Fixed effect in Pandas or Statsmodels

Question

Is there an existing function to estimate fixed effect (one-way or two-way) from Pandas or Statsmodels.

There used to be a function in Statsmodels but it seems discontinued. And in Pandas, there is something called plm, but I can't import it or run it using pd.plm().

Answer 1

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.

Answer 2

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)