Fix the statsmodels SVAR 'zero-size array' ValueError: use 'E' placeholders in the A matrix instead of np.nan

Statsmodels SVAR fit fails with ValueError 'zero-size array to reduction operation'? Replace np.nan in the A matrix with 'E' and set dtype=object. Fix.

Fitting a Structural Vector Autoregression in Python is usually straightforward, until it suddenly isn’t. A common stumbling block appears as a ValueError that reads: “zero-size array to reduction operation maximum which has no identity.” If you see this while constructing SVAR with statsmodels and a custom A matrix, the root of the issue may be the way the constraints matrix is specified.

Repro case: how the error shows up

The following minimal example triggers the failure during fit when the A matrix includes np.nan entries.

import pandas as pd
import numpy as np
from statsmodels.tsa.vector_ar.svar_model import SVAR

ts_data = pd.DataFrame(
    {
        'Item_1': np.random.rand(240) * 10000,
        'Item_2': np.random.rand(240) * 10000,
        'Item_3': np.random.rand(240) * 10000
    },
    index=pd.date_range(start='2019-11-16', periods=240, freq='W-SAT')
)

A_bad = np.array([
    [1, 0, 0],
    [np.nan, 1, 0],
    [np.nan, np.nan, 1]
], dtype='U')

svar_bad = SVAR(ts_data, svar_type='A', A=A_bad)
fit_bad = svar_bad.fit(maxlags=4)

What’s going on

The failure is triggered by using np.nan inside the A matrix. In this setup, statsmodels raises the “zero-size array to reduction operation maximum which has no identity” error during fitting. In practice, replacing np.nan with the expected marker resolves the issue in a reproducible way.

Solution: use 'E' placeholders in A

Switching np.nan to the 'E' notation eliminates the error. The following example mirrors the setup above but uses 'E' entries and an object dtype for the A matrix.

import pandas as pd
import numpy as np
from statsmodels.tsa.vector_ar.svar_model import SVAR

sample_df = pd.DataFrame(
    {
        'Series_A': np.random.rand(240) * 10000,
        'Series_B': np.random.rand(240) * 10000,
        'Series_C': np.random.rand(240) * 10000
    },
    index=pd.date_range(start='2019-11-16', periods=240, freq='W-SAT')
)

A_fixed = np.array([
    [1, 0, 0],
    ['E', 1, 0],
    ['E', 'E', 1]
], dtype=object)

svar_ok = SVAR(sample_df, svar_type='A', A=A_fixed)
fit_ok = svar_ok.fit(maxlags=4)

In this configuration, the model fits without raising the ValueError.

Why this matters

When building SVARs, the constraints matrix is core to identification. A seemingly minor difference in how entries are specified can cascade into an opaque numerical error during fitting. If you run into the same ValueError, verifying how the A matrix is encoded can save time. It also helps to observe the code’s actual behavior with quick print-based checks of shapes, types, and intermediate values, and to keep the full error message and traceback on hand, since they often contain clues beyond the headline exception.

Takeaways

If your SVAR fit fails with “zero-size array to reduction operation maximum which has no identity” while using np.nan in the A matrix, replace those entries with 'E' and set the matrix dtype to object as shown above. This adjustment resolves the issue in a reproducible example and keeps the estimation process on track. When debugging, inspect what your variables really contain and always preserve the complete error output to accelerate diagnosis. With the constraints matrix specified consistently, statsmodels can proceed with SVAR estimation as expected.