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.

### 2 Answers

The problem arises due to the perfect correlation between the two series in your data. From the traceback, you can see, that internally a wald test is used to compute the maximum likelihood estimates for the parameters of the lag-time series. To do this an estimate of the parameters covariance matrix (which is then near-zero) and its inverse is needed (as you can also see in the line `invcov = np.linalg.inv(cov_p)`

in the traceback). This near-zero matrix is now singular for some maximum lag number (>=5) and thus the test crashes. If you add just a little noise to your data, the error disappears:

```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import grangercausalitytests
n = 1000
ls = np.linspace(0, 2*np.pi, n)
df1Clean = pd.DataFrame(np.sin(ls))
df2Clean = pd.DataFrame(2*np.sin(ls+1))
dfClean = pd.concat([df1Clean, df2Clean], axis=1)
dfDirty = dfClean+0.00001*np.random.rand(n, 2)
grangercausalitytests(dfClean, maxlag=20, verbose=False) # Raises LinAlgError
grangercausalitytests(dfDirty, maxlag=20, verbose=False) # Runs fine
```

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.