%
% Status : main Dynare file
%
% Warning : this file is generated automatically by Dynare
%           from model file (.mod)

clearvars -global
clear_persistent_variables(fileparts(which('dynare')), false)
tic0 = tic;
% Define global variables.
global M_ options_ oo_ estim_params_ bayestopt_ dataset_ dataset_info estimation_info
options_ = [];
M_.fname = 'Model10';
M_.dynare_version = '6.2';
oo_.dynare_version = '6.2';
options_.dynare_version = '6.2';
%
% Some global variables initialization
%
global_initialization;
M_.exo_names = cell(5,1);
M_.exo_names_tex = cell(5,1);
M_.exo_names_long = cell(5,1);
M_.exo_names(1) = {'epsilona'};
M_.exo_names_tex(1) = {'epsilona'};
M_.exo_names_long(1) = {'epsilona'};
M_.exo_names(2) = {'epsilone'};
M_.exo_names_tex(2) = {'epsilone'};
M_.exo_names_long(2) = {'epsilone'};
M_.exo_names(3) = {'epsilonx'};
M_.exo_names_tex(3) = {'epsilonx'};
M_.exo_names_long(3) = {'epsilonx'};
M_.exo_names(4) = {'epsilonz'};
M_.exo_names_tex(4) = {'epsilonz'};
M_.exo_names_long(4) = {'epsilonz'};
M_.exo_names(5) = {'epsilonv'};
M_.exo_names_tex(5) = {'epsilonv'};
M_.exo_names_long(5) = {'epsilonv'};
M_.endo_names = cell(30,1);
M_.endo_names_tex = cell(30,1);
M_.endo_names_long = cell(30,1);
M_.endo_names(1) = {'dc'};
M_.endo_names_tex(1) = {'dc'};
M_.endo_names_long(1) = {'dc'};
M_.endo_names(2) = {'dr'};
M_.endo_names_tex(2) = {'dr'};
M_.endo_names_long(2) = {'dr'};
M_.endo_names(3) = {'dm'};
M_.endo_names_tex(3) = {'dm'};
M_.endo_names_long(3) = {'dm'};
M_.endo_names(4) = {'dpi'};
M_.endo_names_tex(4) = {'dpi'};
M_.endo_names_long(4) = {'dpi'};
M_.endo_names(5) = {'din'};
M_.endo_names_tex(5) = {'din'};
M_.endo_names_long(5) = {'din'};
M_.endo_names(6) = {'r'};
M_.endo_names_tex(6) = {'r'};
M_.endo_names_long(6) = {'r'};
M_.endo_names(7) = {'h'};
M_.endo_names_tex(7) = {'h'};
M_.endo_names_long(7) = {'h'};
M_.endo_names(8) = {'x'};
M_.endo_names_tex(8) = {'x'};
M_.endo_names_long(8) = {'x'};
M_.endo_names(9) = {'a'};
M_.endo_names_tex(9) = {'a'};
M_.endo_names_long(9) = {'a'};
M_.endo_names(10) = {'e'};
M_.endo_names_tex(10) = {'e'};
M_.endo_names_long(10) = {'e'};
M_.endo_names(11) = {'z'};
M_.endo_names_tex(11) = {'z'};
M_.endo_names_long(11) = {'z'};
M_.endo_names(12) = {'v'};
M_.endo_names_tex(12) = {'v'};
M_.endo_names_long(12) = {'v'};
M_.endo_names(13) = {'y'};
M_.endo_names_tex(13) = {'y'};
M_.endo_names_long(13) = {'y'};
M_.endo_names(14) = {'k'};
M_.endo_names_tex(14) = {'k'};
M_.endo_names_long(14) = {'k'};
M_.endo_names(15) = {'in'};
M_.endo_names_tex(15) = {'in'};
M_.endo_names_long(15) = {'in'};
M_.endo_names(16) = {'c'};
M_.endo_names_tex(16) = {'c'};
M_.endo_names_long(16) = {'c'};
M_.endo_names(17) = {'d'};
M_.endo_names_tex(17) = {'d'};
M_.endo_names_long(17) = {'d'};
M_.endo_names(18) = {'m'};
M_.endo_names_tex(18) = {'m'};
M_.endo_names_long(18) = {'m'};
M_.endo_names(19) = {'w'};
M_.endo_names_tex(19) = {'w'};
M_.endo_names_long(19) = {'w'};
M_.endo_names(20) = {'q'};
M_.endo_names_tex(20) = {'q'};
M_.endo_names_long(20) = {'q'};
M_.endo_names(21) = {'n'};
M_.endo_names_tex(21) = {'n'};
M_.endo_names_long(21) = {'n'};
M_.endo_names(22) = {'tau'};
M_.endo_names_tex(22) = {'tau'};
M_.endo_names_long(22) = {'tau'};
M_.endo_names(23) = {'lambd'};
M_.endo_names_tex(23) = {'lambd'};
M_.endo_names_long(23) = {'lambd'};
M_.endo_names(24) = {'ksi'};
M_.endo_names_tex(24) = {'ksi'};
M_.endo_names_long(24) = {'ksi'};
M_.endo_names(25) = {'pi'};
M_.endo_names_tex(25) = {'pi'};
M_.endo_names_long(25) = {'pi'};
M_.endo_names(26) = {'Dataslrobs'};
M_.endo_names_tex(26) = {'Dataslrobs'};
M_.endo_names_long(26) = {'Dataslrobs'};
M_.endo_names(27) = {'Dataslpiobs'};
M_.endo_names_tex(27) = {'Dataslpiobs'};
M_.endo_names_long(27) = {'Dataslpiobs'};
M_.endo_names(28) = {'Dataslmobs'};
M_.endo_names_tex(28) = {'Dataslmobs'};
M_.endo_names_long(28) = {'Dataslmobs'};
M_.endo_names(29) = {'Dataslinobs'};
M_.endo_names_tex(29) = {'Dataslinobs'};
M_.endo_names_long(29) = {'Dataslinobs'};
M_.endo_names(30) = {'Dataslcobs'};
M_.endo_names_tex(30) = {'Dataslcobs'};
M_.endo_names_long(30) = {'Dataslcobs'};
M_.endo_partitions = struct();
M_.param_names = cell(28,1);
M_.param_names_tex = cell(28,1);
M_.param_names_long = cell(28,1);
M_.param_names(1) = {'rhoa'};
