I am looking for a way to model that the expected value of variable A depends on today’s value of variable B forever.
To make more clear what I have in mind:

oneworker firm decides to enter in period t with idiosyncratic productivity z_t

idiosyncratic productivity remains at z_t for the whole life of the firm i.e. until separation and death (probability of separation each period is \delta)
Therefore, the expected discounted value of a firm entering with z_t and wage w is:
J = z_t  w + \beta *(1\delta) *Jfuture;
Jfuture = z_t  w(+1) + \beta *(1\delta) *Jfuture(+1);
In my model, z_t can change over time but I want Jfuture, Jfuture(+1), Jfuture(+2),… to abstract from any expected changes in z_t(+1), z_t(+2)….
z_t should simply be treated as a constant in the computation of Jfuture.
My firm concept is more complex than the simplified outline above which prevents me from reformulating the equation above (using e.g. the formula for a sum to infinity of a geometric series).
I couldn’t find an answer to this question, neither in the manual nor in previous forum posts.
I’d be greatful for any idea or workaround that helps me to achieve what I outlined above.