792 lines
26 KiB
Matlab
792 lines
26 KiB
Matlab
function options_ = default_option_values(M_)
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%function default_option_values()
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% Returns structure containing the options for Dynare commands and their
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% default values
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%
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% INPUTS
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% M_ [structure] Model definition
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%
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% OUTPUTS
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% options [structure] Command options
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright © 2018-2023 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 <https://www.gnu.org/licenses/>.
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options_.datafile = '';
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options_.dirname = M_.fname;
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options_.dataset = [];
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options_.verbosity = 1;
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options_.rplottype = 0;
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options_.smpl = 0;
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options_.dynatol.f = 1e-5;
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options_.dynatol.x = 1e-5;
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options_.timing = 0;
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options_.gstep = ones(2,1);
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options_.gstep(1) = 1e-2;
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options_.gstep(2) = 1.0;
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options_.jacobian_tolerance = []; %tolerance for rank of Jacobian in model_diagnostics
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options_.debug = false;
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options_.initval_file = false;
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options_.schur_vec_tol = 1e-11; % used to find nonstationary variables in Schur decomposition of the
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% transition matrix
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options_.qz_criterium = [];
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options_.qz_zero_threshold = 1e-6;
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options_.lyapunov_complex_threshold = 1e-15;
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options_.solve_algo = 4;
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options_.solve_tolf = eps^(1/3);
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options_.solve_tolx = eps^(2/3);
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options_.solve_randomize_initial_guess = true;
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options_.trust_region_initial_step_bound_factor = 1;
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options_.dr_display_tol=1e-6;
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options_.dp.maxit = 3000;
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options_.steady.maxit = 50;
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options_.simul.maxit = 50;
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options_.simul.robust_lin_solve = false;
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options_.mode_check.status = false;
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options_.mode_check.neighbourhood_size = .5;
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options_.mode_check.symmetric_plots = true;
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options_.mode_check.number_of_points = 20;
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options_.mode_check.nolik = false;
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options_.huge_number = 1e7;
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options_.add_tiny_number_to_cholesky=1e-14;
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% Default number of threads for parallelized mex files.
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options_.threads.kronecker.sparse_hessian_times_B_kronecker_C = num_procs;
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options_.threads.local_state_space_iteration_2 = num_procs;
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options_.threads.local_state_space_iteration_3 = num_procs;
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options_.threads.local_state_space_iteration_k = 1;
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options_.threads.perfect_foresight_problem = num_procs;
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options_.threads.k_order_perturbation = max(1, num_procs/2);
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% steady state
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options_.jacobian_flag = true;
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% steady state file
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if exist(['+' M_.fname '/steadystate.m'],'file')
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options_.steadystate_flag = 2;
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elseif exist([M_.fname '_steadystate.m'],'file')
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options_.steadystate_flag = 1;
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else
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options_.steadystate_flag = 0;
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end
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options_.steadystate.nocheck = false;
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% subset of the estimated deep parameters
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options_.ParamSubSet = 'None';
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% bvar-dsge
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options_.dsge_var = 0;
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options_.dsge_varlag = 4;
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% BVAR a la Sims
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options_.bvar_replic = 2000;
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options_.bvar_prior_tau = 3;
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options_.bvar_prior_decay = 0.5;
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options_.bvar_prior_lambda = 5;
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options_.bvar_prior_mu = 2;
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options_.bvar_prior_omega = 1;
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options_.bvar_prior_flat = false;
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options_.bvar_prior_train = 0;
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options_.bvar.conf_sig = 0.6;
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% Initialize the field that will contain the optimization algorithm's options declared in the
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% estimation command (if any).
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options_.optim_opt = [];
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% Same for options to fsolve
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options_.fsolve_options = [];
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% Optimization algorithm [6] gmhmaxlik
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gmhmaxlik.iterations = 3;
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gmhmaxlik.number = 20000;
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gmhmaxlik.nclimb = 200000;
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gmhmaxlik.nscale = 200000;
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gmhmaxlik.target = 1/3; % Target for the acceptance rate.
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options_.gmhmaxlik = gmhmaxlik;
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% Request user input.