M_.param_names_tex(1) = {'rhoa'};
M_.param_names_long(1) = {'rhoa'};
M_.param_names(2) = {'rhoe'};
M_.param_names_tex(2) = {'rhoe'};
M_.param_names_long(2) = {'rhoe'};
M_.param_names(3) = {'rhox'};
M_.param_names_tex(3) = {'rhox'};
M_.param_names_long(3) = {'rhox'};
M_.param_names(4) = {'rhoz'};
M_.param_names_tex(4) = {'rhoz'};
M_.param_names_long(4) = {'rhoz'};
M_.param_names(5) = {'rhov'};
M_.param_names_tex(5) = {'rhov'};
M_.param_names_long(5) = {'rhov'};
M_.param_names(6) = {'alph'};
M_.param_names_tex(6) = {'alph'};
M_.param_names_long(6) = {'alph'};
M_.param_names(7) = {'phip'};
M_.param_names_tex(7) = {'phip'};
M_.param_names_long(7) = {'phip'};
M_.param_names(8) = {'phik'};
M_.param_names_tex(8) = {'phik'};
M_.param_names_long(8) = {'phik'};
M_.param_names(9) = {'lne'};
M_.param_names_tex(9) = {'lne'};
M_.param_names_long(9) = {'lne'};
M_.param_names(10) = {'lnz'};
M_.param_names_tex(10) = {'lnz'};
M_.param_names_long(10) = {'lnz'};
M_.param_names(11) = {'sigmaa'};
M_.param_names_tex(11) = {'sigmaa'};
M_.param_names_long(11) = {'sigmaa'};
M_.param_names(12) = {'sigmae'};
M_.param_names_tex(12) = {'sigmae'};
M_.param_names_long(12) = {'sigmae'};
M_.param_names(13) = {'sigmax'};
M_.param_names_tex(13) = {'sigmax'};
M_.param_names_long(13) = {'sigmax'};
M_.param_names(14) = {'sigmaz'};
M_.param_names_tex(14) = {'sigmaz'};
M_.param_names_long(14) = {'sigmaz'};
M_.param_names(15) = {'sigmav'};
M_.param_names_tex(15) = {'sigmav'};
M_.param_names_long(15) = {'sigmav'};
M_.param_names(16) = {'omegapi'};
M_.param_names_tex(16) = {'omegapi'};
M_.param_names_long(16) = {'omegapi'};
M_.param_names(17) = {'omegay'};
M_.param_names_tex(17) = {'omegay'};
M_.param_names_long(17) = {'omegay'};
M_.param_names(18) = {'omegatau'};
M_.param_names_tex(18) = {'omegatau'};
M_.param_names_long(18) = {'omegatau'};
M_.param_names(19) = {'bet'};
M_.param_names_tex(19) = {'bet'};
M_.param_names_long(19) = {'bet'};
M_.param_names(20) = {'et'};
M_.param_names_tex(20) = {'et'};
M_.param_names_long(20) = {'et'};
M_.param_names(21) = {'delt'};
M_.param_names_tex(21) = {'delt'};
M_.param_names_long(21) = {'delt'};
M_.param_names(22) = {'thet'};
M_.param_names_tex(22) = {'thet'};
M_.param_names_long(22) = {'thet'};
M_.param_names(23) = {'gamm'};
M_.param_names_tex(23) = {'gamm'};
M_.param_names_long(23) = {'gamm'};
M_.param_names(24) = {'ctrend'};
M_.param_names_tex(24) = {'ctrend'};
M_.param_names_long(24) = {'ctrend'};
M_.param_names(25) = {'constepi'};
M_.param_names_tex(25) = {'constepi'};
M_.param_names_long(25) = {'constepi'};
M_.param_names(26) = {'conster'};
M_.param_names_tex(26) = {'conster'};
M_.param_names_long(26) = {'conster'};
M_.param_names(27) = {'intrend'};
M_.param_names_tex(27) = {'intrend'};
M_.param_names_long(27) = {'intrend'};
M_.param_names(28) = {'mtrend'};
M_.param_names_tex(28) = {'mtrend'};
M_.param_names_long(28) = {'mtrend'};
M_.param_partitions = struct();
M_.exo_det_nbr = 0;
M_.exo_nbr = 5;
M_.endo_nbr = 30;
M_.param_nbr = 28;
M_.orig_endo_nbr = 30;
M_.aux_vars = [];
options_.varobs = cell(5, 1);
options_.varobs(1)  = {'Dataslrobs'};
options_.varobs(2)  = {'Dataslpiobs'};
options_.varobs(3)  = {'Dataslmobs'};
options_.varobs(4)  = {'Dataslinobs'};
options_.varobs(5)  = {'Dataslcobs'};
options_.varobs_id = [ 26 27 28 29 30  ];
M_.Sigma_e = zeros(5, 5);
M_.Correlation_matrix = eye(5, 5);
M_.H = 0;
M_.Correlation_matrix_ME = 1;
M_.sigma_e_is_diagonal = true;
M_.det_shocks = [];
M_.surprise_shocks = [];
M_.learnt_shocks = [];
M_.learnt_endval = [];
M_.heteroskedastic_shocks.Qvalue_orig = [];
M_.heteroskedastic_shocks.Qscale_orig = [];
M_.matched_irfs = {};
M_.matched_irfs_weights = {};
options_.linear = false;
options_.block = false;
options_.bytecode = false;
options_.use_dll = false;
options_.ramsey_policy = false;
options_.discretionary_policy = false;
M_.nonzero_hessian_eqs = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25];
M_.hessian_eq_zero = isempty(M_.nonzero_hessian_eqs);
M_.eq_nbr = 30;
M_.ramsey_orig_eq_nbr = 0;
M_.ramsey_orig_endo_nbr = 0;
M_.set_auxiliary_variables = exist(['./+' M_.fname '/set_auxiliary_variables.m'], 'file') == 2;
M_.epilogue_names = {};
M_.epilogue_var_list_ = {};
M_.orig_maximum_endo_lag = 1;
M_.orig_maximum_endo_lead = 1;
M_.orig_maximum_exo_lag = 0;
