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Account for negative steady state in case of log-linearization.

time-shift
Stéphane Adjemian (Charybdis) 2017-06-08 11:52:32 +02:00
parent 21fa1ec86a
commit be61de1367
2 changed files with 9 additions and 0 deletions
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3
matlab/cli/prior.m
View File

@ -103,6 +103,9 @@ if ismember('simulate', varargin) % Prior simulations (BK).
disp(['mjdgges crash share = ' num2str(results.dll.problem_share)])
disp(['Steady state problem share = ' num2str(results.ss.problem_share)])
disp(['Complex steady state share = ' num2str(results.ss.complex_share)])
if options_.loglinear
disp(['Nonpositive steady state share = ' num2str(results.ss.nonpositive_share)])
end
disp(['Analytical steady state problem share = ' num2str(results.ass.problem_share)])
skipline(2)
donesomething = true;

6
matlab/prior_sampler.m
View File

@ -49,6 +49,7 @@ count_complex_steadystate = 0;
count_nan_steadystate = 0;
count_nan_params = 0;
count_complex_params = 0;
count_nonpositive_steadystate = 0;
count_unknown_problem = 0;
NumberOfSimulations = options_.prior_mc;
NumberOfParameters = length(bayestopt_.p1);
@ -135,6 +136,8 @@ while iteration < NumberOfSimulations
count_complex_params = count_complex_params + 1 ;
case 24
count_nan_params = count_nan_params + 1 ;
case 26
count_nonpositive_steadystate = count_nonpositive_steadystate + 1;
otherwise
count_unknown_problem = count_unknown_problem + 1 ;
end
@ -162,6 +165,7 @@ results.dll.problem_share = count_dll_problem/loop_indx;
results.ss.problem_share = count_no_steadystate/loop_indx;
results.ss.complex_share = count_complex_steadystate/loop_indx;
results.ass.problem_share = count_steadystate_file_exit/loop_indx;
results.ss.nonpositive_share = count_nonpositive_steadystate/loop_indx;
results.jacobian.problem_share = count_complex_jacobian/loop_indx;
results.garbage_share = ...
results.bk.indeterminacy_share + ...
@ -170,6 +174,8 @@ results.garbage_share = ...
results.dll.problem_share + ...
results.ss.problem_share + ...
results.ass.problem_share + ...
results.ss.complex_share + ...
results.ss.nonpositive_share + ...
results.jacobian.problem_share + ...
count_unknown_problem/loop_indx ;
results.prior.mean = sampled_prior_expectation;