LinearFactorModels: Issue when number of portfolios larger than number of observations

[matbuechner]

I have been running into a problem when using the LinearFactorModel functionality, that only appears to occur when the number of portfolios is larger than the number of observations per portfolio. The error message pertains to a constant being present (which should not be the case here). My guess is that the has_constant util delivers a faulty output in this use case. The error received is the following:

File ".../anaconda3/lib/python3.6/site-packages/linearmodels/asset_pricing/model.py", line 331, in __init__ super(LinearFactorModel, self).__init__(portfolios, factors) File ".../anaconda3/lib/python3.6/site-packages/linearmodels/asset_pricing/model.py", line 67, in __init__ self._validate_data() File ".../anaconda3/lib/python3.6/site-packages/linearmodels/asset_pricing/model.py", line 102, in _validate_data raise ValueError('portfolios must not contains a constant or equivalent.') ValueError: portfolios must not contains a constant or equivalent.

To aid the debugging, I have constructed a minimal example. Please see below:

import pandas as pd
import numpy as np
from linearmodels.asset_pricing import LinearFactorModel
PF = pd.read_pickle('testPFs.pkl')
factors = pd.read_pickle('testfactors.pkl')

model = LinearFactorModel(PF,factors)
modelres = model.fit()

The necessary data is made available at the following location: https://www.dropbox.com/sh/h6iwxw8t6h0njig/AABcguLzp7XrmSXJ8wuYdygja?dl=0

Any help is very appreciated!

[bashtage]

Thanks. I've fixed this in master. The rank checks weren't correct for asset pricing models -- looked like a bit of copy/paste logic from more standard regressions.

[matbuechner]

Great, thanks!