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Removed global from prior_bounds. Added texinfo header.

time-shift
Stéphane Adjemian (Charybdis) 2011-09-14 23:40:23 +02:00
parent fe55f6a506
commit 322adb92a5
4 changed files with 84 additions and 53 deletions
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2
matlab/dynare_estimation_1.m
View File

@ -857,7 +857,7 @@ end
if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
(any(bayestopt_.pshape >0 ) && options_.load_mh_file) %% not ML estimation
bounds = prior_bounds(bayestopt_);
bounds = prior_bounds(bayestopt_,options_);
bounds(:,1)=max(bounds(:,1),lb);
bounds(:,2)=min(bounds(:,2),ub);
bayestopt_.lb = bounds(:,1);

90
matlab/dynare_estimation_init.m
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@ -1,14 +1,14 @@
function [data,rawdata,xparam1,data_info]=dynare_estimation_init(var_list_, dname, gsa_flag)
function [dataset_,xparam1, M_, options_, oo_, estim_params_,bayestopt_, fake] = dynare_estimation_init(var_list_, dname, gsa_flag, M_, options_, oo_, estim_params_, bayestopt_)
% function dynare_estimation_init(var_list_, gsa_flag)
% preforms initialization tasks before estimation or
% global sensitivity analysis
%
%
% INPUTS
% var_list_: selected endogenous variables vector
% dname: alternative directory name
% gsa_flag: flag for GSA operation (optional)
%
%
% OUTPUTS
% data: data after required transformation
% rawdata: data as in the data file
@ -35,12 +35,9 @@ function [data,rawdata,xparam1,data_info]=dynare_estimation_init(var_list_, dnam
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global M_ options_ oo_ estim_params_ bayestopt_
if nargin < 3 || isempty(gsa_flag)
if isempty(gsa_flag)
gsa_flag = 0;
else
%% Decide if a DSGE or DSGE-VAR has to be estimated.
else% Decide if a DSGE or DSGE-VAR has to be estimated.
if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
options_.dsge_var = 1;
end
@ -48,6 +45,7 @@ else
options_.varlist = var_list_;
end
% Get the indices of the observed variables in M_.endo_names.
options_.lgyidx2varobs = zeros(size(M_.endo_names,1),1);
for i = 1:size(M_.endo_names,1)
tmp = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,'exact');
@ -56,29 +54,27 @@ for i = 1:size(M_.endo_names,1)
disp(' ')
error(['Multiple declarations of ' deblank(M_.endo_names(i,:)) ' as an observed variable is not allowed!'])
end
options_.lgyidx2varobs(i,1) = tmp;
options_.lgyidx2varobs(i) = tmp;
end
end
%% Set the order of approximation to one (if needed).
if options_.order > 1
if ~exist('particle','dir')
disp('This version of Dynare cannot estimate non linearized models!')
disp('Set "order" equal to 1.')
disp(' ')
options_.order = 1;
end
% Set the order of approximation to one (if needed).
if options_.order > 1 & isempty(options_.nonlinear_filter)
disp('This version of Dynare cannot estimate non linearized models!')
disp('Set "order" equal to 1.')
disp(' ')
options_.order = 1;
end
%% Set options_.lik_init equal to 3 if diffuse filter is used.
% Set options_.lik_init equal to 3 if diffuse filter is used.
if (options_.diffuse_filter==1) && (options_.lik_init==1)
options_.lik_init = 3;
end
%% If options_.lik_init == 1
%% set by default options_.qz_criterium to 1-1e-6
%% and check options_.qz_criterium < 1-eps if options_.lik_init == 1
%% Else set by default options_.qz_criterium to 1+1e-6
% If options_.lik_init == 1
% set by default options_.qz_criterium to 1-1e-6
% and check options_.qz_criterium < 1-eps if options_.lik_init == 1
% Else set by default options_.qz_criterium to 1+1e-6
if options_.lik_init == 1
if isempty(options_.qz_criterium)
options_.qz_criterium = 1-1e-6;
@ -91,14 +87,14 @@ elseif isempty(options_.qz_criterium)
options_.qz_criterium = 1+1e-6;
end
%% If the data are prefiltered then there must not be constants in the
%% measurement equation of the DSGE model or in the DSGE-VAR model.
% If the data are prefiltered then there must not be constants in the
% measurement equation of the DSGE model or in the DSGE-VAR model.
if options_.prefilter == 1
options_.noconstant = 1;
end
%% Set options related to filtered variables.
if ~isequal(options_.filtered_vars,0) && isempty(options_.filter_step_ahead)
% Set options related to filtered variables.
if ~isequal(options_.filtered_vars,0) && isempty(options_.filter_step_ahead)
options_.filter_step_ahead = 1;
end
if ~isequal(options_.filtered_vars,0) && isequal(options_.filter_step_ahead,0)