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options_.nointeractive = false;
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% Graphics
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options_.graphics.nrows = 3;
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options_.graphics.ncols = 3;
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options_.graphics.line_types = {'b-'};
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options_.graphics.line_width = 1;
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options_.graph_format = 'eps';
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options_.nodisplay = false;
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options_.nograph = false;
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options_.no_graph.posterior = false;
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options_.no_graph.shock_decomposition = false;
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options_.no_graph.plot_shock_decomposition = false;
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options_.XTick = [];
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options_.XTickLabel = [];
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options_.console_mode = false;
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if isoctave
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if sum(get(0,'screensize'))==4
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options_.console_mode = true;
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options_.nodisplay = true;
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end
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else
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if isunix && (~usejava('jvm') || ~feature('ShowFigureWindows'))
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options_.console_mode = true;
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options_.nodisplay = true;
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end
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end
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% IRFs & other stoch_simul output
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options_.irf = 40;
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options_.impulse_responses.plot_threshold=1e-10;
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options_.relative_irf = false;
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options_.ar = 5;
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options_.hp_filter = 0;
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options_.one_sided_hp_filter = 0;
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options_.filtered_theoretical_moments_grid = 512;
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options_.nodecomposition = false;
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options_.nomoments = false;
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options_.nomodelsummary = false;
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options_.nocorr = false;
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options_.periods = 0;
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options_.noprint = false;
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options_.SpectralDensity.trigger = false;
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options_.SpectralDensity.plot = 1;
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options_.SpectralDensity.cutoff = 150;
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options_.SpectralDensity.sdl = 0.01;
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options_.nofunctions = false;
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options_.bandpass.indicator = false;
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options_.bandpass.passband = [6; 32];
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options_.bandpass.K=12;
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options_.irf_opt.diagonal_only = false;
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options_.irf_opt.stderr_multiples = false;
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options_.irf_opt.irf_shock_graphtitles = {};
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options_.irf_opt.irf_shocks = [];
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% Extended path options
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%
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% Set debug flag
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ep.debug = 0;
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% Set memory flag
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ep.memory = 0;
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% Set verbose mode
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ep.verbosity = 0;
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% Set bytecode flag
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ep.use_bytecode = 0;
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% Initialization of the perfect foresight equilibrium paths
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% * init=0, previous solution is used.
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% * init=1, a path generated with the first order reduced form is used.
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% * init=2, mix of cases 0 and 1.
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ep.init = 0;
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% Maximum number of iterations for the deterministic solver.
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ep.maxit = 500;
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% Number of periods for the perfect foresight model.
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ep.periods = 200;
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% Default step for increasing the number of periods if needed
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ep.step = 50;
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% Set check_stability flag
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ep.check_stability = 0;
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% Define last periods used to test if the solution is stable with respect to an increase in the number of periods.
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ep.lp = 5;
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% Define first periods used to test if the solution is stable with respect to an increase in the number of periods.
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ep.fp = 2;
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% Define the distribution for the structural innovations.
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ep.innovation_distribution = 'gaussian';
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% Set flag for the seed
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ep.set_dynare_seed_to_default = 1;
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% Set algorithm for the perfect foresight solver
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ep.stack_solve_algo = 7;
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ep.solve_algo = 9;
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% Number of replications
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ep.replic_nbr = 1;
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% Parallel execution of replications
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ep.parallel = false;
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% Stochastic extended path related options.
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ep.stochastic.IntegrationAlgorithm = 'Tensor-Gaussian-Quadrature'; % Other possible values are 'Stroud-Cubature-3' and 'Stroud-Cubature-5'
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ep.stochastic.method = '';
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ep.stochastic.algo = 0;
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ep.stochastic.quadrature.ortpol = 'hermite';
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ep.stochastic.order = 0;
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ep.stochastic.quadrature.nodes = 5;
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ep.stochastic.quadrature.pruned.status = 0;
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ep.stochastic.quadrature.pruned.relative = 1e-5;
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ep.stochastic.quadrature.pruned.level = 1e-5;
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ep.stochastic.hybrid_order = 0;
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% homotopic step in extended path simulations
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ep.stochastic.homotopic_steps = true;
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% Copy ep structure in options_ global structure
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options_.ep = ep;
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% Simulations of backward looking models options
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%
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bnlms.set_dynare_seed_to_default = true;
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bnlms.innovation_distribution = 'gaussian';
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options_.bnlms = bnlms;
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% Particle filter
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%
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% Default is that we do not use the non linear kalman filter
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particle.status = false;
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% How do we initialize the states?
