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What are the advantages of expressing an ARMA model as a state-space-model and do forecasting using a Kalman filter?

This methodology is for example used in the SARIMAX implementation of python-statsmodels:

https://github.com/statsmodels/statsmodels/tree/master/statsmodels/tsa/statespace

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To me one of the main advantages is handling of missing data and uneven time steps. Kalman filter easily handles the missing observations, and actually can be used to impute them.

OLS and MLE don't handle missing data as easily, and not every package will have this feature support unlike Kalman filter.

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