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block:
ep:
accuracy:
stochastic:
order
debug: 0
memory: 0
init: 0
check_stability: 0
lp: 5
fp: 2
innovation_distribution: 'gaussian'
'calibrated'
maxit: 500
periods: 200
set_dynare_seed_to_default: 1
solve_algo:
stack_solve_algo: 4
step: 50
stochastic:
IntegrationAlgorithm: 'Tensor-Gaussian-Quadrature'
'Stroud-Cubature-3'
'Stroud-Cubature-5'
'Unscented'
method: ''
algo: 0
order: 1
hybrid_order: 0
homotopic_steps: 1
nodes: 3
quadrature:
ortpol: 'hermite'
nodes: 5
pruned:
ortpol: 'hermite'
nodes: 5
pruned: [1x1 struct]
verbosity: 0
initial_period: NaN
lmmcp:
lb:
ub:
status: 0 (?? status is not an option ??)
mcppath:
lb:
ub:
minimal_solving_period:
order:
ramsey_policy:
simul:
maxit
solve_algo:
stack_solve_algo:
ut: (unscented free parameter)
pfm.stochastic_order = options_.ep.stochastic.order;
pfm.periods = options_.ep.periods;
pfm.verbose = options_.ep.verbosity;
* in extended_path_core, one passes options.ep and individual options
* there are no options to control extended_path_homotopy
* extended_path_initialization sets ep and options
* setup_integration_nodes: number of nodes is not handled in a symmetric way for all algorithms
* why extended_path_initialization et setup_stochastic_extended_path ?
* do we need solve_stochastic_perfect_foresight_model.m and solve_stochastic_perfect_foresight_model_1.m ?
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