diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 0578791d7452941b55b69908fc4b7fa92926c5dd..9be0c34402e2c16fdf6e3f6dc809b49560f695ee 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -281,25 +281,22 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
epsilon = options_.gradient_epsilon;
% Change some options.
if isfield(options_,'optim_opt')
- options_list = strsplit(options_.optim_opt,',');
- number_of_options = length(options_list)/2;
- o = 1;
- while o<=number_of_options
- switch strtrim(options_list{2*(o-1)+1})
- case '''MaxIter'''
- nit = str2num(options_list{2*(o-1)+2});
- case '''InitialInverseHessian'''
- H0 = eval(eval(options_list{2*(o-1)+2}));
+ options_list = read_key_value_string(options_.optim_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+ case 'MaxIter'
+ nit = options_list{i,2};
+ case 'InitialInverseHessian'
+ H0 = eval(options_list{i,2});
case '''TolFun'''
- crit = str2double(options_list{2*(o-1)+2});
- case '''NumgradAlgorithm'''
- numgrad = str2num(options_list{2*(o-1)+2});
- case '''NumgradEpsilon'''
- epsilon = str2double(options_list{2*(o-1)+2});
+ crit = options_list{i,2};
+ case 'NumgradAlgorithm'
+ numgrad = options_list{i,2};
+ case 'NumgradEpsilon'
+ epsilon = options_list{i,2};
otherwise
- warning(['csminwel: Unknown option (' options_list{2*(o-1)+1} ')!'])
+ warning(['csminwel: Unknown option (' options_list{i,1} ')!'])
end
- o = o + 1;
end
end
% Set flag for analytical gradient.
@@ -353,21 +350,19 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
gmhmaxlikOptions.varinit = 'prior';
end
if isfield(options_,'optim_opt')
- options_list = strsplit(options_.optim_opt,',');
- number_of_options = length(options_list)/2;
- o = 1;
- while o<=number_of_options
- switch strtrim(options_list{2*(o-1)+1})
- case '''NumberOfMh'''
- gmhmaxlikOptions.iterations = str2num(options_list{2*(o-1)+2});
- case '''ncov-mh'''
- gmhmaxlikOptions.number = str2num(options_list{2*(o-1)+2});
- case '''nscale'''
- gmhmaxlikOptions.nscale = str2double(options_list{2*(o-1)+2});
- case '''nclimb'''
- gmhmaxlikOptions.nclimb = str2num(options_list{2*(o-1)+2});
- case '''InitialCovarianceMatrix'''
- switch eval(options_list{2*(o-1)+2})
+ options_list = read_key_value_string(options_.optim_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+ case 'NumberOfMh'
+ gmhmaxlikOptions.iterations = options_list{i,2};
+ case 'ncov-mh'
+ gmhmaxlikOptions.number = options_list{i,2};
+ case 'nscale'
+ gmhmaxlikOptions.nscale = options_list{i,2};
+ case 'nclimb'
+ gmhmaxlikOptions.nclimb = options_list{i,2};
+ case 'InitialCovarianceMatrix'
+ switch options_list{i,2}
case 'previous'
if isempty(hh)
error('gmhmaxlik: No previous estimate of the Hessian matrix available!')
@@ -375,21 +370,20 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
gmhmaxlikOptions.varinit = 'previous'
end
case {'prior', 'identity'}
- gmhmaxlikOptions.varinit = eval(options_list{2*(o-1)+2});
+ gmhmaxlikOptions.varinit = options_list{i,2};
otherwise
error('gmhmaxlik: Unknown value for option ''InitialCovarianceMatrix''!')
end
- case '''AcceptanceRateTarget'''
- gmhmaxlikOptions.target = str2num(options_list{2*(o-1)+2});
+ case 'AcceptanceRateTarget'
+ gmhmaxlikOptions.target = options_list{i,2};
if gmhmaxlikOptions.target>1 || gmhmaxlikOptions.target<eps
error('gmhmaxlik: The value of option AcceptanceRateTarget should be a double between 0 and 1!')
end
otherwise
- Warning(['gmhmaxlik: Unknown option (' options_list{2*(o-1)+1} ')!'])
+ Warning(['gmhmaxlik: Unknown option (' options_list{i,1} ')!'])
end
- o = o + 1;
end
- end
+ end
% Evaluate the objective function.
fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
OldMode = fval;
@@ -485,27 +479,24 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
% Dynare implementation of the simplex algorithm.
simplexOptions = options_.simplex;
if isfield(options_,'optim_opt')
- options_list = strsplit(options_.optim_opt,',');
- number_of_options = length(options_list)/2;
- o = 1;
- while o<=number_of_options
- switch strtrim(options_list{2*(o-1)+1})
- case '''MaxIter'''
- simplexOptions.maxiter = str2num(options_list{2*(o-1)+2});
- case '''TolFun'''
- simplexOptions.tolerance.f = str2double(options_list{2*(o-1)+2});
- case '''TolX'''
- simplexOptions.tolerance.x = str2double(options_list{2*(o-1)+2});
- case '''MaxFunEvals'''
- simplexOptions.maxfcall = str2num(options_list{2*(o-1)+2});
- case '''MaxFunEvalFactor'''
- simplexOptions.maxfcallfactor = str2num(options_list{2*(o-1)+2});
- case '''InitialSimplexSize'''
- simplexOptions.delta_factor = str2double(options_list{2*(o-1)+2});
+ options_list = read_key_value_string(options_.optim_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+ case 'MaxIter'
+ simplexOptions.maxiter = options_list{i,2};
+ case 'TolFun'
+ simplexOptions.tolerance.f = options_list{i,2};
+ case 'TolX'
+ simplexOptions.tolerance.x = options_list{i,2};
+ case 'MaxFunEvals'
+ simplexOptions.maxfcall = options_list{i,2};
+ case 'MaxFunEvalFactor'
+ simplexOptions.maxfcallfactor = options_list{i,2};
+ case 'InitialSimplexSize'
+ simplexOptions.delta_factor = options_list{i,2};
otherwise
- warning(['simplex: Unknown option (' options_list{2*(o-1)+1} ')!'])