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particle.initialization = 1;
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particle.initial_state_prior_std = .1;
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% Set the default number of particles.
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particle.number_of_particles = 5000;
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% Set the default approximation order (Smolyak)
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particle.smolyak_accuracy = 3;
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% By default we don't use pruning
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particle.pruning = false;
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% Set the Gaussian approximation method for particles distributions
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particle.approximation_method = 'unscented';
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% Set unscented parameters alpha, beta and kappa for gaussian approximation
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particle.unscented.alpha = 1;
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particle.unscented.beta = 2;
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particle.unscented.kappa = 1;
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% Configuration of resampling in case of particles
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particle.resampling.status.systematic = true;
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particle.resampling.status.none = false;
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particle.resampling.status.generic = false;
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particle.resampling.threshold = .5;
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particle.resampling.method.kitagawa = true;
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particle.resampling.method.smooth = false;
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particle.resampling.method.stratified = false;
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% Set default algorithm
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particle.filter_algorithm = 'sis';
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% Approximation of the proposal distribution
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particle.proposal_approximation.cubature = false;
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particle.proposal_approximation.unscented = true;
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particle.proposal_approximation.montecarlo = false;
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% Approximation of the particle distribution
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particle.distribution_approximation.cubature = false;
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particle.distribution_approximation.unscented = true;
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particle.distribution_approximation.montecarlo = false;
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% Number of partitions for the smoothed resampling method
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particle.resampling.number_of_partitions = 200;
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% Configuration of the mixture filters
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%particle.mixture_method = 'particles' ;
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% Size of the different mixtures
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particle.mixture_state_variables = 5 ;
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particle.mixture_structural_shocks = 1 ;
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particle.mixture_measurement_shocks = 1 ;
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% Online approach
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particle.liu_west_delta = 0.99 ;
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particle.liu_west_max_resampling_tries = 5000;
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% Options for setting the weights in conditional particle filters.
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particle.cpf_weights_method.amisanotristani = true;
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particle.cpf_weights_method.murrayjonesparslow = false;
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particle.particle_filter_options ='';
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% Copy particle structure in options_ global structure
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options_.particle = particle;
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options_.rwgmh.init_scale = 1e-4 ;
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options_.rwgmh.scale_chain = 1 ;
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options_.rwgmh.scale_shock = 1e-5 ;
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% TeX output
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options_.TeX = false;
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% Exel
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options_.xls_sheet = 1; % Octave does not support the empty string, rather use first sheet
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options_.xls_range = '';
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% Prior draws
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options_.prior_draws = 10000;
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% Prior posterior function sampling draws
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options_.sampling_draws = 500;
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options_.forecast = 0;
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options_.forecasts.conf_sig = 0.9;
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options_.conditional_forecast.conf_sig = 0.9;
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% Model
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options_.linear = false;
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% Deterministic simulation
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options_.stack_solve_algo = 0;
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options_.markowitz = 0.5;
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options_.minimal_solving_periods = 1;
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options_.endogenous_terminal_period = false;
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options_.no_homotopy = false;
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options_.simul.endval_steady = false;
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options_.simul.homotopy_max_completion_share = 1;
