Estimation of ARMA: state space vs. alternatives

I am interested in estimation of ARMA models. I understand that a popular approach is to write the model down in the state space form and then maximize the likelihood of the model using some optimization routine.

Question: Why rewrite the model into its state space representation and maximize the corresponding likelihood — instead of maximizing the “naive” or “direct” likelihood?

(I could imagine that a different parameterization can make the optimization easier — is that the case here?)

Related questions:

I am also aware of some general advantages and disadvantages of the state space representation as mentioned in “What are disadvantages of state-space models and Kalman Filter for time-series modelling?”.

Answer

Attribution
Source : Link , Question Author : Richard Hardy , Answer Author : Community

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