Time-Series Analysis – Understanding Stationarity and Invertibility of a Process

arimastationaritystochastic-processestime series

I have this process: $X_t-0.5X_{t-1}=\epsilon_t-1.3\epsilon_{t-1}+0.4\epsilon_{t-2 }$

I'm wondering if this ARMA(1,2) is stationary and/or invertible. I know we can rewrite the process as follows:
$$(1-0.5L)X_t=(1-1.3L+0.4L^2)\epsilon_t$$where $L$ is the backward operator.

  • It's stationary cause the MA part is by definition, and AR part has $-0.5$ that's $|-0.5|<1$
  • But I have some problem understanding the invertibility part. By definition the AR part is invertible, so we need to check the MA part, so we pick the polynomial: $(1-1.3L+0.4L^2)$ and we find the roots, if they are $|L_{(1,2)}|
    >
    1$
    my process is invertible. The roots are:$1.25$ and $2$
    So I would say that the process is invertible.

But now come my question: I know that in a process (AR(2) or MA(2)) we can easily prove stationairy or invertibilty looking at the coefficients we these restrictions : \begin{cases}
|\theta_2|<1\\
\theta_2+\theta_1<1\\
\theta_2-\theta_1<1
\end{cases}

If if put inside my coefficients, I get:\begin{cases}
|0.4|<1 \quad True\\
0.4+1.3<1 \quad False\\
0.4-1.3 <1\quad True
\end{cases}

So if I apply these rule my process is not invertible. Why do I get this contradiction?

Best Answer

The confusion comes from the fact that these conditions (that you state under the label "can be easily proven") pertain to the $Y_t=\varepsilon_t-\theta_1\varepsilon_{t-1}-\theta_2\varepsilon_{t-2}$ formulation. In your case this means $\theta_1=1.3$, but $\theta_2=-0.4$ (not $0.4$!). Substituting these to the conditions, you'll see that all of them is fulfilled.

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