Added main routine for non linear filters.
parent
bf69bac140
commit
c152bbedd4
|
@ -0,0 +1,375 @@
|
|||
function [fval,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults] = non_linear_dsge_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
|
||||
% Evaluates the posterior kernel of a dsge model using a non linear filter.
|
||||
|
||||
%@info:
|
||||
%! @deftypefn {Function File} {[@var{fval},@var{exit_flag},@var{ys},@var{trend_coeff},@var{info},@var{Model},@var{DynareOptions},@var{BayesInfo},@var{DynareResults}] =} non_linear_dsge_likelihood (@var{xparam1},@var{DynareDataset},@var{DynareOptions},@var{Model},@var{EstimatedParameters},@var{BayesInfo},@var{DynareResults})
|
||||
%! @anchor{dsge_likelihood}
|
||||
%! @sp 1
|
||||
%! Evaluates the posterior kernel of a dsge model using a non linear filter.
|
||||
%! @sp 2
|
||||
%! @strong{Inputs}
|
||||
%! @sp 1
|
||||
%! @table @ @var
|
||||
%! @item xparam1
|
||||
%! Vector of doubles, current values for the estimated parameters.
|
||||
%! @item DynareDataset
|
||||
%! Matlab's structure describing the dataset (initialized by dynare, see @ref{dataset_}).
|
||||
%! @item DynareOptions
|
||||
%! Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
|
||||
%! @item Model
|
||||
%! Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
|
||||
%! @item EstimatedParamemeters
|
||||
%! Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
|
||||
%! @item BayesInfo
|
||||
%! Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
|
||||
%! @item DynareResults
|
||||
%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
|
||||
%! @end table
|
||||
%! @sp 2
|
||||
%! @strong{Outputs}
|
||||
%! @sp 1
|
||||
%! @table @ @var
|
||||
%! @item fval
|
||||
%! Double scalar, value of (minus) the likelihood.
|
||||
%! @item exit_flag
|
||||
%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
|
||||
%! @item ys
|
||||
%! Vector of doubles, steady state level for the endogenous variables.
|
||||
%! @item trend_coeffs
|
||||
%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
|
||||
%! @item info
|
||||
%! Integer scalar, error code.
|
||||
%! @table @ @code
|
||||
%! @item info==0
|
||||
%! No error.
|
||||
%! @item info==1
|
||||
%! The model doesn't determine the current variables uniquely.
|
||||
%! @item info==2
|
||||
%! MJDGGES returned an error code.
|
||||
%! @item info==3
|
||||
%! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
|
||||
%! @item info==4
|
||||
%! Blanchard & Kahn conditions are not satisfied: indeterminacy.
|
||||
%! @item info==5
|
||||
%! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
|
||||
%! @item info==6
|
||||
%! The jacobian evaluated at the deterministic steady state is complex.
|
||||
%! @item info==19
|
||||
%! The steadystate routine thrown an exception (inconsistent deep parameters).
|
||||
%! @item info==20
|
||||
%! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
|
||||
%! @item info==21
|
||||
%! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
|
||||
%! @item info==22
|
||||
%! The steady has NaNs.
|
||||
%! @item info==23
|
||||
%! M_.params has been updated in the steadystate routine and has complex valued scalars.
|
||||
%! @item info==24
|
||||
%! M_.params has been updated in the steadystate routine and has some NaNs.
|
||||
%! @item info==30
|
||||
%! Ergodic variance can't be computed.
|
||||
%! @item info==41
|
||||
%! At least one parameter is violating a lower bound condition.
|
||||
%! @item info==42
|
||||
%! At least one parameter is violating an upper bound condition.
|
||||
%! @item info==43
|
||||
%! The covariance matrix of the structural innovations is not positive definite.
|
||||
%! @item info==44
|
||||
%! The covariance matrix of the measurement errors is not positive definite.
|
||||
%! @item info==45
|
||||
%! Likelihood is not a number (NaN).
|
||||
%! @item info==45
|
||||
%! Likelihood is a complex valued number.
|
||||
%! @end table
|
||||
%! @item Model
|
||||
%! Matlab's structure describing the model (initialized by dynare, see @ref{M_}).
