Python statsmodel Errors and Workarounds

Working with statsmodels feels great when everything runs smoothly.

But we’ve all hit those frustrating moments when the library throws cryptic warnings, produces NaN values, or refuses to converge.

After building dozens of statistical models with statsmodels, I’ve learned that most problems fall into predictable categories with straightforward solutions.

What causes multicollinearity errors and how do you resolve them?

Statsmodels uses generalized inverse for linear models, which means cases of almost perfect multicollinearity or ill-conditioned design matrices might produce numerically unstable results. You’ll notice this when coefficients have wildly large standard errors or when signs flip unexpectedly between models.

The Variance Inflation Factor (VIF) determines the strength of correlation between independent variables by regressing each variable against every other variable. Here’s how to diagnose the problem:

from statsmodels.stats.outliers_influence import variance_inflation_factor
import pandas as pd
import numpy as np

# Check VIF for all features
vif_data = pd.DataFrame()
vif_data["feature"] = X.columns
vif_data["VIF"] = [variance_inflation_factor(X.values, i) 
                   for i in range(X.shape[1])]
print(vif_data)

VIFs exceeding 4 warrant further investigation, while VIFs exceeding 10 are signs of serious multicollinearity requiring correction. When I see VIF values above 10, I know parameter estimates won’t be reliable.

The fix depends on your situation. Remove one of the correlated variables when you have two highly correlated predictors. I check pairwise correlations to decide which one to drop. Removing one or more explanatory variables from variables with exact multicollinearity does not cause loss of information from a multiple linear regression model.

For more complex cases, consider dimensionality reduction. Principal Component Analysis works well when you have many correlated features and want to preserve maximum information while reducing multicollinearity.

How do you handle convergence warnings in ARIMA models?

Under default settings, statsmodels prints a warning if the optimization algorithm stops without reaching convergence. The dreaded “Maximum Likelihood optimization failed to converge” warning appears frequently with time series models.

When ARIMA models show convergence warnings, the two estimators often fit similarly with only a small difference in log-likelihood, which is a numerical issue that should not materially affect predictions.

Here’s my troubleshooting sequence:

Increase maximum iterations when the optimizer needs more time:

import statsmodels.api as sm

model = sm.tsa.SARIMAX(endog, order=(p, d, q))
results = model.fit(maxiter=200)

Switch optimization algorithms when the default BFGS struggles. Nelder-Mead seemed to require a lot of iterations but finally converged:

results = model.fit(method='nm', maxiter=500)

Check if parameters make sense despite the warning. Even with convergence warnings, estimated parameters may still be in the ballpark rather than nonsense or NaN. I verify this by looking at coefficient values and comparing predictions to actuals.

Simplify the model when convergence repeatedly fails. Complex seasonal patterns with high-order AR and MA terms often cause problems. Start with simpler specifications and add complexity gradually.

You can suppress warnings for production code, but only after confirming results are valid:

results = model.fit(method_kwargs={"warn_convergence": False})

What should you do when logistic regression detects perfect separation?

For binary Logit and Probit models, statsmodels raises an exception if perfect prediction is detected. Perfect separation happens when a predictor completely separates the two outcome classes.

When one category has Y equals zero for all observations with predictor values less than or equal to three and Y equals one for all observations with predictor values greater than three, you have found a perfect predictor without needing to estimate a model.

You’ll see extremely large coefficient estimates and standard errors. The model coefficient might reach values like 20 or 30 when typical coefficients are under 3.

Check your data structure first. Run crosstabs between predictors and outcomes:

pd.crosstab(df['predictor'], df['outcome'])

Look for cells with zero observations. When all or nearly all values in one predictor category are associated with only one binary outcome value, a solution cannot be found for the predictor coefficient.

Remove the separating variable when appropriate. Sometimes a predictor perfectly predicts because it’s essentially a transformation of the outcome variable.