@ -108,26 +104,26 @@ if ~isequal(options_.filter_step_ahead,0)
options_.nk = max(options_.filter_step_ahead);
end
%% Set the name of the directory where (intermediary) results will be saved.
if nargin>1
M_.dname = dname;
% Set the name of the directory where (intermediary) results will be saved.
if isempty(dname)
M_.dname = M_.fname;
else
M_.dname = M_.fname;
M_.dname = dname;
end
%% Set the number of observed variables.
% Set the number of observed variables.
n_varobs = size(options_.varobs,1);
%% Set priors over the estimated parameters.
% Set priors over the estimated parameters.
if ~isempty(estim_params_)
[xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
if any(bayestopt_.pshape > 0)
% Plot prior densities.
if options_.plot_priors
plot_priors(bayestopt_,M_,options_)
plot_priors(bayestopt_,M_,estim_params_,options_)
end
% Set prior bounds
bounds = prior_bounds(bayestopt_);
bounds = prior_bounds(bayestopt_,options_);
bounds(:,1)=max(bounds(:,1),lb);
bounds(:,2)=min(bounds(:,2),ub);
else
@ -186,12 +182,12 @@ else% Yes!
end
%% Set the "size" of penalty.
bayestopt_.penalty = 1e8;
bayestopt_.penalty = 1e8;
%% Get informations about the variables of the model.
dr = set_state_space(oo_.dr,M_);
oo_.dr = dr;
nstatic = dr.nstatic; % Number of static variables.
nstatic = dr.nstatic; % Number of static variables.
npred = dr.npred; % Number of predetermined variables.
nspred = dr.nspred; % Number of predetermined variables in the state equation.
@ -230,7 +226,7 @@ if options_.block == 1
% set mf0 to positions of state variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf0] = ismember(M_.state_var',oo_.dr.restrict_var_list);
% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf1] = ismember(k1,oo_.dr.restrict_var_list);
[junk,bayestopt_.mf1] = ismember(k1,oo_.dr.restrict_var_list);
% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
bayestopt_.mf2 = var_obs_index;
bayestopt_.mfys = k1;
@ -250,7 +246,7 @@ else
% set mf0 to positions of state variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf0] = ismember([dr.nstatic+1:dr.nstatic+dr.npred]',k2);
% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
[junk,bayestopt_.mf1] = ismember(var_obs_index,k2);
[junk,bayestopt_.mf1] = ismember(var_obs_index,k2);
% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
bayestopt_.mf2 = var_obs_index;
bayestopt_.mfys = k1;
@ -289,9 +285,9 @@ if ~isempty(options_.unit_root_vars)
il2 = find(l2' > 0);
l2(il2) = (1:length(il2))';
bayestopt_.restrict_var_list_stationary = ...
nonzeros(l2(:,bayestopt_.restrict_var_list_stationary));
nonzeros(l2(:,bayestopt_.restrict_var_list_stationary));
bayestopt_.restrict_var_list_nonstationary = ...
nonzeros(l2(:,bayestopt_.restrict_var_list_nonstationary));
nonzeros(l2(:,bayestopt_.restrict_var_list_nonstationary));
end
options_.lik_init = 3;
end % if ~isempty(options_.unit_root_vars)
@ -305,7 +301,7 @@ if isempty(options_.datafile)
return
else
error('datafile option is missing')
end
end
end
%% If jscale isn't specified for an estimated parameter, use global option options_.jscale, set to 0.2, by default.
@ -321,10 +317,10 @@ rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
% Take the log of the variables if needed
if options_.loglinear % If the model is log-linearized...
if ~options_.logdata % and if the data are not in logs, then...
rawdata = log(rawdata);
rawdata = log(rawdata);
end
end
% Test if the observations are real numbers.
% Test if the observations are real numbers.
if ~isreal(rawdata)
error('There are complex values in the data! Probably a wrong transformation')
end
@ -349,12 +345,12 @@ end
data = transpose(rawdata);
if nargout>3
%% Compute the steady state:
%% Compute the steady state:
if options_.steadystate_flag% if the *_steadystate.m file is provided.
[ys,tchek] = feval([M_.fname '_steadystate'],...
[zeros(M_.exo_nbr,1);...
oo_.exo_det_steady_state]);
if size(ys,1) < M_.endo_nbr
if size(ys,1) < M_.endo_nbr
if length(M_.aux_vars) > 0
ys = add_auxiliary_variables_to_steadystate(ys,M_.aux_vars,...
M_.fname,...
@ -376,10 +372,10 @@ if nargout>3
else
options_.noconstant = 0;
end
[data_index,number_of_observations,no_more_missing_observations] = describe_missing_data(data,gend,n_varobs);
missing_value = ~(number_of_observations == gend*n_varobs);
% initial_estimation_checks(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations);
data_info.gend = gend;