|
||||
%! @item DynareOptions
|
||||
%! Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
|
||||
%! @item BayesInfo
|
||||
%! Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
|
||||
%! @item DynareResults
|
||||
%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
|
||||
%! @end table
|
||||
%! @sp 2
|
||||
%! @strong{This function is called by:}
|
||||
%! @sp 1
|
||||
%! @ref{dynare_estimation_1}, @ref{mode_check}
|
||||
%! @sp 2
|
||||
%! @strong{This function calls:}
|
||||
%! @sp 1
|
||||
%! @ref{dynare_resolve}, @ref{lyapunov_symm}, @ref{priordens}
|
||||
%! @end deftypefn
|
||||
%@eod:
|
||||
|
||||
% Copyright (C) 2010-2011 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
|
||||
% frederic DOT karame AT univ DASH lemans DOT fr
|
||||
|
||||
% Declaration of the penalty as a persistent variable.
|
||||
persistent penalty
|
||||
persistent init_flag
|
||||
persistent restrict_variables_idx observed_variables_idx state_variables_idx mf0 mf1
|
||||
persistent sample_size number_of_state_variables number_of_observed_variables number_of_structural_innovations
|
||||
|
||||
|
||||
% Initialization of the persistent variable.
|
||||
if ~nargin || isempty(penalty)
|
||||
penalty = 1e8;
|
||||
if ~nargin, return, end
|
||||
end
|
||||
if nargin==1
|
||||
penalty = xparam1;
|
||||
return
|
||||
end
|
||||
|
||||
% Initialization of the returned arguments.
|
||||
fval = [];
|
||||
ys = [];
|
||||
trend_coeff = [];
|
||||
cost_flag = 1;
|
||||
|
||||
% Set the number of observed variables
|
||||
nvobs = DynareDataset.info.vobs;
|
||||
|
||||
%------------------------------------------------------------------------------
|
||||
% 1. Get the structural parameters & define penalties
|
||||
%------------------------------------------------------------------------------
|
||||
|
||||
% Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
|
||||
if (DynareOptions.mode_compute~=1) & any(xparam1<BayesInfo.lb)
|
||||
k = find(xparam1 < BayesInfo.lb);
|
||||
fval = penalty+sum((BayesInfo.lb(k)-xparam1(k)).^2);
|
||||
cost_flag = 0;
|
||||
info = 41;
|
||||
return
|
||||
end
|
||||
|
||||
% Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
|
||||
if (DynareOptions.mode_compute~=1) & any(xparam1>BayesInfo.ub)
|
||||
k = find(xparam1>BayesInfo.ub);
|
||||
fval = penalty+sum((xparam1(k)-BayesInfo.ub(k)).^2);
|
||||
cost_flag = 0;
|
||||
info = 42;
|
||||
return
|
||||
end
|
||||
|
||||
% Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
|
||||
Q = Model.Sigma_e;
|
||||
H = Model.H;
|
||||
for i=1:EstimatedParameters_.nvx
|
||||
k =EstimatedParameters_.var_exo(i,1);
|
||||
Q(k,k) = xparam1(i)*xparam1(i);
|
||||
end
|
||||
offset = EstimatedParameters_.nvx;
|
||||
if EstimatedParameters_.nvn
|
||||
for i=1:EstimatedParameters_.nvn
|
||||
k = EstimatedParameters_.var_endo(i,1);
|
||||
H(k,k) = xparam1(i+offset)*xparam1(i+offset);
|
||||
end
|
||||
offset = offset+EstimatedParameters_.nvn;
|
||||
else
|
||||
H = zeros(nvobs);
|
||||
end
|
||||
|
||||
% Get the off-diagonal elements of the covariance matrix for the structural innovations. Test if Q is positive definite.
|
||||
if EstimatedParameters_.ncx
|
||||
for i=1:EstimatedParameters_.ncx
|
||||
k1 =EstimatedParameters_.corrx(i,1);
|
||||
k2 =EstimatedParameters_.corrx(i,2);
|
||||
Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
|
||||
Q(k2,k1) = Q(k1,k2);
|
||||
end
|
||||
% Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
|
||||
[CholQ,testQ] = chol(Q);
|
||||
if testQ
|
||||
% The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
|
||||
a = diag(eig(Q));
|
||||
k = find(a < 0);
|
||||
if k > 0
|
||||
fval = penalty+sum(-a(k));
|
||||
cost_flag = 0;
|
||||
info = 43;
|
||||
return
|
||||
end
|
||||
end
|
||||
offset = offset+EstimatedParameters_.ncx;
|
||||
end
|
||||
|
||||
% Get the off-diagonal elements of the covariance matrix for the measurement errors. Test if H is positive definite.
