351 lines
14 KiB
Matlab
351 lines
14 KiB
Matlab
function [ys,trend_coeff,exit_flag,info,Model,DynareOptions,BayesInfo,DynareResults,ReducedForm] = solve_model_for_online_filter(observation_number,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
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% solve the dsge model for an particular parameters set.
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%@info:
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%! @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})
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%! @anchor{dsge_likelihood}
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%! @sp 1
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%! Evaluates the posterior kernel of a dsge model using a non linear filter.
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%! @sp 2
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%! @strong{Inputs}
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%! @sp 1
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%! @table @ @var
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%! @item xparam1
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%! Vector of doubles, current values for the estimated parameters.
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%! @item DynareDataset
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%! Matlab's structure describing the dataset (initialized by dynare, see @ref{dataset_}).
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%! @item DynareOptions
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%! Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
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%! @item Model
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%! Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
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%! @item EstimatedParamemeters
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%! Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
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%! @item BayesInfo
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%! Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
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%! @item DynareResults
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%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
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%! @end table
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%! @sp 2
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%! @strong{Outputs}
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%! @sp 1
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%! @table @ @var
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%! @item fval
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%! Double scalar, value of (minus) the likelihood.
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%! @item exit_flag
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%! Integer scalar, equal to zero if the routine return with a penalty (one otherwise).
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%! @item ys
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%! Vector of doubles, steady state level for the endogenous variables.
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%! @item trend_coeffs
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%! Matrix of doubles, coefficients of the deterministic trend in the measurement equation.
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%! @item info
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%! Integer scalar, error code.
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%! @table @ @code
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%! @item info==0
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%! No error.
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%! @item info==1
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%! The model doesn't determine the current variables uniquely.
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%! @item info==2
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%! MJDGGES returned an error code.
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%! @item info==3
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%! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
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%! @item info==4
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%! Blanchard & Kahn conditions are not satisfied: indeterminacy.
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%! @item info==5
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%! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
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%! @item info==6
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%! The jacobian evaluated at the deterministic steady state is complex.
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%! @item info==19
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%! The steadystate routine thrown an exception (inconsistent deep parameters).
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%! @item info==20
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%! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
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%! @item info==21
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%! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
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%! @item info==22
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%! The steady has NaNs.
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%! @item info==23
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%! M_.params has been updated in the steadystate routine and has complex valued scalars.
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%! @item info==24
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%! M_.params has been updated in the steadystate routine and has some NaNs.
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%! @item info==30
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%! Ergodic variance can't be computed.
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%! @item info==41
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%! At least one parameter is violating a lower bound condition.
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%! @item info==42
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%! At least one parameter is violating an upper bound condition.
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%! @item info==43
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%! The covariance matrix of the structural innovations is not positive definite.
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%! @item info==44
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%! The covariance matrix of the measurement errors is not positive definite.
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%! @item info==45
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%! Likelihood is not a number (NaN).
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%! @item info==45
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%! Likelihood is a complex valued number.
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%! @end table
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%! @item Model
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%! Matlab's structure describing the model (initialized by dynare, see @ref{M_}).
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%! @item DynareOptions
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%! Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
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%! @item BayesInfo
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%! Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
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%! @item DynareResults
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%! Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
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%! @end table
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%! @sp 2
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%! @strong{This function is called by:}
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%! @sp 1
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%! @ref{dynare_estimation_1}, @ref{mode_check}
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%! @sp 2
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%! @strong{This function calls:}
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%! @sp 1
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%! @ref{dynare_resolve}, @ref{lyapunov_symm}, @ref{priordens}
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%! @end deftypefn
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%@eod:
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% Copyright (C) 2013 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
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% frederic DOT karame AT univ DASH lemans DOT fr
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%global objective_function_penalty_base
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% Declaration of the penalty as a persistent variable.
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persistent init_flag
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persistent restrict_variables_idx observed_variables_idx state_variables_idx mf0 mf1
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persistent sample_size number_of_state_variables number_of_observed_variables number_of_structural_innovations
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% Initialization of the returned arguments.
