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Chapter 4: The Model file
92
max_iterations_starting_value = INTEGER
This is the maximum number of iterations allowed in the hill-climbing optimization routine and should be rather small since it will gradually be increased during
estimation. Default: 50
max_iterations_increment_value = DOUBLE
Determines how quickly the maximum number of iterations is increased. Default: 2
max_block_iterations = INTEGER
The parameters are divided into blocks and optimization proceeds over each block.
After a set of blockwise optimizations are performed, the convergence criterion is
checked and the blockwise optimizations are repeated if the criterion is violated.
This controls the maximum number of times the blockwise optimization can be
performed. Note that after the blockwise optimizations have converged, a single
optimization over all the parameters is performed before updating the convergence
value and maximum number of iterations. Default: 100
max_repeated_optimization_runs = INTEGER
The entire process described by [max block iterations], page 92 is repeated until
improvement has stopped. This is the maximum number of times the process is
allowed to repeat. Set this to 0 to not allow repetitions. Default: 10
function_convergence_criterion = DOUBLE
The convergence criterion for the objective function when max_repeated_
optimizations_runs is positive. Default: 0.1
parameter_convergence_criterion = DOUBLE
The convergence criterion for parameter
optimizations_runs is positive. Default: 0.1
values
when
max_repeated_
number_of_large_perturbations = INTEGER
The entire process described by [max block iterations], page 92 is repeated with
random starting values drawn from the posterior. This specifies the number of
random starting values used. Set this to 0 to not use random starting values. A
larger number should be specified to ensure that the entire parameter space has
been covered. Default: 5
number_of_small_perturbations = INTEGER
The number of small perturbations to make after the large perturbations have
stopped improving. Setting this number much above 10 is probably overkill. Default: 5
number_of_posterior_draws_after_perturbation = INTEGER
The number of consecutive posterior draws to make when producing a small perturbation. Because the posterior draws are serially correlated, a small number will
result in a small perturbation. Default: 1
max_number_of_stages = INTEGER
The small and large perturbation are repeated until improvement has stopped. This
specifics the maximum number of stages allowed. Default: 20
random_function_convergence_criterion = DOUBLE
The convergence criterion for the objective function when number_of_large_
perturbations is positive. Default: 0.1
random_parameter_convergence_criterion = DOUBLE
The convergence criterion for parameter values when number_of_large_
perturbations is positive. Default: 0.1