M_.orig_maximum_exo_lead = 0;
M_.orig_maximum_exo_det_lag = 0;
M_.orig_maximum_exo_det_lead = 0;
M_.orig_maximum_lag = 1;
M_.orig_maximum_lead = 1;
M_.orig_maximum_lag_with_diffs_expanded = 1;
M_.lead_lag_incidence = [
 0 10 0;
 0 11 0;
 0 12 0;
 0 13 0;
 0 14 0;
 0 15 0;
 0 16 0;
 1 17 40;
 2 18 0;
 3 19 0;
 4 20 0;
 5 21 0;
 0 22 41;
 6 23 42;
 7 24 0;
 8 25 0;
 0 26 0;
 9 27 0;
 0 28 0;
 0 29 43;
 0 30 0;
 0 31 0;
 0 32 44;
 0 33 0;
 0 34 45;
 0 35 0;
 0 36 0;
 0 37 0;
 0 38 0;
 0 39 0;]';
M_.nstatic = 17;
M_.nfwrd   = 4;
M_.npred   = 7;
M_.nboth   = 2;
M_.nsfwrd   = 6;
M_.nspred   = 9;
M_.ndynamic   = 13;
M_.dynamic_tmp_nbr = [24; 19; 2; 0; ];
M_.equations_tags = {
  1 , 'name' , 'k' ;
  2 , 'name' , '2' ;
  3 , 'name' , '3' ;
  4 , 'name' , '4' ;
  5 , 'name' , '5' ;
  6 , 'name' , '6' ;
  7 , 'name' , 'a' ;
  8 , 'name' , '8' ;
  9 , 'name' , '9' ;
  10 , 'name' , 'lambd' ;
  11 , 'name' , '11' ;
  12 , 'name' , 'y' ;
  13 , 'name' , 'd' ;
  14 , 'name' , '14' ;
  15 , 'name' , '15' ;
  16 , 'name' , '16' ;
  17 , 'name' , '17' ;
  18 , 'name' , '18' ;
  19 , 'name' , 'n' ;
  20 , 'name' , 'tau' ;
  21 , 'name' , 'Dataslcobs' ;
  22 , 'name' , 'Dataslmobs' ;
  23 , 'name' , 'Dataslinobs' ;
  24 , 'name' , 'Dataslpiobs' ;
  25 , 'name' , 'Dataslrobs' ;
  26 , 'name' , 'din' ;
  27 , 'name' , 'dc' ;
  28 , 'name' , 'dpi' ;
  29 , 'name' , 'dr' ;
  30 , 'name' , 'dm' ;
};
M_.mapping.dc.eqidx = [27 ];
M_.mapping.dr.eqidx = [29 ];
M_.mapping.dm.eqidx = [30 ];
M_.mapping.dpi.eqidx = [28 ];
M_.mapping.din.eqidx = [26 ];
M_.mapping.r.eqidx = [9 10 18 25 29 ];
M_.mapping.h.eqidx = [8 13 14 17 19 ];
M_.mapping.x.eqidx = [1 2 11 ];
M_.mapping.a.eqidx = [3 7 ];
M_.mapping.e.eqidx = [4 7 9 ];
M_.mapping.z.eqidx = [5 17 ];
M_.mapping.v.eqidx = [6 18 ];
M_.mapping.y.eqidx = [12 13 14 15 16 17 18 19 ];
M_.mapping.k.eqidx = [1 11 12 13 15 17 ];
M_.mapping.in.eqidx = [1 12 23 26 ];
M_.mapping.c.eqidx = [7 9 12 21 27 ];
M_.mapping.d.eqidx = [13 ];
M_.mapping.m.eqidx = [7 9 20 22 30 ];
M_.mapping.w.eqidx = [8 13 14 ];
M_.mapping.q.eqidx = [11 13 15 ];
M_.mapping.n.eqidx = [19 ];
M_.mapping.tau.eqidx = [18 20 ];
M_.mapping.lambd.eqidx = [7 8 10 11 14 15 16 ];
M_.mapping.ksi.eqidx = [14 15 16 ];
M_.mapping.pi.eqidx = [10 12 13 16 18 20 24 28 ];
M_.mapping.Dataslrobs.eqidx = [25 ];
M_.mapping.Dataslpiobs.eqidx = [24 ];
M_.mapping.Dataslmobs.eqidx = [22 ];
M_.mapping.Dataslinobs.eqidx = [23 ];
M_.mapping.Dataslcobs.eqidx = [21 ];
M_.mapping.epsilona.eqidx = [3 ];
M_.mapping.epsilone.eqidx = [4 ];
M_.mapping.epsilonx.eqidx = [2 ];
M_.mapping.epsilonz.eqidx = [5 ];
M_.mapping.epsilonv.eqidx = [6 ];
M_.static_and_dynamic_models_differ = false;
M_.has_external_function = false;
M_.block_structure.time_recursive = false;
M_.block_structure.block(1).Simulation_Type = 3;
M_.block_structure.block(1).endo_nbr = 1;
M_.block_structure.block(1).mfs = 1;
M_.block_structure.block(1).equation = [ 2];
M_.block_structure.block(1).variable = [ 8];
M_.block_structure.block(1).is_linear = false;
M_.block_structure.block(1).NNZDerivatives = 2;
M_.block_structure.block(1).bytecode_jacob_cols_to_sparse = [0 1 ];
M_.block_structure.block(2).Simulation_Type = 3;
M_.block_structure.block(2).endo_nbr = 1;
M_.block_structure.block(2).mfs = 1;
M_.block_structure.block(2).equation = [ 3];
M_.block_structure.block(2).variable = [ 9];
M_.block_structure.block(2).is_linear = false;
M_.block_structure.block(2).NNZDerivatives = 2;
M_.block_structure.block(2).bytecode_jacob_cols_to_sparse = [0 1 ];
M_.block_structure.block(3).Simulation_Type = 3;
M_.block_structure.block(3).endo_nbr = 1;
M_.block_structure.block(3).mfs = 1;
M_.block_structure.block(3).equation = [ 4];
M_.block_structure.block(3).variable = [ 10];
M_.block_structure.block(3).is_linear = false;
M_.block_structure.block(3).NNZDerivatives = 2;
M_.block_structure.block(3).bytecode_jacob_cols_to_sparse = [0 1 ];
M_.block_structure.block(4).Simulation_Type = 3;
M_.block_structure.block(4).endo_nbr = 1;
M_.block_structure.block(4).mfs = 1;
M_.block_structure.block(4).equation = [ 5];
M_.block_structure.block(4).variable = [ 11];
M_.block_structure.block(4).is_linear = false;
M_.block_structure.block(4).NNZDerivatives = 2;
M_.block_structure.block(4).bytecode_jacob_cols_to_sparse = [0 1 ];
