MODEL:
 ! Markov chain model;
SETS:
 ! There are four states in our model and over time
   the model will arrive at a steady state 
   equilibrium.
   SPROB( J) = steady state probability;
 STATE/ A B C D/: SPROB;

 ! For each state, there's a probability of moving
   to each other state. TPROB( I, J) = transition
   probability;
 SXS( STATE, STATE): TPROB;
ENDSETS

DATA:
 ! The transition probabilities. These are proba-
   bilities of moving from one state to the next in
   each time period. Our model has four states, for
   each time period there's a probability of moving
   to each of the four states. The sum of proba-
   bilities across each of the rows is 1, since the
   system either moves to a new state or remains in
   the current one.;
   TPROB = .75  .1  .05  .1
           .4   .2  .1   .3
           .1   .2  .4   .3
           .2   .2  .3   .3;
ENDDATA

! The model;
!  Steady state equations;
!   Only need N equations, so drop last;
  @FOR( STATE( J)| J #LT# @SIZE( STATE):
   SPROB( J) = @SUM( SXS( I, J): SPROB( I) *
    TPROB( I, J))
  );

! The steady state probabilities must sum to 1;
  @SUM( STATE: SPROB) = 1;

! Check the input data, warn the user if the sum of
  probabilities in a row does not equal 1.;
  @FOR( STATE( I):
   @WARN( 'Probabilities in a row must sum to 1.',
    @ABS( 1 - @SUM( SXS( I, K): TPROB( I, K)))
     #GT# .000001);
  );

END