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options_.simul.homotopy_min_step_size = 1e-3;
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options_.simul.homotopy_step_size_increase_success_count = 3;
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options_.simul.homotopy_initial_step_size = 1;
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options_.simul.homotopy_linearization_fallback = false;
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options_.simul.homotopy_marginal_linearization_fallback = 0; % Size of the step used for the marginal linearization; 0 means disabled
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% Options used by perfect_foresight_* commands when they compute the steady
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% state corresponding to a terminal condition
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options_.simul.steady_solve_algo = options_.solve_algo;
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options_.simul.steady_maxit = options_.steady.maxit;
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options_.simul.steady_tolf = options_.solve_tolf;
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options_.simul.steady_tolx = options_.solve_tolx;
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options_.simul.steady_markowitz = options_.markowitz;
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% Perfect foresight with expectation errors
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options_.pfwee.constant_simulation_length = false;
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% Solution
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options_.order = 2;
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options_.pruning = false;
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options_.replic = 50;
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options_.simul_replic = 1;
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options_.drop = 100;
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options_.aim_solver = false; % i.e. by default do not use G.Anderson's AIM solver, use mjdgges instead
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options_.k_order_solver = false; % by default do not use k_order_perturbation but mjdgges
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options_.partial_information = false;
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options_.ACES_solver = false;
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options_.conditional_variance_decomposition = [];
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% Ramsey policy
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options_.ramsey_policy = false;
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options_.instruments = {};
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options_.timeless = 0;
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options_.ramsey.maxit = 500;
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options_.ramsey.periods = 10000;
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options_.ramsey.drop = 1000;
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% estimation
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options_.initial_period = NaN; %dates(1,1);
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options_.no_init_estimation_check_first_obs=false;
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options_.dataset.file = [];
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options_.dataset.series = [];
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options_.dataset.firstobs = dates();
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options_.dataset.lastobs = dates();
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options_.dataset.nobs = NaN;
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options_.dataset.xls_sheet = [];
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options_.dataset.xls_range = [];
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options_.Harvey_scale_factor = 10;
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options_.heteroskedastic_filter = false;
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options_.MaxNumberOfBytes = 1e8;
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options_.MaximumNumberOfMegaBytes = 111;
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options_.analytic_derivation = 0; % Not a boolean, can also take values -1 or 2
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options_.analytic_derivation_mode = 0;
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options_.bayesian_irf = false;
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options_.bayesian_th_moments = 0;
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options_.diffuse_filter = false;
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options_.filter_step_ahead = [];
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options_.filtered_vars = false;
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options_.smoothed_state_uncertainty = false;
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options_.first_obs = NaN;
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options_.nobs = NaN;
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options_.kalman_algo = 0;
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options_.kalman_filter_mex = false;
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options_.fast_kalman_filter = false;
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options_.kalman_tol = 1e-10;
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options_.kalman.keep_kalman_algo_if_singularity_is_detected = false;
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options_.diffuse_kalman_tol = 1e-6;
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options_.use_univariate_filters_if_singularity_is_detected = 1;
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options_.riccati_tol = 1e-6;
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options_.lik_init = 1;
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options_.load_mh_file = false;
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options_.load_results_after_load_mh = false;
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options_.logdata = false;
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options_.loglinear = false;
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options_.linear_approximation = false;
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options_.logged_steady_state = 0;
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options_.mh_conf_sig = 0.90;
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options_.prior_interval = 0.90;