M_.block_structure.block(5).Simulation_Type = 3;
M_.block_structure.block(5).endo_nbr = 1;
M_.block_structure.block(5).mfs = 1;
M_.block_structure.block(5).equation = [ 6];
M_.block_structure.block(5).variable = [ 12];
M_.block_structure.block(5).is_linear = false;
M_.block_structure.block(5).NNZDerivatives = 2;
M_.block_structure.block(5).bytecode_jacob_cols_to_sparse = [0 1 ];
M_.block_structure.block(6).Simulation_Type = 8;
M_.block_structure.block(6).endo_nbr = 13;
M_.block_structure.block(6).mfs = 13;
M_.block_structure.block(6).equation = [ 1 7 8 10 20 16 17 9 11 12 14 15 18];
M_.block_structure.block(6).variable = [ 15 16 19 6 22 24 7 18 14 25 23 20 13];
M_.block_structure.block(6).is_linear = false;
M_.block_structure.block(6).NNZDerivatives = 55;
M_.block_structure.block(6).bytecode_jacob_cols_to_sparse = [8 9 14 15 16 17 18 19 20 21 22 23 24 25 26 35 36 37 38 39 ];
M_.block_structure.block(7).Simulation_Type = 1;
M_.block_structure.block(7).endo_nbr = 12;
M_.block_structure.block(7).mfs = 12;
M_.block_structure.block(7).equation = [ 27 26 25 28 29 30 24 23 22 21 19 13];
M_.block_structure.block(7).variable = [ 1 5 26 4 2 3 27 29 28 30 21 17];
M_.block_structure.block(7).is_linear = true;
M_.block_structure.block(7).NNZDerivatives = 12;
M_.block_structure.block(7).bytecode_jacob_cols_to_sparse = [13 14 15 16 17 18 19 20 21 22 23 24 ];
M_.block_structure.block(1).g1_sparse_rowval = int32([1 ]);
M_.block_structure.block(1).g1_sparse_colval = int32([1 ]);
M_.block_structure.block(1).g1_sparse_colptr = int32([1 2 ]);
M_.block_structure.block(2).g1_sparse_rowval = int32([1 ]);
M_.block_structure.block(2).g1_sparse_colval = int32([1 ]);
M_.block_structure.block(2).g1_sparse_colptr = int32([1 2 ]);
M_.block_structure.block(3).g1_sparse_rowval = int32([1 ]);
M_.block_structure.block(3).g1_sparse_colval = int32([1 ]);
M_.block_structure.block(3).g1_sparse_colptr = int32([1 2 ]);
M_.block_structure.block(4).g1_sparse_rowval = int32([1 ]);
M_.block_structure.block(4).g1_sparse_colval = int32([1 ]);
M_.block_structure.block(4).g1_sparse_colptr = int32([1 2 ]);
M_.block_structure.block(5).g1_sparse_rowval = int32([1 ]);
M_.block_structure.block(5).g1_sparse_colval = int32([1 ]);
M_.block_structure.block(5).g1_sparse_colptr = int32([1 2 ]);
M_.block_structure.block(6).g1_sparse_rowval = int32([5 1 7 9 10 12 1 10 2 8 10 3 11 4 8 13 5 13 6 11 12 3 7 11 2 5 8 1 9 10 5 6 10 13 2 3 4 6 9 11 12 12 6 7 10 11 12 13 9 4 4 6 9 9 6 ]);
M_.block_structure.block(6).g1_sparse_colval = int32([8 9 9 9 9 9 14 14 15 15 15 16 16 17 17 17 18 18 19 19 19 20 20 20 21 21 21 22 22 22 23 23 23 23 24 24 24 24 24 24 24 25 26 26 26 26 26 26 35 36 37 37 37 38 39 ]);
M_.block_structure.block(6).g1_sparse_colptr = int32([1 1 1 1 1 1 1 1 2 7 7 7 7 7 9 12 14 17 19 22 25 28 31 35 42 43 49 49 49 49 49 49 49 49 49 50 51 54 55 56 ]);
M_.block_structure.block(7).g1_sparse_rowval = int32([]);
M_.block_structure.block(7).g1_sparse_colval = int32([]);
M_.block_structure.block(7).g1_sparse_colptr = int32([]);
M_.block_structure.variable_reordered = [ 8 9 10 11 12 15 16 19 6 22 24 7 18 14 25 23 20 13 1 5 26 4 2 3 27 29 28 30 21 17];
M_.block_structure.equation_reordered = [ 2 3 4 5 6 1 7 8 10 20 16 17 9 11 12 14 15 18 27 26 25 28 29 30 24 23 22 21 19 13];
M_.block_structure.incidence(1).lead_lag = -1;
M_.block_structure.incidence(1).sparse_IM = [
 1 14;
 2 8;
 3 9;
 4 10;
 5 11;
 6 12;
 11 14;
 12 14;
 13 14;
 15 14;
 17 14;
 20 18;
 26 15;
 27 16;
 30 18;
];
M_.block_structure.incidence(2).lead_lag = 0;
M_.block_structure.incidence(2).sparse_IM = [
 1 8;
 1 14;
 1 15;
 2 8;
 3 9;
 4 10;
 5 11;
 6 12;
 7 9;
 7 10;
 7 16;
 7 18;
 7 23;
 8 7;
 8 19;
 8 23;
 9 6;
 9 10;
 9 16;
 9 18;
 10 6;
 10 23;
 11 8;
 11 14;
 11 23;
 12 13;
 12 14;
 12 15;
 12 16;
 12 25;
 13 7;
 13 13;
 13 17;
 13 19;
 13 20;
 13 25;
 14 7;
 14 13;
 14 19;
 14 23;
 14 24;
 15 13;
 15 20;
 15 23;
 15 24;
 16 13;
 16 23;
 16 24;
 16 25;
 17 7;
 17 11;
 17 13;
 18 6;
 18 12;
 18 13;
 18 22;
 18 25;
 19 7;
 19 13;
 19 21;
 20 18;
 20 22;
 20 25;
 21 16;
 21 30;
 22 18;
 22 28;
 23 15;
 23 29;
 24 25;
 24 27;
 25 6;
 25 26;
 26 5;
 26 15;
 27 1;
 27 16;
 28 4;
 28 25;
 29 2;
 29 6;
 30 3;
 30 18;
];
M_.block_structure.incidence(3).lead_lag = 1;
