Panel B: Some title'], 
               col_groups=[{'Group1':[1,2]}]*2,
               col_names=[['Model 1', 'Model 2', 'Model 3']]*2,
               hlines=[[0,1,4,13], [1,4,13]] )
print(tbl)

\newpage 
 \clearpage 
 \begin{table}[!h] \footnotesize 
\addtocounter{table}{0} 
\caption{\textbf{Table title}} 
\par {Table description} 

 \vspace{2mm} 

 \begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}l*{3}{c}} 
 \hline \noalign{\smallskip} 
\multicolumn{4}{@{} l}{Panel A: Some title} \\ 
 \hline \noalign{\smallskip} 
& \multicolumn{2}{c}{Group1}  \\ 
\cline{2-3}  
 & Model 1 & Model 2 & Model 3 \\ 
 \hline \noalign{\smallskip} 
cons & 0.51*** & 0.70*** & 0.73*** \\ 
 & (3.91) & (21.48) & (167.36) \\ 
x & 0.35 & 0.57** & 0.64* \\ 
 & (1.29) & (2.85) & (2.26) \\ 
z &  & -0.64** & -0.77** \\ 
 &  & (-3.55) & (-2.91) \\ 
r2 & 0.193 & 0.487 & 0.352 \\ 
nobs & 9 & 9 & 9 \\ 
Fixed effects & No & No & Entity \\ 
 \hline \noalign{\smallskip} 
\end{tabular*}
 \smallskip 
\begin{tabular*}{\textwidth}{@{\extracolsep{\fill}}l*{3}{c}} 
\multicolumn{4}{@{} l}{Panel B: Some title} \\ 
 \hline \noalign{\smallskip} 
& \multicolumn{2}{c}{Group1}  \\ 
\cline{2-3}  
 & Model 1 & Model 2 & Model 3 \\ 
 \hline \noalign{\smallskip} 
cons & 0.51*** & 0.70*** & 0.73*** \\ 
 & (3.91) & (21.48) & (167.36) \\ 
x & 0.35 & 0.57** & 0.64* \\ 
 & (1.29) & (2.85) & (2.26) \\ 
z &  & -0.64** & -0.77** \\ 
 &  & (-3.55) & (-2.91) \\ 
r2 & 0.193 & 0.487 & 0.352 \\ 
nobs & 9 & 9 & 9 \\ 
Fixed effects & No & No & Entity \\ 
 \hline \noalign{\smallskip} 
\end{tabular*} 
\label{} 
 \end{table} 

Exporting to PDF

With the estout.to_pdf function, we can combine the LaTex code for multiple tables (like the ones produced by estout.to_tex) into a single .tex document.

By default, the resulting .tex file is run through TexLive’s pdflatex utility to produce a PDF document with the tables (set make_pdf=False if you do not want the PDF to be automatically produced).

You can also set open_pdf to True if you want the resulting pdf to be opened after it is produced.

For the code below to work, you need to have TexLive installed (and change the path below to a valid path on your system).

estout.to_pdf(outfile='../_outputs/paper.tex', 
              table_tex_code=[tbl, tbl],
              make_pdf=True,
              open_pdf=False)

PDF creation successful!

This produced a PDF with two tables (given by the tbl tex string), each with two panels (given by the df DataFrame above).

Note that the table_tex_code parameter also accepts paths to tex files. Those tex files must have the complete table environment for that table (i.e. from ).