statsmodel: panel regression
Question:
I am currently using from pandas.stats.plm import PanelOLS to run Panel regressions. I am needing to switch to statsmodel so that I can ouput heteroskedastic robust results. I have been unable to find notation on calling a panel regression for statsmodel. In general, I find the documentation for statsmodel not very user friendly. Is someone familiar with panel regression syntax in statsmodel?
Answers:
The linearmodels package is created to extend the statsmodels package to panelOLS (see https://github.com/bashtage/linearmodels). Here is the example from the package doc:
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)
Best Daniel
I am currently using from pandas.stats.plm import PanelOLS to run Panel regressions. I am needing to switch to statsmodel so that I can ouput heteroskedastic robust results. I have been unable to find notation on calling a panel regression for statsmodel. In general, I find the documentation for statsmodel not very user friendly. Is someone familiar with panel regression syntax in statsmodel?
The linearmodels package is created to extend the statsmodels package to panelOLS (see https://github.com/bashtage/linearmodels). Here is the example from the package doc:
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)
Best Daniel