Panel data regression with fixed effects using Python

Question

I have the following panel stored in df:

	state	district	year	y	constant	x1	x2	time
0	01	01001	2009	12	1	0.956007	639673	1
1	01	01001	2010	20	1	0.972175	639673	2
2	01	01001	2011	22	1	0.988343	639673	3
3	01	01002	2009	0	1	0	33746	1
4	01	01002	2010	1	1	0.225071	33746	2
5	01	01002	2011	5	1	0.450142	33746	3
6	01	01003	2009	0	1	0	45196	1
7	01	01003	2010	5	1	0.427477	45196	2
8	01	01003	2011	9	1	0.854955	45196	3

y is the number of protests in each district
constant is a column full of ones
x1 is the proportion of the district's area covered by a mobile network provider
x2 is the population count in each district (note that it is fixed in time)

How can I run the following model in Python?

Here's what I tried

# Transform `x2` to match model
df['x2'] = df['x2'].multiply(df['time'], axis=0)
# District fixed effects
df['delta'] = pd.Categorical(df['district'])
# State-time fixed effects
df['eta'] = pd.Categorical(df['state'] + df['year'].astype(str))
# Set indexes
df.set_index(['district','year'])

from linearmodels.panel import PanelOLS
m = PanelOLS(dependent=df['y'], exog=df[['constant','x1','x2','delta','eta']])

ValueError: exog does not have full column rank. If you wish to proceed with model estimation irrespective of the numerical accuracy of coefficient estimates, you can set rank_check=False.

What am I doing wrong?

Arturo Sbr · Accepted Answer · 2021-09-22 14:51:45Z

9

I dug around the documentation and the solution turned out to be quite simple.

After setting the indexes and turning the fixed effect columns to pandas.Categorical types (see question above):

# Import model
from linearmodels.panel import PanelOLS

# Model
m = PanelOLS(dependent=df['y'],
             exog=df[['constant','x1','x2']],
             entity_effects=True,
             time_effects=False,
             other_effects=df['eta'])
m.fit(cov_type='clustered', cluster_entity=True)

That is, DO NOT pass your fixed effect columns to exog.

You should pass them to entity_effects (boolean), time_effects (boolean) or other_effects (pandas.Categorical).

edited Sep 22, 2021 at 14:51

answered Sep 21, 2021 at 1:24

Arturo Sbr

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5 Comments

asd Over a year ago

Thanks, are you saying that "delta" in this example should not go in "exog" but that it should go in "other_effects"?

Arturo Sbr Over a year ago

No. Delta are the "individuals" if you may. Hence, you make delta the index and set entity_effects = True. In my example, eta is passed as other_effects.

asd Over a year ago

Thank you. How could I see the formula of the model? I tried "mod.formula". It ran but nothing happens

Arturo Sbr Over a year ago

Perhaps you could try print(mod.formula), but to be honest I don't know.

Kevin S Over a year ago

You can only see the formula when you create a model using the formula interface (e.g. PanelOLS.from_formula). If you create it using DataFrames then there is no formula.

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