Collect more data when separation results from small sample size. Perfect separation often disappears with additional observations that fill empty cells.

Use penalized regression as a statistical solution. Firth logistic regression or Ridge regression can handle separation by shrinking coefficients:

from sklearn.linear_model import LogisticRegression

# Ridge penalty helps with separation
model = LogisticRegression(penalty='l2', C=1.0, solver='lbfgs')

Why do you get NaN results with robust linear models?

For RLM, if residuals equal zero, it does not cause an exception, but having this perfect fit can produce NaNs in some results due to scale equals zero and zero divided by zero. This happens when your model fits training data perfectly.

Perfect fits occur with small datasets or when you have as many parameters as observations. The robust scale estimator breaks down because there’s no variation to estimate.

Check your data size relative to the number of parameters. With 10 observations and 9 predictors plus an intercept, you’ll likely see this issue.

Add regularization to prevent perfect fits:

import statsmodels.api as sm

# Add small ridge penalty
model = sm.RLM(y, X)
results = model.fit(scale_est=sm.robust.scale.HuberScale())

Remove redundant features when you have more predictors than necessary. Use domain knowledge to identify which variables truly matter.

How do you avoid issues when refitting models?

Result instances need to access attributes from the corresponding model instance, and fitting a model multiple times with different arguments can change model attributes. I learned this the hard way when comparing models.

Create separate model instances for different specifications:

# Wrong approach
model = sm.RLM(y, X)
results1 = model.fit(scale_est='mad')
results2 = model.fit(scale_est=sm.robust.scale.HuberScale())
# results1 may now point to wrong model attributes

# Correct approach
model1 = sm.RLM(y, X)
results1 = model1.fit(scale_est='mad')

model2 = sm.RLM(y, X)  # Separate instance
results2 = model2.fit(scale_est=sm.robust.scale.HuberScale())

This pattern ensures each result object maintains correct references to its model specification. The memory overhead is minimal compared to debugging mysterious result inconsistencies.

What causes numerical issues with large values in OLS?

When using large values for linear regression, statsmodels fails to correctly model the data, appearing to want the line to pass through the intercept when it shouldn’t despite add_constant being used. This happens with values in the tens of trillions.

The problem stems from numerical precision limits. These numbers aren’t large enough to cause overflow on 32-bit precision, but a simple workaround is to scale the data before fitting.

Scale your features before modeling:

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

model = sm.OLS(y, sm.add_constant(X_scaled))
results = model.fit()

Alternatively, use the QR decomposition method which handles ill-conditioned matrices better:

results = model.fit(method='qr')

I prefer scaling because it makes coefficients more interpretable and helps with convergence across different model types.

How do you handle domain restriction violations?

In some cases domain restrictions for parameters might be inconsistent with the data, causing estimation to stop at parameter values close to the boundary or fail with runtime errors producing NaNs during optimization.

Estimating a negative binomial model when data is not overdispersed relative to Poisson is inconsistent with the overdispersion assumption of the Negative Binomial distribution. The model expects variance greater than the mean, but your data shows equality or underdispersion.

Check your distributional assumptions before choosing models. For count data, compare mean and variance:

print(f"Mean: {df['counts'].mean()}")
print(f"Variance: {df['counts'].var()}")

When variance approximately equals mean, use Poisson instead of Negative Binomial. When variance is less than mean, neither standard count model is appropriate.

Test model assumptions after fitting. Statsmodels provides diagnostic tests, but you need to run them manually. I always check residual plots and overdispersion tests for count models.

Conclusion

Most statsmodels issues trace back to data problems rather than code bugs. Multicollinearity, perfect separation, and numerical instability all reflect characteristics of your dataset that violate model assumptions.

Check your data structure, scale features appropriately, and verify distributional assumptions before blaming the library. When warnings appear, investigate rather than suppress them. Understanding what statsmodels is trying to tell you turns frustrating errors into opportunities to build better models.