diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index e50e8e51ad108fbb2110abfca5285c3d048bd22c..49dfecfc881ab7ca1cb23c6793baaaa682560097 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -331,41 +331,85 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
parameter_names = bayestopt_.name;
save([M_.fname '_mode.mat'],'xparam1','hh','gg','fval','invhess','parameter_names');
case 6
+ % Set default options
+ gmhmaxlikOptions = options_.gmhmaxlik;
+ if ~isempty(hh);
+ gmhmaxlikOptions.varinit = 'previous';
+ else
+ 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})
+ case 'previous'
+ if isempty(hh)
+ error('gmhmaxlik: No previous estimate of the Hessian matrix available!')
+ else
+ gmhmaxlikOptions.varinit = 'previous'
+ end
+ case {'prior', 'identity'}
+ gmhmaxlikOptions.varinit = eval(options_list{2*(o-1)+2});
+ otherwise
+ error('gmhmaxlik: Unknown value for option ''InitialCovarianceMatrix''!')
+ end
+ case '''AcceptanceRateTarget'''
+ gmhmaxlikOptions.target = str2num(options_list{2*(o-1)+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} ')!'])
+ end
+ o = o + 1;
+ end
+ end
+ % Evaluate the objective function.
fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
OldMode = fval;
if ~exist('MeanPar','var')
MeanPar = xparam1;
end
- if ~isempty(hh)
+ switch gmhmaxlikOptions.varinit
+ case 'previous'
CovJump = inv(hh);
- else% The covariance matrix is initialized with the prior
+ case 'prior'
+ % The covariance matrix is initialized with the prior
% covariance (a diagonal matrix) %%Except for infinite variances ;-)
- varinit = 'prior';
- if strcmpi(varinit,'prior')
- stdev = bayestopt_.p2;
- indx = find(isinf(stdev));
- stdev(indx) = ones(length(indx),1)*sqrt(10);
- vars = stdev.^2;
- CovJump = diag(vars);
- elseif strcmpi(varinit,'eye')
- vars = ones(length(bayestopt_.p2),1)*0.1;
- CovJump = diag(vars);
- else
- disp('gmhmaxlik :: Error!')
- return
- end
+ stdev = bayestopt_.p2;
+ indx = find(isinf(stdev));
+ stdev(indx) = ones(length(indx),1)*sqrt(10);
+ vars = stdev.^2;
+ CovJump = diag(vars);
+ case 'identity'
+ vars = ones(length(bayestopt_.p2),1)*0.1;
+ CovJump = diag(vars);
+ otherwise
+ error('gmhmaxlik: This is a bug! Please contact the developers.')
end
OldPostVar = CovJump;
Scale = options_.mh_jscale;
- for i=1:options_.gmhmaxlik.iterations
+ for i=1:gmhmaxlikOptions.iterations
if i == 1
- if options_.gmhmaxlik.iterations>1
+ if gmhmaxlikOptions.iterations>1
flag = '';
else
flag = 'LastCall';
end
[xparam1,PostVar,Scale,PostMean] = ...
- gmhmaxlik(objective_function,xparam1,[lb ub],options_.gmhmaxlik,Scale,flag,MeanPar,CovJump,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ gmhmaxlik(objective_function,xparam1,[lb ub],gmhmaxlikOptions,Scale,flag,MeanPar,CovJump,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
options_.mh_jscale = Scale;
mouvement = max(max(abs(PostVar-OldPostVar)));
@@ -378,14 +422,14 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
OldMode = fval;
else
OldPostVar = PostVar;
- if i<options_.gmhmaxlik.iterations
+ if i<gmhmaxlikOptions.iterations
flag = '';
else
flag = 'LastCall';
end
[xparam1,PostVar,Scale,PostMean] = ...
gmhmaxlik(objective_function,xparam1,[lb ub],...
- options_.gmhmaxlik,Scale,flag,PostMean,PostVar,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+ gmhmaxlikOptions,Scale,flag,PostMean,PostVar,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
options_.mh_jscale = Scale;
mouvement = max(max(abs(PostVar-OldPostVar)));
@@ -399,7 +443,8 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
OldMode = fval;
end
hh = inv(PostVar);
- save([M_.fname '_mode.mat'],'xparam1','hh');
+ parameter_names = bayestopt_.name;
+ save([M_.fname '_mode.mat'],'xparam1','hh','parameter_names');
save([M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
bayestopt_.jscale = ones(length(xparam1),1)*Scale;
end
diff --git a/matlab/global_initialization.m b/matlab/global_initialization.m
index c067ef33fc137bbb4652148827fc5c25fac5be10..6eaece758359c5f36dff70ecf878fe1f0bc7f823 100644
--- a/matlab/global_initialization.m
+++ b/matlab/global_initialization.m
@@ -105,6 +105,7 @@ gmhmaxlik.iterations = 3;
gmhmaxlik.number = 20000;
gmhmaxlik.nclimb = 200000;
gmhmaxlik.nscale = 200000;
+gmhmaxlik.target = 1/3; % Target for the acceptance rate.
options_.gmhmaxlik = gmhmaxlik;
% Request user input.
diff --git a/matlab/gmhmaxlik.m b/matlab/gmhmaxlik.m
index b57f0593bf546fe20fa887a232c9e019c77cd1ff..79c76e0872e7c534edbed1873e59242a289045ac 100644
--- a/matlab/gmhmaxlik.m
+++ b/matlab/gmhmaxlik.m
@@ -84,7 +84,7 @@ npar = length(xparam1);
NumberOfIterations = options.number;
MaxNumberOfTuningSimulations = options.nscale;
MaxNumberOfClimbingSimulations = options.nclimb;
-AcceptanceTarget = 1/3;
+AcceptanceTarget = options.target;
CovJump = VarCov;
ModePar = xparam1;