ARMA coefficient recursions ($\Phi (z)C(z)=\Theta (z),\Theta (z)D(z)=\Phi (z)$)

For an autoregressive moving-average model, set $\Phi (z)=1-{\sum }_{k=1}^p{\varphi }_k{z}^k$ and $\Theta (z)=1+{\sum }_{k=1}^q{\theta }_k{z}^k$. If both have no zeros on the closed unit disc, the causal and inverse filters have absolutely summable coefficients $C(z)=\Theta (z)/\Phi (z)={\sum }_{j\ge 0}{c}_j{z}^j$ and $D(z)=\Phi (z)/\Theta (z)={\sum }_{j\ge 0}{d}_j{z}^j$. Comparing power series coefficients gives $c_j={\theta }_j+{\sum }_{k=1}^p{\varphi }_k{c}_{j-k}$ and $d_j=a_j-{\sum }_{k=1}^q{\theta }_k{d}_{j-k}$, where $a_0={\theta }_0=1$, $a_j=-{\varphi }_j$ for $1\le j\le p$, and all out-of-range coefficients vanish. The backshift operator then gives the corresponding time-series filters.

For $|\varphi |<1$ and $|\theta |<1$, the autoregressive moving-average model $(1-\varphi B)X_t=(1+\theta B){ϵ}_t$ has $c_0=d_0=1$ and the displayed coefficients for $j\ge 1$. These follow by multiplying the two geometric power series. If $\varphi +\theta =0$, all later coefficients vanish and the filtered process is white noise. Thus root restrictions on an uncancelled nonminimal representation are sufficient but need not be necessary for the process after common factor cancellation in an ARMA model.