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fval = [];
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ys = [];
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trend_coeff = [];
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exit_flag = 1;
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% Set the number of observed variables
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%nvobs = DynareDataset.info.nvobs;
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nvobs = size(DynareDataset.data,1) ;
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%------------------------------------------------------------------------------
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% 1. Get the structural parameters & define penalties
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%------------------------------------------------------------------------------
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% Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
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%if (DynareOptions.mode_compute~=1) && any(xparam1<BayesInfo.lb)
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% k = find(xparam1(:) < BayesInfo.lb);
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% fval = objective_function_penalty_base+sum((BayesInfo.lb(k)-xparam1(k)).^2);
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% exit_flag = 0;
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% info = 41;
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% return
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%end
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% Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
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%if (DynareOptions.mode_compute~=1) && any(xparam1>BayesInfo.ub)
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% k = find(xparam1(:)>BayesInfo.ub);
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% fval = objective_function_penalty_base+sum((xparam1(k)-BayesInfo.ub(k)).^2);
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% exit_flag = 0;
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% info = 42;
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% return
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%end
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% Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
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Q = Model.Sigma_e;
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H = Model.H;
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for i=1:EstimatedParameters.nvx
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k =EstimatedParameters.var_exo(i,1);
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Q(k,k) = xparam1(i)*xparam1(i);
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end
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offset = EstimatedParameters.nvx;
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if EstimatedParameters.nvn
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for i=1:EstimatedParameters.nvn
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H(i,i) = xparam1(i+offset)*xparam1(i+offset);
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end
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offset = offset+EstimatedParameters.nvn;
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else
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H = zeros(nvobs);
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end
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% Get the off-diagonal elements of the covariance matrix for the structural innovations. Test if Q is positive definite.
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if EstimatedParameters.ncx
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for i=1:EstimatedParameters.ncx
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k1 =EstimatedParameters.corrx(i,1);
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k2 =EstimatedParameters.corrx(i,2);
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Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
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Q(k2,k1) = Q(k1,k2);
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end
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% Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
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% [CholQ,testQ] = chol(Q);
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% if testQ
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% 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.
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% a = diag(eig(Q));
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% k = find(a < 0);
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% if k > 0
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% fval = objective_function_penalty_base+sum(-a(k));
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% exit_flag = 0;
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% info = 43;
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% return
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% end
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% end
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offset = offset+EstimatedParameters.ncx;
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end
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% Get the off-diagonal elements of the covariance matrix for the measurement errors. Test if H is positive definite.
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if EstimatedParameters.ncn
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corrn_observable_correspondence = EstimatedParameters.corrn_observable_correspondence;
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for i=1:EstimatedParameters.ncn
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k1 = corrn_observable_correspondence(i,1);
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k2 = corrn_observable_correspondence(i,2);
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H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
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H(k2,k1) = H(k1,k2);
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end
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% Try to compute the cholesky decomposition of H (possible iff H is positive definite)
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% [CholH,testH] = chol(H);
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% if testH
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% 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.
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% a = diag(eig(H));
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% k = find(a < 0);
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% if k > 0
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% fval = objective_function_penalty_base+sum(-a(k));
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% exit_flag = 0;
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% info = 44;
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% return
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% end
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% end
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offset = offset+EstimatedParameters.ncn;
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end
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% Update estimated structural parameters in Mode.params.
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if EstimatedParameters.np > 0
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Model.params(EstimatedParameters.param_vals(:,1)) = xparam1(offset+1:end);
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end
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% Update Model.Sigma_e and Model.H.
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Model.Sigma_e = Q;
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Model.H = H;
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%------------------------------------------------------------------------------
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% 2. call model setup & reduction program
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%------------------------------------------------------------------------------
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% Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
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[T,R,SteadyState,info,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults,'restrict');
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%disp(info)
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if info(1) ~= 0
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ReducedForm = 0 ;
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exit_flag = 55;
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return
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end
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% Define a vector of indices for the observed variables. Is this really usefull?...
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BayesInfo.mf = BayesInfo.mf1;
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% Define the deterministic linear trend of the measurement equation.
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if DynareOptions.noconstant
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constant = zeros(nvobs,1);
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else
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if DynareOptions.loglinear
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constant = log(SteadyState(BayesInfo.mfys));
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else
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constant = SteadyState(BayesInfo.mfys);
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end
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end
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% Define the deterministic linear trend of the measurement equation.