M_.block_structure.incidence(3).sparse_IM = [
 10 23;
 10 25;
 11 8;
 11 14;
 11 20;
 11 23;
 16 13;
 16 23;
];
M_.block_structure.dyn_tmp_nbr = 11;
M_.state_var = [8 9 10 11 12 15 16 18 14 ];
M_.maximum_lag = 1;
M_.maximum_lead = 1;
M_.maximum_endo_lag = 1;
M_.maximum_endo_lead = 1;
oo_.steady_state = zeros(30, 1);
M_.maximum_exo_lag = 0;
M_.maximum_exo_lead = 0;
oo_.exo_steady_state = zeros(5, 1);
M_.params = NaN(28, 1);
M_.endo_trends = struct('deflator', cell(30, 1), 'log_deflator', cell(30, 1), 'growth_factor', cell(30, 1), 'log_growth_factor', cell(30, 1));
M_.NNZDerivatives = [111; 143; -1; ];
M_.dynamic_g1_sparse_rowval = int32([2 3 4 5 6 1 11 12 13 15 17 26 27 20 30 27 29 30 28 26 9 10 18 25 29 8 13 14 17 19 1 2 11 3 7 4 7 9 5 17 6 18 12 13 14 15 16 17 18 19 1 11 12 1 12 23 26 7 9 12 21 27 13 7 9 20 22 30 8 13 14 13 15 19 18 20 7 8 10 11 14 15 16 14 15 16 12 13 16 18 20 24 28 25 24 22 23 21 11 16 11 11 10 11 16 10 3 4 2 5 6 ]);
M_.dynamic_g1_sparse_colval = int32([8 9 10 11 12 14 14 14 14 14 14 15 16 18 18 31 32 33 34 35 36 36 36 36 36 37 37 37 37 37 38 38 38 39 39 40 40 40 41 41 42 42 43 43 43 43 43 43 43 43 44 44 44 45 45 45 45 46 46 46 46 46 47 48 48 48 48 48 49 49 49 50 50 51 52 52 53 53 53 53 53 53 53 54 54 54 55 55 55 55 55 55 55 56 57 58 59 60 68 73 74 80 83 83 83 85 91 92 93 94 95 ]);
M_.dynamic_g1_sparse_colptr = int32([1 1 1 1 1 1 1 1 2 3 4 5 6 6 12 13 14 14 16 16 16 16 16 16 16 16 16 16 16 16 16 17 18 19 20 21 26 31 34 36 39 41 43 51 54 58 63 64 69 72 74 75 77 84 87 94 95 96 97 98 99 99 99 99 99 99 99 99 100 100 100 100 100 101 102 102 102 102 102 102 103 103 103 106 106 107 107 107 107 107 107 108 109 110 111 112 ]);
M_.dynamic_g2_sparse_indices = int32([1 38 45 ;
2 8 8 ;
2 38 38 ;
3 9 9 ;
3 39 39 ;
4 10 10 ;
4 40 40 ;
5 11 11 ;
5 41 41 ;
6 12 12 ;
6 42 42 ;
7 40 40 ;
7 40 46 ;
7 40 48 ;
7 40 53 ;
7 46 46 ;
7 46 48 ;
7 46 53 ;
7 48 48 ;
7 48 53 ;
8 37 49 ;
8 37 53 ;
8 49 53 ;
9 36 36 ;
9 36 48 ;
9 40 46 ;
10 36 83 ;
10 36 85 ;
10 83 85 ;
10 85 85 ;
11 38 38 ;
11 38 53 ;
11 68 68 ;
11 68 83 ;
11 14 14 ;
11 14 44 ;
11 14 53 ;
11 44 44 ;
11 44 74 ;
11 44 53 ;
11 44 83 ;
11 74 74 ;
11 74 83 ;
11 80 83 ;
12 43 55 ;
12 14 14 ;
12 14 44 ;
12 44 44 ;
12 55 55 ;
13 37 49 ;
13 43 55 ;
13 14 50 ;
13 55 55 ;
14 37 49 ;
14 37 53 ;
14 43 54 ;
14 49 53 ;
15 43 54 ;
15 14 50 ;
15 14 53 ;
15 50 53 ;
16 43 43 ;
16 43 73 ;
16 43 83 ;
16 43 55 ;
16 73 83 ;
16 73 55 ;
16 53 55 ;
16 83 55 ;
16 55 55 ;
17 37 37 ;
17 37 41 ;
17 37 14 ;
17 41 41 ;
17 41 14 ;
17 14 14 ;
18 36 36 ;
18 42 42 ;
18 43 43 ;
18 52 52 ;
18 55 55 ;
19 37 37 ;
19 37 43 ;
20 18 18 ;
20 18 48 ;
20 18 55 ;
20 48 55 ;
21 46 46 ;
22 48 48 ;
23 45 45 ;
24 55 55 ;
25 36 36 ;
]);
M_.lhs = {
'k'; 
'log(x)'; 
'log(a)'; 
'log(e)'; 
'log(z)'; 
'log(v)'; 
'a'; 
'lambd*w*(1-h)'; 
'e*c'; 
'lambd'; 
'lambd*(1/x+phik*(k/k(-1)-1))'; 
'y'; 
'd'; 
'lambd*w*h'; 
'k(-1)*lambd*q'; 
'pi/(pi)*(pi/(pi)-1)*lambd*phip'; 
'y'; 
'log(r/(r))'; 
'n'; 
'tau'; 
'Dataslcobs'; 
'Dataslmobs'; 
'Dataslinobs'; 
'Dataslpiobs'; 
'Dataslrobs'; 
'din'; 
'dc'; 
'dpi'; 
'dr'; 
'dm'; 
};
M_.static_tmp_nbr = [14; 3; 0; 0; ];
M_.block_structure_stat.block(1).Simulation_Type = 3;
M_.block_structure_stat.block(1).endo_nbr = 1;
M_.block_structure_stat.block(1).mfs = 1;
M_.block_structure_stat.block(1).equation = [ 2];
M_.block_structure_stat.block(1).variable = [ 8];
M_.block_structure_stat.block(2).Simulation_Type = 3;
M_.block_structure_stat.block(2).endo_nbr = 1;
M_.block_structure_stat.block(2).mfs = 1;
M_.block_structure_stat.block(2).equation = [ 3];
M_.block_structure_stat.block(2).variable = [ 9];
M_.block_structure_stat.block(3).Simulation_Type = 3;
M_.block_structure_stat.block(3).endo_nbr = 1;
M_.block_structure_stat.block(3).mfs = 1;
M_.block_structure_stat.block(3).equation = [ 4];
M_.block_structure_stat.block(3).variable = [ 10];
M_.block_structure_stat.block(4).Simulation_Type = 3;
M_.block_structure_stat.block(4).endo_nbr = 1;
M_.block_structure_stat.block(4).mfs = 1;
M_.block_structure_stat.block(4).equation = [ 5];
M_.block_structure_stat.block(4).variable = [ 11];
M_.block_structure_stat.block(5).Simulation_Type = 3;
M_.block_structure_stat.block(5).endo_nbr = 1;
M_.block_structure_stat.block(5).mfs = 1;
M_.block_structure_stat.block(5).equation = [ 6];