|
||||
if EstimatedParameters_.ncn
|
||||
for i=1:EstimatedParameters_.ncn
|
||||
k1 = DynareOptions.lgyidx2varobs(EstimatedParameters_.corrn(i,1));
|
||||
k2 = DynareOptions.lgyidx2varobs(EstimatedParameters_.corrn(i,2));
|
||||
H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
|
||||
H(k2,k1) = H(k1,k2);
|
||||
end
|
||||
% Try to compute the cholesky decomposition of H (possible iff H is positive definite)
|
||||
[CholH,testH] = chol(H);
|
||||
if testH
|
||||
% The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
|
||||
a = diag(eig(H));
|
||||
k = find(a < 0);
|
||||
if k > 0
|
||||
fval = penalty+sum(-a(k));
|
||||
cost_flag = 0;
|
||||
info = 44;
|
||||
return
|
||||
end
|
||||
end
|
||||
offset = offset+EstimatedParameters_.ncn;
|
||||
end
|
||||
|
||||
% Update estimated structural parameters in Mode.params.
|
||||
if EstimatedParameters_.np > 0
|
||||
Model.params(EstimatedParameters_.param_vals(:,1)) = xparam1(offset+1:end);
|
||||
end
|
||||
|
||||
% Update Model.Sigma_e and Model.H.
|
||||
Model.Sigma_e = Q;
|
||||
Model.H = H;
|
||||
|
||||
%------------------------------------------------------------------------------
|
||||
% 2. call model setup & reduction program
|
||||
%------------------------------------------------------------------------------
|
||||
|
||||
% Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
|
||||
[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
|
||||
|
||||
if info(1) == 1 || info(1) == 2 || info(1) == 5
|
||||
fval = penalty+1;
|
||||
cost_flag = 0;
|
||||
return
|
||||
elseif info(1) == 3 || info(1) == 4 || info(1)==6 ||info(1) == 19 || info(1) == 20 || info(1) == 21
|
||||
fval = penalty+info(2);
|
||||
cost_flag = 0;
|
||||
return
|
||||
end
|
||||
|
||||
% Define a vector of indices for the observed variables. Is this really usefull?...
|
||||
BayesInfo.mf = BayesInfo.mf1;
|
||||
|
||||
% Define the deterministic linear trend of the measurement equation.
|
||||
if DynareOptions.noconstant
|
||||
constant = zeros(nvobs,1);
|
||||
else
|
||||
if DynareOptions.loglinear
|
||||
constant = log(SteadyState(BayesInfo.mfys));
|
||||
else
|
||||
constant = SteadyState(BayesInfo.mfys);
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
% Define the deterministic linear trend of the measurement equation.
|
||||
if BayesInfo.with_trend
|
||||
trend_coeff = zeros(DynareDataset.info.nvobs,1);
|
||||
t = DynareOptions.trend_coeffs;
|
||||
for i=1:length(t)
|
||||
if ~isempty(t{i})
|
||||
trend_coeff(i) = evalin('base',t{i});
|
||||
end
|
||||
end
|
||||
trend = repmat(constant,1,DynareDataset.info.ntobs)+trend_coeff*[1:DynareDataset.info.ntobs];
|
||||
else
|
||||
trend = repmat(constant,1,DynareDataset.info.ntobs);
|
||||
end
|
||||
|
||||
% Get needed informations for kalman filter routines.
|
||||
start = DynareOptions.presample+1;
|
||||
np = size(T,1);
|
||||
mf = BayesInfo.mf;
|
||||
Y = transpose(dataset_.rawdata);
|
||||
|
||||
%------------------------------------------------------------------------------
|
||||
% 3. Initial condition of the Kalman filter
|
||||
%------------------------------------------------------------------------------
|
||||
|
||||
% Get decision rules and transition equations.