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%if BayesInfo.with_trend
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% trend_coeff = zeros(DynareDataset.info.nvobs,1);
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% t = DynareOptions.trend_coeffs;
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% for i=1:length(t)
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% if ~isempty(t{i})
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% trend_coeff(i) = evalin('base',t{i});
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% end
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% end
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% trend = repmat(constant,1,DynareDataset.info.ntobs)+trend_coeff*[1:DynareDataset.info.ntobs];
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%else
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% trend = repmat(constant,1,DynareDataset.info.ntobs);
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%end
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% Get needed informations for kalman filter routines.
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start = DynareOptions.presample+1;
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np = size(T,1);
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mf = BayesInfo.mf;
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Y = transpose(DynareDataset.data);
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%------------------------------------------------------------------------------
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% 3. Initial condition of the Kalman filter
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%------------------------------------------------------------------------------
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% Get decision rules and transition equations.
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dr = DynareResults.dr;
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% Set persistent variables (first call).
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if isempty(init_flag)
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mf0 = BayesInfo.mf0;
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mf1 = BayesInfo.mf1;
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restrict_variables_idx = dr.restrict_var_list;
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observed_variables_idx = restrict_variables_idx(mf1);
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state_variables_idx = restrict_variables_idx(mf0);
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sample_size = size(Y,2);
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number_of_state_variables = length(mf0);
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number_of_observed_variables = length(mf1);
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number_of_structural_innovations = length(Q);
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init_flag = 1;
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end
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ReducedForm.ghx = dr.ghx(restrict_variables_idx,:);
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ReducedForm.ghu = dr.ghu(restrict_variables_idx,:);
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ReducedForm.steadystate = dr.ys(dr.order_var(restrict_variables_idx));
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if DynareOptions.order>1
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ReducedForm.ghxx = dr.ghxx(restrict_variables_idx,:);
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ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
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ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
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ReducedForm.constant = ReducedForm.steadystate + .5*dr.ghs2(restrict_variables_idx);
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else
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ReducedForm.ghxx = zeros(size(restrict_variables_idx,1),size(dr.kstate,2));
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ReducedForm.ghuu = zeros(size(restrict_variables_idx,1),size(dr.ghu,2));
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ReducedForm.ghxu = zeros(size(restrict_variables_idx,1),size(dr.ghx,2));
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ReducedForm.constant = ReducedForm.steadystate ;
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end
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ReducedForm.state_variables_steady_state = dr.ys(dr.order_var(state_variables_idx));
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ReducedForm.Q = Q;
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ReducedForm.H = H;
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ReducedForm.mf0 = mf0;
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ReducedForm.mf1 = mf1;
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% Set initial condition for t=1
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if observation_number==1
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switch DynareOptions.particle.initialization
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case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model.
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StateVectorMean = ReducedForm.constant(mf0);
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StateVectorVariance = lyapunov_symm(ReducedForm.ghx(mf0,:),ReducedForm.ghu(mf0,:)*ReducedForm.Q*ReducedForm.ghu(mf0,:)',DynareOptions.lyapunov_fixed_point_tol,DynareOptions.qz_criterium,DynareOptions.lyapunov_complex_threshold);
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case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model).
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StateVectorMean = ReducedForm.constant(mf0);
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old_DynareOptionsperiods = DynareOptions.periods;
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DynareOptions.periods = 5000;
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y_ = simult(oo_.steady_state, dr,Model,DynareOptions,DynareResults);
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y_ = y_(state_variables_idx,2001:5000);
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StateVectorVariance = cov(y_');
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DynareOptions.periods = old_DynareOptionsperiods;
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clear('old_DynareOptionsperiods','y_');
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case 3% Initial state vector covariance is a diagonal matrix.
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StateVectorMean = ReducedForm.constant(mf0);
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StateVectorVariance = DynareOptions.particle.initial_state_prior_std*eye(number_of_state_variables);
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otherwise
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error('Unknown initialization option!')
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end
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ReducedForm.StateVectorMean = StateVectorMean;
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ReducedForm.StateVectorVariance = StateVectorVariance;
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end
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