M_.block_structure_stat.block(5).variable = [ 12];
M_.block_structure_stat.block(6).Simulation_Type = 1;
M_.block_structure_stat.block(6).endo_nbr = 3;
M_.block_structure_stat.block(6).mfs = 3;
M_.block_structure_stat.block(6).equation = [ 26 27 30];
M_.block_structure_stat.block(6).variable = [ 5 1 3];
M_.block_structure_stat.block(7).Simulation_Type = 6;
M_.block_structure_stat.block(7).endo_nbr = 13;
M_.block_structure_stat.block(7).mfs = 13;
M_.block_structure_stat.block(7).equation = [ 9 10 11 12 8 14 15 16 17 18 7 20 1];
M_.block_structure_stat.block(7).variable = [ 18 6 20 16 19 7 24 25 14 13 23 22 15];
M_.block_structure_stat.block(8).Simulation_Type = 1;
M_.block_structure_stat.block(8).endo_nbr = 9;
M_.block_structure_stat.block(8).mfs = 9;
M_.block_structure_stat.block(8).equation = [ 25 28 29 24 23 22 21 19 13];
M_.block_structure_stat.block(8).variable = [ 26 4 2 27 29 28 30 21 17];
M_.block_structure_stat.variable_reordered = [ 8 9 10 11 12 5 1 3 18 6 20 16 19 7 24 25 14 13 23 22 15 26 4 2 27 29 28 30 21 17];
M_.block_structure_stat.equation_reordered = [ 2 3 4 5 6 26 27 30 9 10 11 12 8 14 15 16 17 18 7 20 1 25 28 29 24 23 22 21 19 13];
M_.block_structure_stat.incidence.sparse_IM = [
 1 8;
 1 14;
 1 15;
 2 8;
 3 9;
 4 10;
 5 11;
 6 12;
 7 9;
 7 10;
 7 16;
 7 18;
 7 23;
 8 7;
 8 19;
 8 23;
 9 6;
 9 10;
 9 16;
 9 18;
 10 6;
 10 23;
 10 25;
 11 8;
 11 20;
 11 23;
 12 13;
 12 15;
 12 16;
 12 25;
 13 7;
 13 13;
 13 14;
 13 17;
 13 19;
 13 20;
 13 25;
 14 7;
 14 13;
 14 19;
 14 23;
 14 24;
 15 13;
 15 14;
 15 20;
 15 23;
 15 24;
 16 23;
 16 24;
 16 25;
 17 7;
 17 11;
 17 13;
 17 14;
 18 6;
 18 12;
 18 13;
 18 22;
 18 25;
 19 7;
 19 13;
 19 21;
 20 22;
 20 25;
 21 16;
 21 30;
 22 18;
 22 28;
 23 15;
 23 29;
 24 25;
 24 27;
 25 6;
 25 26;
 26 5;
 27 1;
 28 4;
 28 25;
 29 2;
 29 6;
 30 3;
];
M_.block_structure_stat.tmp_nbr = 13;
M_.block_structure_stat.block(1).g1_sparse_rowval = int32([1 ]);
M_.block_structure_stat.block(1).g1_sparse_colval = int32([1 ]);
M_.block_structure_stat.block(1).g1_sparse_colptr = int32([1 2 ]);
M_.block_structure_stat.block(2).g1_sparse_rowval = int32([1 ]);
M_.block_structure_stat.block(2).g1_sparse_colval = int32([1 ]);
M_.block_structure_stat.block(2).g1_sparse_colptr = int32([1 2 ]);
M_.block_structure_stat.block(3).g1_sparse_rowval = int32([1 ]);
M_.block_structure_stat.block(3).g1_sparse_colval = int32([1 ]);
M_.block_structure_stat.block(3).g1_sparse_colptr = int32([1 2 ]);
M_.block_structure_stat.block(4).g1_sparse_rowval = int32([1 ]);
M_.block_structure_stat.block(4).g1_sparse_colval = int32([1 ]);
M_.block_structure_stat.block(4).g1_sparse_colptr = int32([1 2 ]);
M_.block_structure_stat.block(5).g1_sparse_rowval = int32([1 ]);
M_.block_structure_stat.block(5).g1_sparse_colval = int32([1 ]);
M_.block_structure_stat.block(5).g1_sparse_colptr = int32([1 2 ]);
M_.block_structure_stat.block(6).g1_sparse_rowval = int32([]);
M_.block_structure_stat.block(6).g1_sparse_colval = int32([]);
M_.block_structure_stat.block(6).g1_sparse_colptr = int32([]);
M_.block_structure_stat.block(7).g1_sparse_rowval = int32([1 11 1 2 10 3 7 1 4 11 5 6 5 6 9 6 7 8 2 4 8 10 12 7 9 13 4 6 7 9 10 2 3 5 6 7 8 11 10 12 4 13 ]);
M_.block_structure_stat.block(7).g1_sparse_colval = int32([1 1 2 2 2 3 3 4 4 4 5 5 6 6 6 7 7 7 8 8 8 8 8 9 9 9 10 10 10 10 10 11 11 11 11 11 11 11 12 12 13 13 ]);
M_.block_structure_stat.block(7).g1_sparse_colptr = int32([1 3 6 8 11 13 16 19 24 27 32 39 41 43 ]);
M_.block_structure_stat.block(8).g1_sparse_rowval = int32([]);
M_.block_structure_stat.block(8).g1_sparse_colval = int32([]);
M_.block_structure_stat.block(8).g1_sparse_colptr = int32([]);
M_.static_g1_sparse_rowval = int32([27 29 30 28 26 9 10 18 25 29 8 13 14 17 19 1 2 11 3 7 4 7 9 5 17 6 18 12 13 14 15 17 18 19 1 13 15 17 1 12 23 7 9 12 21 13 7 9 22 8 13 14 11 13 15 19 18 20 7 8 10 11 14 15 16 14 15 16 10 12 13 16 18 20 24 28 25 24 22 23 21 ]);
M_.static_g1_sparse_colval = int32([1 2 3 4 5 6 6 6 6 6 7 7 7 7 7 8 8 8 9 9 10 10 10 11 11 12 12 13 13 13 13 13 13 13 14 14 14 14 15 15 15 16 16 16 16 17 18 18 18 19 19 19 20 20 20 21 22 22 23 23 23 23 23 23 23 24 24 24 25 25 25 25 25 25 25 25 26 27 28 29 30 ]);
M_.static_g1_sparse_colptr = int32([1 2 3 4 5 6 11 16 19 21 24 26 28 35 39 42 46 47 50 53 56 57 59 66 69 77 78 79 80 81 82 ]);
