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:

1. If you use Python 3 you can use linearmodels as specified in the more recent answer: https://stackoverflow.com/a/44836199/3435183

2. Just specify various dummies in your statsmodels specification, e.g. using pd.get_dummies. May not be feasible if the number of fixed effects is large.

3. 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.