Fixed effect in Pandas or Statsmodels
As noted in the comments, PanelOLS has been removed from Pandas as of version 0.20.0. So you really have three options:
If you use Python 3 you can use
linearmodelsas specified in the more recent answer: https://stackoverflow.com/a/44836199/3435183Just specify various dummies in your
statsmodelsspecification, e.g. usingpd.get_dummies. May not be feasible if the number of fixed effects is large.-
Or do some groupby based demeaning and then use
statsmodels(this would work if you're estimating lots of fixed effects). Here is a barebones version of what you could do for one way fixed effects:import statsmodels.api as sm import statsmodels.formula.api as smf import patsy def areg(formula,data=None,absorb=None,cluster=None): y,X = patsy.dmatrices(formula,data,return_type='dataframe') ybar = y.mean() y = y - y.groupby(data[absorb]).transform('mean') + ybar Xbar = X.mean() X = X - X.groupby(data[absorb]).transform('mean') + Xbar reg = sm.OLS(y,X) # Account for df loss from FE transform reg.df_resid -= (data[absorb].nunique() - 1) return reg.fit(cov_type='cluster',cov_kwds={'groups':data[cluster].values})
For example, suppose you have a panel of stock data: stock returns and other stock data for all stocks, every month over a number of months and you want to regress returns on lagged returns with calendar month fixed effects (where the calender month variable is called caldt) and you also want to cluster the standard errors by calendar month. You can estimate such a fixed effect model with the following:
reg0 = areg('ret~retlag',data=df,absorb='caldt',cluster='caldt')
And here is what you can do if using an older version of Pandas:
An example with time fixed effects using pandas' PanelOLS (which is in the plm module). Notice, the import of PanelOLS:
>>> from pandas.stats.plm import PanelOLS
>>> df
y x
date id
2012-01-01 1 0.1 0.2
2 0.3 0.5
3 0.4 0.8
4 0.0 0.2
2012-02-01 1 0.2 0.7
2 0.4 0.5
3 0.2 0.3
4 0.1 0.1
2012-03-01 1 0.6 0.9
2 0.7 0.5
3 0.9 0.6
4 0.4 0.5
Note, the dataframe must have a multindex set ; panelOLS determines the time and entity effects based on the index:
>>> reg = PanelOLS(y=df['y'],x=df[['x']],time_effects=True)
>>> reg
-------------------------Summary of Regression Analysis-------------------------
Formula: Y ~ <x>
Number of Observations: 12
Number of Degrees of Freedom: 4
R-squared: 0.2729
Adj R-squared: 0.0002
Rmse: 0.1588
F-stat (1, 8): 1.0007, p-value: 0.3464
Degrees of Freedom: model 3, resid 8
-----------------------Summary of Estimated Coefficients------------------------
Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%
--------------------------------------------------------------------------------
x 0.3694 0.2132 1.73 0.1214 -0.0485 0.7872
---------------------------------End of Summary---------------------------------
Docstring:
PanelOLS(self, y, x, weights = None, intercept = True, nw_lags = None,
entity_effects = False, time_effects = False, x_effects = None,
cluster = None, dropped_dummies = None, verbose = False,
nw_overlap = False)
Implements panel OLS.
See ols function docs
This is another function (like fama_macbeth) where I believe the plan is to move this functionality to statsmodels.
There is a package called linearmodels (https://pypi.org/project/linearmodels/) that has a fairly complete fixed effects and random effects implementation including clustered standard errors. It does not use high-dimensional OLS to eliminate effects and so can be used with large data sets.
# Outer is entity, inner is time
entity = list(map(chr,range(65,91)))
time = list(pd.date_range('1-1-2014',freq='A', periods=4))
index = pd.MultiIndex.from_product([entity, time])
df = pd.DataFrame(np.random.randn(26*4, 2),index=index, columns=['y','x'])
from linearmodels.panel import PanelOLS
mod = PanelOLS(df.y, df.x, entity_effects=True)
res = mod.fit(cov_type='clustered', cluster_entity=True)
print(res)
This produces the following output:
PanelOLS Estimation Summary
================================================================================
Dep. Variable: y R-squared: 0.0029
Estimator: PanelOLS R-squared (Between): -0.0109
No. Observations: 104 R-squared (Within): 0.0029
Date: Thu, Jun 29 2017 R-squared (Overall): -0.0007
Time: 23:52:28 Log-likelihood -125.69
Cov. Estimator: Clustered
F-statistic: 0.2256
Entities: 26 P-value 0.6362
Avg Obs: 4.0000 Distribution: F(1,77)
Min Obs: 4.0000
Max Obs: 4.0000 F-statistic (robust): 0.1784
P-value 0.6739
Time periods: 4 Distribution: F(1,77)
Avg Obs: 26.000
Min Obs: 26.000
Max Obs: 26.000
Parameter Estimates
==============================================================================
Parameter Std. Err. T-stat P-value Lower CI Upper CI
------------------------------------------------------------------------------
x 0.0573 0.1356 0.4224 0.6739 -0.2127 0.3273
==============================================================================
F-test for Poolability: 1.0903
P-value: 0.3739
Distribution: F(25,77)
Included effects: Entity
It also has a formula interface which is similar to statsmodels,
mod = PanelOLS.from_formula('y ~ x + EntityEffects', df)