linearmodels_results

Functions that extract and standardize results from `linearmodels.PanelOLS.fit()’ objects.

Each function extracts a particular statistic from the results object.

These functions must be consistent across the different statistical packages supported by estout.

For example, if we write functions to extract the F-statistic from the linearmodels and statsmodels packages, these functions must have the same name in the linearmodels_results and statsmodels_results modules. They must also have the same return type.

The names of these functions will be the keys of the dictionary returned by the collect_stats function in the core module.

import numpy as np
import pandas as pd

from linearmodels import PanelOLS

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

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)
lmres = PanelOLS(df['y'],  df[['cons','x','z']], entity_effects=True
                 ).fit(cov_type='clustered', cluster_entity=True)

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ynames

 ynames (res)

ynames(lmres)

['y']

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xnames

 xnames (res)

xnames(lmres)

['cons', 'x', 'z']

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params

 params (res)

params(lmres)

cons    0.728016
x       0.643274
z      -0.774956
Name: parameter, dtype: float64

type(params(lmres))

pandas.core.series.Series

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tstats

 tstats (res)

tstats(lmres)

cons    167.358372
x         2.262089
z        -2.909770
Name: tstat, dtype: float64

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pvalues

 pvalues (res)

pvalues(lmres)

cons    7.646419e-09
x       8.648041e-02
z       4.368821e-02
Name: pvalue, dtype: float64

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covmat

 covmat (res)

covmat(lmres)

	cons	x	z
cons	0.000019	-0.000652	0.000580
x	-0.000652	0.080867	-0.075700
z	0.000580	-0.075700	0.070931

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se

 se (res)

se(lmres)

cons    0.004350
x       0.284371
z       0.266329
Name: std_error, dtype: float64

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nobs

 nobs (res)

nobs(lmres)

9

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r2

 r2 (res)

r2(lmres)

0.35189790336774396