+ warning(['simplex: Unknown option (' options_list{i,1} ')!'])
end
- o = o + 1;
end
end
[xparam1,fval,exitflag] = simplex_optimization_routine(objective_function,xparam1,simplexOptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
@@ -515,23 +506,20 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
cmaesOptions = options_.cmaes;
% Modify defaults
if isfield(options_,'optim_opt')
- options_list = strsplit(options_.optim_opt,',');
- number_of_options = length(options_list)/2;
- o = 1;
- while o<=number_of_options
- switch strtrim(options_list{2*(o-1)+1})
- case '''MaxIter'''
- cmaesOptions.MaxIter = str2num(options_list{2*(o-1)+2});
- case '''TolFun'''
- cmaesOptions.TolFun = str2double(options_list{2*(o-1)+2});
- case '''TolX'''
- cmaesOptions.TolX = str2double(options_list{2*(o-1)+2});
- case '''MaxFunEvals'''
- cmaesOptions.MaxFunEvals = str2num(options_list{2*(o-1)+2});
+ options_list = read_key_value_string(options_.optim_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+ case 'MaxIter'
+ cmaesOptions.MaxIter = options_list{i,2};
+ case 'TolFun'
+ cmaesOptions.TolFun = options_list{i,2};
+ case 'TolX'
+ cmaesOptions.TolX = options_list{i,2};
+ case 'MaxFunEvals'
+ cmaesOptions.MaxFunEvals = options_list{i,2};
otherwise
- warning(['cmaes: Unknown option (' options_list{2*(o-1)+1} ')!'])
+ warning(['cmaes: Unknown option (' options_list{i,1} ')!'])
end
- o = o + 1;
end
end
warning('off','CMAES:NonfinitenessRange');
@@ -542,36 +530,33 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
case 10
simpsaOptions = options_.simpsa;
if isfield(options_,'optim_opt')
- options_list = strsplit(options_.optim_opt,',');
- number_of_options = length(options_list)/2;
- o = 1;
- while o<=number_of_options
- switch strtrim(options_list{2*(o-1)+1})
- case '''MaxIter'''
- simpsaOptions.MAX_ITER_TOTAL = str2num(options_list{2*(o-1)+2});
- case '''TolFun'''
- simpsaOptions.TOLFUN = str2double(options_list{2*(o-1)+2});
- case '''TolX'''
- tolx = str2double(options_list{2*(o-1)+2});
+ options_list = read_key_value_string(options_.optim_opt);
+ for i=1:rows(options_list)
+ switch options_list{i,1}
+ case 'MaxIter'
+ simpsaOptions.MAX_ITER_TOTAL = options_list{i,2};
+ case 'TolFun'
+ simpsaOptions.TOLFUN = options_list{i,2};
+ case 'TolX'
+ tolx = options_list{i,2};
if tolx<0
- simpsaOptions = rmfield(simpsaOptions,'TOLX'); % Let cmaes choose the default.
+ simpsaOptions = rmfield(simpsaOptions,'TOLX'); % Let simpsa choose the default.
else
simpsaOptions.TOLX = tolx;
end
- case '''EndTemparature'''
- simpsaOptions.TEMP_END = str2double(options_list{2*(o-1)+2});
- case '''MaxFunEvals'''
- simpsaOptions.MAX_FUN_EVALS = str2num(options_list{2*(o-1)+2});
+ case 'EndTemparature'
+ simpsaOptions.TEMP_END = options_list{i,2};
+ case 'MaxFunEvals'
+ simpsaOptions.MAX_FUN_EVALS = options_list{i,2};
otherwise
- warning(['simpsa: Unknown option (' options_list{2*(o-1)+1} ')!'])
+ warning(['simpsa: Unknown option (' options_list{i,1} ')!'])
end
- o = o + 1;
end
end
simpsaOptionsList = options2cell(simpsaOptions);
simpsaOptions = simpsaset(simpsaOptionsList{:});
[xparam1, fval, exitflag] = simpsa(func2str(objective_function),xparam1,lb,ub,simpsaOptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
- case 11
+ case 11
options_.cova_compute = 0 ;
[xparam1,stdh,lb_95,ub_95,med_param] = online_auxiliary_filter(xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_) ;
case 101
@@ -632,7 +617,6 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
[junk1, junk2, hh] = feval(objective_function,xparam1, ...
dataset_,options_,M_,estim_params_,bayestopt_,oo_);
options_.analytic_derivation = ana_deriv;
-
else
hh = reshape(hessian(objective_function,xparam1, ...
options_.gstep,dataset_,options_,M_,estim_params_,bayestopt_,oo_),nx,nx);
@@ -657,13 +641,13 @@ switch options_.MCMC_jumping_covariance
case 'prior_variance' %Use prior variance
if any(isinf(bayestopt_.p2))
error('Infinite prior variances detected. You cannot use the prior variances as the proposal density, if some variances are Inf.')
- else
+ else
hh = diag(1./(bayestopt_.p2.^2));
end
case 'identity_matrix' %Use identity
- hh = eye(nx);
+ hh = eye(nx);
otherwise %user specified matrix in file
- try
+ try
load(options_.MCMC_jumping_covariance,'jumping_covariance')
hh=jumping_covariance;
catch