|
||||
dr = DynareResults.dr;
|
||||
|
||||
% Set persistent variables (first call).
|
||||
if isempty(init_flag)
|
||||
mf0 = BayesInfo.mf0;
|
||||
mf1 = BayesInfo.mf1;
|
||||
restrict_variables_idx = BayesInfo.restrict_var_list;
|
||||
observed_variables_idx = restrict_variables_idx(mf1);
|
||||
state_variables_idx = restrict_variables_idx(mf0);
|
||||
sample_size = size(Y,2);
|
||||
number_of_state_variables = length(mf0);
|
||||
number_of_observed_variables = length(mf1);
|
||||
number_of_structural_innovations = length(Q);
|
||||
init_flag = 1;
|
||||
end
|
||||
|
||||
ReducedForm.ghx = dr.ghx(restrict_variables_idx,:);
|
||||
ReducedForm.ghu = dr.ghu(restrict_variables_idx,:);
|
||||
ReducedForm.ghxx = dr.ghxx(restrict_variables_idx,:);
|
||||
ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
|
||||
ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
|
||||
ReducedForm.steadystate = dr.ys(dr.order_var(restrict_variables_idx));
|
||||
ReducedForm.constant = ReducedForm.steadystate + .5*dr.ghs2(restrict_variables_idx);
|
||||
ReducedForm.state_variables_steady_state = dr.ys(dr.order_var(state_variables_idx));
|
||||
ReducedForm.Q = Q;
|
||||
ReducedForm.H = H;
|
||||
ReducedForm.mf0 = mf0;
|
||||
ReducedForm.mf1 = mf1;
|
||||
|
||||
% Set initial condition.
|
||||
switch DynareOptions.particle.initialization
|
||||
case 1% Initial state vector variance is the ergodic variance associated to the first order Taylor-approximation of the model.
|
||||
StateVectorMean = ReducedForm.constant(mf0);
|
||||
StateVectorVariance = lyapunov_symm(ReducedForm.ghx(mf0,:),ReducedForm.ghu(mf0,:)*ReducedForm.Q*ReducedForm.ghu(mf0,:)',1e-12,1e-12);
|
||||
case 2% Initial state vector variance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model).
|
||||
StateVectorMean = ReducedForm.constant(mf0);
|
||||
old_DynareOptionsperiods = DynareOptions.periods;
|
||||
DynareOptions.periods = 5000;
|
||||
y_ = simult(oo_.steady_state, dr);
|
||||
y_ = y_(state_variables_idx,2001:5000);
|
||||
StateVectorVariance = cov(y_');
|
||||
DynareOptions.periods = old_DynareOptionsperiods;
|
||||
clear('old_DynareOptionsperiods','y_');
|
||||
case 3
|
||||
StateVectorMean = ReducedForm.constant(mf0);
|
||||
StateVectorVariance = DynareOptions.particle.initial_state_prior_std*eye(number_of_state_variables);
|
||||
otherwise
|
||||
error('Unknown initialization option!')
|
||||
end
|
||||
ReducedForm.StateVectorMean = StateVectorMean;
|
||||
ReducedForm.StateVectorVariance = StateVectorVariance;
|
||||
|
||||
%------------------------------------------------------------------------------
|
||||
% 4. Likelihood evaluation
|
||||
%------------------------------------------------------------------------------
|
||||
DynareOptions.warning_for_steadystate = 0;
|
||||
LIK = feval(DynareOptions.particle.algorithm,ReducedForm,Y,[]);
|
||||
if imag(LIK)
|
||||
likelihood = penalty;
|
||||
cost_flag = 0;
|
||||
elseif isnan(LIK)
|
||||
likelihood = penalty;
|
||||
cost_flag = 0;
|
||||
else
|
||||
likelihood = LIK;
|
||||
end
|
||||
DynareOptions.warning_for_steadystate = 1;
|
||||
% ------------------------------------------------------------------------------
|
||||
% Adds prior if necessary
|
||||
% ------------------------------------------------------------------------------
|
||||
lnprior = priordens(xparam1,BayesInfo.pshape,BayesInfo.p6,BayesInfo.p7,BayesInfo.p3,BayesInfo.p4);
|
||||
fval = (likelihood-lnprior);
|
Loading…
Reference in New Issue