M_.params(1) = 0.75;
rhoa = M_.params(1);
M_.params(2) = 0.75;
rhoe = M_.params(2);
M_.params(3) = 0.75;
rhox = M_.params(3);
M_.params(4) = 0.75;
rhoz = M_.params(4);
M_.params(5) = 0.50;
rhov = M_.params(5);
M_.params(6) = 0.3;
alph = M_.params(6);
M_.params(7) = 50;
phip = M_.params(7);
M_.params(8) = 32.14;
phik = M_.params(8);
M_.params(11) = 0.01;
sigmaa = M_.params(11);
M_.params(12) = 0.01;
sigmae = M_.params(12);
M_.params(13) = 0.01;
sigmax = M_.params(13);
M_.params(14) = 0.01;
sigmaz = M_.params(14);
M_.params(15) = 0.01;
sigmav = M_.params(15);
M_.params(16) = 1.3;
omegapi = M_.params(16);
M_.params(17) = 0.125;
omegay = M_.params(17);
M_.params(18) = 0.38;
omegatau = M_.params(18);
M_.params(19) = 0.99;
bet = M_.params(19);
M_.params(20) = 1.5;
et = M_.params(20);
M_.params(21) = 0.025;
delt = M_.params(21);
M_.params(22) = 6;
thet = M_.params(22);
M_.params(23) = 0.042;
gamm = M_.params(23);
M_.params(24) = 0;
ctrend = M_.params(24);
M_.params(25) = 0;
constepi = M_.params(25);
M_.params(26) = 0;
conster = M_.params(26);
M_.params(24) = 0;
ctrend = M_.params(24);
M_.params(27) = 0;
intrend = M_.params(27);
M_.params(28) = 0;
mtrend = M_.params(28);
steady;
options_resid_ = struct();
display_static_residuals(M_, options_, oo_, options_resid_);
if isempty(estim_params_)
    estim_params_.var_exo = zeros(0, 10);
    estim_params_.var_endo = zeros(0, 10);
    estim_params_.corrx = zeros(0, 11);
    estim_params_.corrn = zeros(0, 11);
    estim_params_.param_vals = zeros(0, 10);
end
if ~isempty(find(estim_params_.param_vals(:,1)==23))
    error('Parameter gamm has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.param_vals = [estim_params_.param_vals; 23, NaN, (-Inf), Inf, 5, NaN, NaN, 0, 0.1, NaN ];
if ~isempty(find(estim_params_.param_vals(:,1)==7))
    error('Parameter phip has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.param_vals = [estim_params_.param_vals; 7, NaN, (-Inf), Inf, 2, 50, 10, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.param_vals(:,1)==8))
    error('Parameter phik has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.param_vals = [estim_params_.param_vals; 8, NaN, (-Inf), Inf, 2, 32.14, 2.8, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.param_vals(:,1)==1))
    error('Parameter rhoa has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.param_vals = [estim_params_.param_vals; 1, NaN, (-Inf), Inf, 1, 0.75, 0.15, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.param_vals(:,1)==2))
    error('Parameter rhoe has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.param_vals = [estim_params_.param_vals; 2, NaN, (-Inf), Inf, 1, 0.75, 0.15, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.param_vals(:,1)==3))
    error('Parameter rhox has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.param_vals = [estim_params_.param_vals; 3, NaN, (-Inf), Inf, 1, 0.75, 0.15, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.param_vals(:,1)==4))
    error('Parameter rhoz has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.param_vals = [estim_params_.param_vals; 4, NaN, (-Inf), Inf, 1, 0.75, 0.15, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.param_vals(:,1)==5))
    error('Parameter rhov has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.param_vals = [estim_params_.param_vals; 5, NaN, (-Inf), Inf, 1, 0.5, 0.1, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.param_vals(:,1)==16))
    error('Parameter omegapi has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.param_vals = [estim_params_.param_vals; 16, NaN, (-Inf), Inf, 3, 1.3, 0.3, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.param_vals(:,1)==17))
    error('Parameter omegay has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.param_vals = [estim_params_.param_vals; 17, NaN, (-Inf), Inf, 3, 0.125, 0.125, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.param_vals(:,1)==18))
    error('Parameter omegatau has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.param_vals = [estim_params_.param_vals; 18, NaN, (-Inf), Inf, 3, 0.38, 0.07, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.var_exo(:,1)==1))
