Multivariate VAR model: ValueError: x already contains a constant I have already read this question and the associated answer.

I have removed any 'all zero' columns, as recommended in the answer. I have 3,169 columns remaining.

datavals_no_con = datavals.loc[:, (datavals != datavals.iloc[0]).any()]


I checked whether any were missed, for some bizarre reason:

varcon = np.asarray([np.var(datavals_no_con[datavals_no_con.columns[i]]) for i in range(len(datavals_no_con.columns))])
print np.where(varcon==0.) #empty array.


Also checked the minimum column variance value, which ended up being 4.306x10^(-7)

This was generated by a column that has no zero entries.

When I run this:

model = VAR(datavals_no_con)

results = model.fit(2)


I still get:

Traceback (most recent call last):
  File "vector_autoregression.py", line 163, in &lt;module&gt;
    results = model.fit(2)
  File "/user/anaconda2/lib/python2.7/site-packages/statsmodels/tsa/vector_ar/var_model.py", line 438, in fit
    return self._estimate_var(lags, trend=trend)
  File "/user/anaconda2/lib/python2.7/site-packages/statsmodels/tsa/vector_ar/var_model.py", line 457, in _estimate_var
    z = util.get_var_endog(y, lags, trend=trend, has_constant='raise')
  File "/user/anaconda2/lib/python2.7/site-packages/statsmodels/tsa/vector_ar/util.py", line 32, in get_var_endog
    has_constant=has_constant)
  File "/user/anaconda2/lib/python2.7/site-packages/statsmodels/tsa/tsatools.py", line 102, in add_trend
    raise ValueError("x already contains a constant")
ValueError: x already contains a constant


How can I resolve this?

EDIT: It occurred to me that the problem would be that x contains a constant, not that x contains all 0s. So the original answer suggested in the previous question was not entirely sufficient.

To test whether any of my columns contained 'all the same value' (e.g. a column of all 0.5), I tried this:

ptplist = []
for i in range(len(datavals_no_con.columns)):
    ptplist.append(np.ptp(datavals_no_con[datavals_no_con.columns[i]], axis=0))

ptparray = np.asarray(ptplist)
print any(ptparray==0.) #FALSE


So none of my columns are constant, unless I'm still missing something.

EDIT 2: I have found the root cause of the problem. 

Suppose my input matrix (that is, my set of endogenous variables) is a 5x5 identity matrix, for the sake of argument, and that my lag value is 2 (that is, I'm looking for an AR(2) model: y_{t+1} = A + B_1y_{t} + B_2y_{t-1} + error) :

y = np.eye(5)

1 0 0 0 0 (row 1)
0 1 0 0 0 (row 2)
0 0 1 0 0 (row 3)
0 0 0 1 0 (row 4)
0 0 0 0 1 (row 5)


In the get_var_endog function in /statsmodels/tsa/util.py, under lags=2, the y matrix gets rearranged to this general idea:

[row 2, row 1] (i.e. concatenate these two)
[row 3, row 2]
[row 4, row 3]


And this new matrix could have zero columns, in places where my original data matrix did not. In fact, this is exactly what was happening. Following my example, the np.array Z in get_endog_var looks like this:

0 1 0 0 0 1 0 0 0 0
0 0 1 0 0 0 1 0 0 0
0 0 0 1 0 0 0 1 0 0


So now columns 0, 4, 8, and 9 are completely 0, which throws the ValueError. 

Two possible approaches to a solution come to mind:

1) Remove the zero columns from the Z matrix. 

2) Edit the original data set such that these zero columns never occur in the first place (much harder, because then the Z matrix here would never have existed, so how can you know which columns to remove...catch 22).

I chose option 1, but now I'm dealing with shape issues down the line. Because, of course, when doing the least squares fit, the shape of the parameters is going to be different from the shape of the original data set (some columns don't exist in the parameters, because I removed them, that do exist in the original data set).

Now, this looks like it should be a relatively frequent problem. A lot of the time, we're working with high-dimensional sparse data, which would generate this issue.
Does anyone have a more robust solution than what I've proposed?
It seems you may have things working, but maybe I can still help

Catching those pesky non-varying features

Assuming a is your dataframe, with only numerical types and columns as features, you can try the following, which will return rows filled with NaN values for features of zero variance i.e. the mean value is equal to the minimum value, so the value cannot vary.

a.T.where(a.T['mean'] == a.T['min'])


They check is slightly differently in the source code of the VAR model which raises your error:

result = (np.ptp(s) == 0.0 and np.any(s != 0.0))


So they check that the value doesn't have any peaks (ptp = "peak-to-peak"), so it doesn't vary - and also that the value is not equal to zero.

In any case, there is little tolerance for it. You have done right by trying to prune out features with low (near zero) variance.

Another possiblity which I didn't notice you having already tried, would be to utilise the other arguments to the fit() method of the VAR model class. It has the following signature:

VAR.fit(maxlags=None, method='ols', ic=None, trend='c', verbose=False)


If you were to change the trend argument to nc, meaning no constant - you may also get around your error. Check out the documentation.

Source code analysis

Looking through the contents of the file that raised your error, it seems it occurs while processing the "original input data" - as defined by the function add_trend (where your error was raised).

This suggest that your reasoning about the error being found on the data after lagged features are produced would be incorrect in this case (although it's a clever idea!)

Clearing my name...

You said:


  ... So the original answer suggested in the previous question was not entirely sufficient.


I would like to point out that my answer to your linked question was indeed sufficient, because I had written that there are constants - not that the values must all be equal to zero!  :-P