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Let us consider the following model:

$$ y_{t} = c_{t} + \alpha y_{t-1} + v_{t} \\ c_{t+1} = c_{0} + \beta c_{t} + w_{t} $$ where $v_{t} \in \mathcal{N}(0, \sigma^{2}_{v})$ and $w_{t} \in \mathcal{N}(0, \sigma^{2}_{w})$ are independent.

I am a bit stuck: does this system have a simpler ARMA form or other solvable state-state representation?

If $\alpha = 0$, then the system is AR(1) plus noise, which is equivalent to ARMA(1,1).

The idea is to model a process as a mean reversion taking into account that the mean can change over the time.

ABK
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  • The autoregressive intercept is simply a second AR process, and $y_t$ can be considered the sum of two AR processes. As discussed in Hamilton, Time Series Analysis p.107/108, this system can be represented as an ARMA(2,1). As for the changing mean over time, if both processes are stationary then the unconditional mean will not be time dependent. – Henry Sep 12 '20 at 20:20

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