    error('The standard deviation for epsilona has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.var_exo = [estim_params_.var_exo; 1, NaN, (-Inf), Inf, 4, 0.01, 0.5, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.var_exo(:,1)==2))
    error('The standard deviation for epsilone has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.var_exo = [estim_params_.var_exo; 2, NaN, (-Inf), Inf, 4, 0.01, 0.5, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.var_exo(:,1)==3))
    error('The standard deviation for epsilonx has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.var_exo = [estim_params_.var_exo; 3, NaN, (-Inf), Inf, 4, 0.01, 0.5, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.var_exo(:,1)==4))
    error('The standard deviation for epsilonz has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.var_exo = [estim_params_.var_exo; 4, NaN, (-Inf), Inf, 4, 0.01, 0.5, NaN, NaN, NaN ];
if ~isempty(find(estim_params_.var_exo(:,1)==5))
    error('The standard deviation for epsilonv has been specified twice in two concatenated ''estimated_params'' blocks. Depending on your intention, you may want to use the ''overwrite'' option or an ''estimated_params_remove'' block.')
end
estim_params_.var_exo = [estim_params_.var_exo; 5, NaN, (-Inf), Inf, 4, 0.01, 0.5, NaN, NaN, NaN ];
%
% SHOCKS instructions
%
M_.exo_det_length = 0;
M_.Sigma_e(1, 1) = M_.params(11)^2;
M_.Sigma_e(2, 2) = M_.params(12)^2;
M_.Sigma_e(3, 3) = M_.params(13)^2;
M_.Sigma_e(4, 4) = M_.params(14)^2;
M_.Sigma_e(5, 5) = M_.params(15)^2;
options_.graph_format = {'fig'};
options_.order = 2;
var_list_ = {};
[info, oo_, options_, M_] = stoch_simul(M_, options_, oo_, var_list_);
options_.datafile = 'Slovenia';
options_.first_obs = 12;
options_.lik_init = 2;
options_.mh_drop = 0.2;
options_.mh_jscale = 1.08;
options_.mh_nblck = 2;
options_.mh_replic = 250000;
options_.mode_check.status = true;
options_.mode_compute = 6;
options_.optim_opt = '''MaxIter'',200';
options_.prefilter = 0;
options_.presample = 4;
options_.prior_trunc = 0;
options_.xls_range = 'C1:G118';
options_.order = 1;
var_list_ = {};
oo_recursive_=dynare_estimation(var_list_);
temp=load("data1.mat");
Slovenia=[temp.Dataslrobs temp.Dataslpiobs temp.Dataslmobs temp.Dataslinobs temp.Dataslcobs];
parameter_draw=reestimate(data1);
[~, estim_params_, bayestopt_, lb, ub, M_]=set_prior(estim_params_, M_, options_);
prior_draw(bayestopt_,0);
N=2;
for par_iter=1:N
xparam1=prior_draw;
M_ = set_all_parameters(xparam1,estim_params_,M_);
options_.cova_compute = 0;
options_.datafile = 'Slovenia';
options_.logdata = true;
options_.loglinear = true;
options_.mh_replic = 0;
options_.mode_compute = 5;
options_.nodiagnostic = true;
options_.nograph = true;
options_.order = 1;
options_.plot_priors = 0;
var_list_ = {};
oo_recursive_=dynare_estimation(var_list_);
temp=load([M_.dname filesep 'Output' filesep M_.fname '_mode.mat'],'xparam1')
mode_parameter(:,par_iter)=temp.xparam1;
end
save mode_parameter_results mode_parameter;


oo_.time = toc(tic0);
disp(['Total computing time : ' dynsec2hms(oo_.time) ]);
if ~exist([M_.dname filesep 'Output'],'dir')
    mkdir(M_.dname,'Output');
end
save([M_.dname filesep 'Output' filesep 'Model10_results.mat'], 'oo_', 'M_', 'options_');
if exist('estim_params_', 'var') == 1
  save([M_.dname filesep 'Output' filesep 'Model10_results.mat'], 'estim_params_', '-append');
end
if exist('bayestopt_', 'var') == 1
  save([M_.dname filesep 'Output' filesep 'Model10_results.mat'], 'bayestopt_', '-append');
end
if exist('dataset_', 'var') == 1
  save([M_.dname filesep 'Output' filesep 'Model10_results.mat'], 'dataset_', '-append');
end
if exist('estimation_info', 'var') == 1
  save([M_.dname filesep 'Output' filesep 'Model10_results.mat'], 'estimation_info', '-append');
end
if exist('dataset_info', 'var') == 1
  save([M_.dname filesep 'Output' filesep 'Model10_results.mat'], 'dataset_info', '-append');
end
if exist('oo_recursive_', 'var') == 1
  save([M_.dname filesep 'Output' filesep 'Model10_results.mat'], 'oo_recursive_', '-append');
end
if exist('options_mom_', 'var') == 1
  save([M_.dname filesep 'Output' filesep 'Model10_results.mat'], 'options_mom_', '-append');
end
disp('Note: 3 warning(s) encountered in the preprocessor')
if ~isempty(lastwarn)
  disp('Note: warning(s) encountered in MATLAB/Octave code')
end