2
matlab/get_prior_info.m
View File

@ -63,7 +63,7 @@ if size(M_.param_names,1)==size(M_.param_names_tex,1)% All the parameters have a
% Column 7: the upper bound of the interval containing 80% of the prior mass.
prior_trunc_backup = options_.prior_trunc ;
options_.prior_trunc = (1-options_.prior_interval)/2 ;
PriorIntervals = prior_bounds(bayestopt_) ;
PriorIntervals = prior_bounds(bayestopt_,options_) ;
options_.prior_trunc = prior_trunc_backup ;
for i=1:size(bayestopt_.name,1)
[tmp,TexName] = get_the_name(i,1,M_,estim_params_,options_);

43
matlab/prior_bounds.m
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@ -1,4 +1,41 @@
function bounds = prior_bounds(bayestopt)
function bounds = prior_bounds(bayestopt,option)
%@info:
%! @deftypefn {Function File} {@var{bounds} =} prior_bounds (@var{bayesopt},@var{option})
%! @anchor{prior_bounds}
%! @sp 1
%! Returns bounds for the prior densities. For each estimated parameter the upper and lower bounds
%! are such that the defined intervals contains a probability mass equal to 1-2*@var{option}.prior_trunc. The
%! default value for @var{option}.prior_trunc is 1e-10 (set in @ref{global_initialization}).
%! @sp 2
%! @strong{Inputs}
%! @sp 1
%! @table @ @var
%! @item bayestopt
%! Matlab's structure describing the prior distribution (initialized by @code{dynare}).
%! @item option
%! Matlab's structure describing the options (initialized by @code{dynare}).
%! @end table
%! @sp 2
%! @strong{Outputs}
%! @sp 1
%! @table @ @var
%! @item bounds
%! p*2 matrix of doubles, where p is the number of estimated parameters. The first and second columns report
%! respectively the lower and upper bounds.
%! @end table
%! @sp 2
%! @strong{This function is called by:}
%! @sp 1
%! @ref{get_prior_info}, @ref{dynare_estimation_1}, @ref{dynare_estimation_init}
%! @sp 2
%! @strong{This function calls:}
%! @sp 1
%! None.
%! @end deftypefn
%@eod:
% function bounds = prior_bounds(bayestopt)
% computes bounds for prior density.
%
@ -28,14 +65,12 @@ function bounds = prior_bounds(bayestopt)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
global options_
pshape = bayestopt.pshape;
p3 = bayestopt.p3;
p4 = bayestopt.p4;
p6 = bayestopt.p6;
p7 = bayestopt.p7;
prior_trunc = options_.prior_trunc;
prior_trunc = options.prior_trunc;
bounds = zeros(length(p6),2);