Why am I getting "LinAlgError: Singular matrix" from grangercausalitytests?

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I am trying to run grangercausalitytests on two time series:

import numpy as np
import pandas as pd

from statsmodels.tsa.stattools import grangercausalitytests

n = 1000
ls = np.linspace(0, 2*np.pi, n)

df1 = pd.DataFrame(np.sin(ls))
df2 = pd.DataFrame(2*np.sin(1+ls))

df = pd.concat([df1, df2], axis=1)

df.plot()

grangercausalitytests(df, maxlag=20)

However, I am getting

Granger Causality
number of lags (no zero) 1
ssr based F test:         F=272078066917221398041264652288.0000, p=0.0000  , df_denom=996, df_num=1
ssr based chi2 test:   chi2=272897579166972095424217743360.0000, p=0.0000  , df=1
likelihood ratio test: chi2=60811.2671, p=0.0000  , df=1
parameter F test:         F=272078066917220553616334520320.0000, p=0.0000  , df_denom=996, df_num=1

Granger Causality
number of lags (no zero) 2
ssr based F test:         F=7296.6976, p=0.0000  , df_denom=995, df_num=2
ssr based chi2 test:   chi2=14637.3954, p=0.0000  , df=2
likelihood ratio test: chi2=2746.0362, p=0.0000  , df=2
parameter F test:         F=13296850090491009488285469769728.0000, p=0.0000  , df_denom=995, df_num=2
...
/usr/local/lib/python3.5/dist-packages/numpy/linalg/linalg.py in _raise_linalgerror_singular(err, flag)
     88 
     89 def _raise_linalgerror_singular(err, flag):
---> 90     raise LinAlgError("Singular matrix")
     91 
     92 def _raise_linalgerror_nonposdef(err, flag):

LinAlgError: Singular matrix

and I am not sure why this is the case.

statsmodels python
2 Answers

Another thing to keep an eye out for is duplicate columns. Duplicate columns will have a correlation score of 1.0, resulting in singularity. Otherwise, it's also possible you have 2 features that are perfectly correlated. And easy way to check this is with df.corr(), and look for pairs of columns with correlation = 1.0.

user_120815710
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