stepan
/
dynare
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Make last input argument optional.

remove-particles-submodule
Stéphane Adjemian (Ryûk) 2023-04-28 22:58:41 +02:00
parent 278b669a33
commit 5375070fa3
Signed by: stepan
GPG Key ID: 295C1FE89E17EB3C
1 changed files with 32 additions and 65 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

97
matlab/prior_bounds.m
View File

@ -1,51 +1,14 @@
function bounds = prior_bounds(bayestopt, prior_trunc)
%@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 lower and upper 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
%! A structure with two fields lb and up (p*1 vectors of doubles, where p is the number of estimated parameters) for 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, priortrunc)
% function bounds = prior_bounds(bayestopt)
% computes bounds for prior density.
%
% INPUTS
% bayestopt [structure] characterizing priors (shape, mean, p1..p4)
% - bayestopt [struct] characterizing priors (shape, mean, p1..p4)
% - priortrunc [double] scalar, probability mass in the tails to be removed
%
% OUTPUTS
% bounds [double] structure specifying prior bounds (lb and ub fields)
%
% SPECIAL REQUIREMENTS
% none
% - bounds [struct] prior bounds (lb, lower bounds, and ub, upper bounds, fields are n×1 vectors)
% Copyright © 2003-2023 Dynare Team
%
@ -64,74 +27,78 @@ function bounds = prior_bounds(bayestopt, prior_trunc)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
if nargin<2, priortrunc = 0.0; end
assert(priortrunc>=0 && priortrunc<=1, 'Second input argument must be non negative and not larger than one.')
pshape = bayestopt.pshape;
p3 = bayestopt.p3;
p4 = bayestopt.p4;
p6 = bayestopt.p6;
p7 = bayestopt.p7;
bounds.lb = zeros(length(p6),1);
bounds.ub = zeros(length(p6),1);
bounds.lb = zeros(size(p6));
bounds.ub = zeros(size(p6));
for i=1:length(p6)
switch pshape(i)
case 1
if prior_trunc == 0
if priortrunc==0
bounds.lb(i) = p3(i);
bounds.ub(i) = p4(i);
else
bounds.lb(i) = betainv(prior_trunc,p6(i),p7(i))*(p4(i)-p3(i))+p3(i);
bounds.ub(i) = betainv(1-prior_trunc,p6(i),p7(i))*(p4(i)-p3(i))+p3(i);
bounds.lb(i) = betainv(priortrunc, p6(i), p7(i))*(p4(i)-p3(i))+p3(i);
bounds.ub(i) = betainv(1.0-priortrunc, p6(i), p7(i))*(p4(i)-p3(i))+p3(i);
end
case 2
if prior_trunc == 0
if priortrunc==0
bounds.lb(i) = p3(i);
bounds.ub(i) = Inf;
else
bounds.lb(i) = gaminv(prior_trunc,p6(i),p7(i))+p3(i);
bounds.ub(i) = gaminv(1-prior_trunc,p6(i),p7(i))+p3(i);
bounds.lb(i) = gaminv(priortrunc, p6(i), p7(i))+p3(i);
bounds.ub(i) = gaminv(1.0-priortrunc, p6(i), p7(i))+p3(i);
end
case 3
if prior_trunc == 0
if priortrunc==0
bounds.lb(i) = -Inf;
bounds.ub(i) = Inf;
else
bounds.lb(i) = norminv(prior_trunc,p6(i),p7(i));
bounds.ub(i) = norminv(1-prior_trunc,p6(i),p7(i));
bounds.lb(i) = norminv(priortrunc, p6(i), p7(i));
bounds.ub(i) = norminv(1.0-priortrunc, p6(i), p7(i));
end
case 4
if prior_trunc == 0
if priortrunc==0
bounds.lb(i) = p3(i);
bounds.ub(i) = Inf;
else
bounds.lb(i) = 1/sqrt(gaminv(1-prior_trunc, p7(i)/2, 2/p6(i)))+p3(i);
bounds.ub(i) = 1/sqrt(gaminv(prior_trunc, p7(i)/2, 2/p6(i)))+p3(i);
bounds.lb(i) = 1.0/sqrt(gaminv(1.0-priortrunc, p7(i)/2.0, 2.0/p6(i)))+p3(i);
bounds.ub(i) = 1.0/sqrt(gaminv(priortrunc, p7(i)/2.0, 2.0/p6(i)))+p3(i);
end
case 5
if prior_trunc == 0
if priortrunc == 0
bounds.lb(i) = p6(i);
bounds.ub(i) = p7(i);
else
bounds.lb(i) = p6(i)+(p7(i)-p6(i))*prior_trunc;
bounds.ub(i) = p7(i)-(p7(i)-p6(i))*prior_trunc;
bounds.lb(i) = p6(i)+(p7(i)-p6(i))*priortrunc;
bounds.ub(i) = p7(i)-(p7(i)-p6(i))*priortrunc;
end
case 6
if prior_trunc == 0
if priortrunc == 0
bounds.lb(i) = p3(i);
bounds.ub(i) = Inf;
else
bounds.lb(i) = 1/gaminv(1-prior_trunc, p7(i)/2, 2/p6(i))+p3(i);
bounds.ub(i) = 1/gaminv(prior_trunc, p7(i)/2, 2/p6(i))+ p3(i);
bounds.lb(i) = 1.0/gaminv(1.0-priortrunc, p7(i)/2.0, 2.0/p6(i))+p3(i);
bounds.ub(i) = 1.0/gaminv(priortrunc, p7(i)/2.0, 2.0/p6(i))+ p3(i);
end
case 8
if prior_trunc == 0
if priortrunc == 0
bounds.lb(i) = p3(i);
bounds.ub(i) = Inf;
else
bounds.lb(i) = p3(i)+wblinv(prior_trunc,p6(i),p7(i));
bounds.ub(i) = p3(i)+wblinv(1-prior_trunc,p6(i),p7(i));
bounds.lb(i) = p3(i)+wblinv(priortrunc, p6(i), p7(i));
bounds.ub(i) = p3(i)+wblinv(1.0-priortrunc, p6(i), p7(i));
end
otherwise
error(sprintf('prior_bounds: unknown distribution shape (index %d, type %d)', i, pshape(i)));
end
end
end