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Macroeconomietrics: When would you use an ARMA model?

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When would you use an ARMA model?
The question for you is: When would you use an ARMA model? This class consists of students of a wide variety of backgrounds, both educational and professional. Per your own experience, be it professional or academic, what time series that you've encountered might you feel comfortable modeling as an ARMA process? A perfectly valid alternative answer is: What time series would you *not* feel comfortable as an ARMA process? In either case, why? Let me start off by giving some ideas: We've dealt with vacancies and unemployment thus far. Do you think that modeling this processes as ARMA models is fair? If so, why? If not, why not? Again, these two variables are just suggestions. Feel free to comment on any time series that you'd like, and let's move the discussion forward that way.

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ARMA Model
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ARMA Model
The usage of the ARMA model depends on the suitability of the time series. Overfitting can be used in the selection of an appropriate time series for the ARMA model. To select the most parsimonious specification or the one with the fewest parameters, we use the box-je kings method. Since the models' dilemma seems too easy to illustrate the sequence's complexities, overfitting can overcome it. When evaluating the ARMA model's time series, the parameter redundancy is often considered (Shen et al., 2018).
To decide which order of the ARMA model is suitable for a sequence, we must first calculate the AIC (or BIC) over a subset of values, and then use the Box-Jenkins method to see whether a good fit has been achieved that favors the system to be a series of unobserved shocks (the MA or moving average part) as well as its behavior (Asparouhov&Muthen, 2019). Autoregressive–moving–average (ARMA) models offer a parsimonious definition of a (weakly) stationary stochast...
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