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options_.mh_drop = 0.5;
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options_.mh_jscale = [];
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options_.mh_tune_jscale.status = false;
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options_.mh_tune_jscale.guess = [];
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options_.mh_tune_jscale.target = .33;
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options_.mh_tune_jscale.maxiter = 200000;
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options_.mh_tune_jscale.rho = .7;
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options_.mh_tune_jscale.stepsize = 1000;
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options_.mh_tune_jscale.c1 = .02;
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options_.mh_tune_jscale.c2 = .05;
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options_.mh_tune_jscale.c3 = 4;
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options_.mh_init_scale_factor = 2;
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options_.mh_initialize_from_previous_mcmc.status = false;
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options_.mh_initialize_from_previous_mcmc.directory = '';
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options_.mh_initialize_from_previous_mcmc.record = '';
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options_.mh_initialize_from_previous_mcmc.prior = '';
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options_.mh_nblck = 2;
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options_.mh_recover = false;
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options_.mh_replic = 20000;
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options_.recursive_estimation_restart = 0;
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options_.MCMC_jumping_covariance='hessian';
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options_.use_calibration_initialization = 0;
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options_.endo_vars_for_moment_computations_in_estimation=[];
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% occbin options
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options_.occbin.likelihood.status=false;
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options_.occbin.smoother.status=false;
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% Run optimizer silently
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options_.silent_optimizer = false;
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% Prior restrictions
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options_.prior_restrictions.status = 0;
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options_.prior_restrictions.routine = [];
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options_.mode_compute = 5;
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options_.additional_optimizer_steps= [];
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options_.mode_file = '';
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options_.moments_varendo = false;
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options_.nk = 1;
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options_.noconstant = false;
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options_.nodiagnostic = false;
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options_.mh_posterior_mode_estimation = false;
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options_.smc_posterior_mode_estimation = false;
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options_.prefilter = 0;
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options_.presample = 0;
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options_.prior_trunc = 1e-10;
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options_.smoother = false;
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options_.smoother_redux = false;
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options_.posterior_max_subsample_draws = 1200;
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options_.sub_draws = [];
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options_.ME_plot_tol=1e-6;
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options_.use_mh_covariance_matrix = false;
|
|
options_.gradient_method = 2; %used by csminwel and newrat
|
|
options_.gradient_epsilon = 1e-6; %used by csminwel and newrat
|
|
options_.posterior_sampler_options.sampling_opt = []; %extended set of options for individual posterior samplers
|
|
% Random Walk Metropolis-Hastings
|
|
options_.posterior_sampler_options.posterior_sampling_method = 'random_walk_metropolis_hastings';
|
|
options_.posterior_sampler_options.rwmh.proposal_distribution = 'rand_multivariate_normal';
|
|
options_.posterior_sampler_options.rwmh.student_degrees_of_freedom = 3;
|
|
options_.posterior_sampler_options.rwmh.use_mh_covariance_matrix=0;
|
|
options_.posterior_sampler_options.rwmh.save_tmp_file=0;
|
|
% Tailored Random Block Metropolis-Hastings
|
|
options_.posterior_sampler_options.tarb.proposal_distribution = 'rand_multivariate_normal';
|
|
options_.posterior_sampler_options.tarb.student_degrees_of_freedom = 3;
|
|
options_.posterior_sampler_options.tarb.mode_compute=4;
|
|
options_.posterior_sampler_options.tarb.new_block_probability=0.25; %probability that next parameter belongs to new block
|
|
options_.posterior_sampler_options.tarb.optim_opt=''; %probability that next parameter belongs to new block
|
|
options_.posterior_sampler_options.tarb.save_tmp_file=1;
|
|
% Slice
|
|
options_.posterior_sampler_options.slice.proposal_distribution = '';
|
|
options_.posterior_sampler_options.slice.rotated=0;
|
|
options_.posterior_sampler_options.slice.slice_initialize_with_mode=0;
|
|
options_.posterior_sampler_options.slice.use_mh_covariance_matrix=0;
|
|
options_.posterior_sampler_options.slice.WR=[];
|
|
options_.posterior_sampler_options.slice.mode_files=[];
|
|
options_.posterior_sampler_options.slice.mode=[];
|
|
options_.posterior_sampler_options.slice.initial_step_size=0.8;
|
|
options_.posterior_sampler_options.slice.save_tmp_file=1;
|
|
% Independent Metropolis-Hastings
|
|
options_.posterior_sampler_options.imh.proposal_distribution = 'rand_multivariate_normal';
|
|
options_.posterior_sampler_options.imh.use_mh_covariance_matrix=0;
|
|
options_.posterior_sampler_options.imh.save_tmp_file=0;
|
|
% Herbst and Schorfeide SMC Sampler
|
|
options_.posterior_sampler_options.hssmc.steps= 25;
|
|
options_.posterior_sampler_options.hssmc.lambda = 2;
|
|
options_.posterior_sampler_options.hssmc.particles = 20000;
|
|
options_.posterior_sampler_options.hssmc.scale = 0.5;
|
|
options_.posterior_sampler_options.hssmc.acpt = 1.00;
|
|
options_.posterior_sampler_options.hssmc.target = 0.25;
|
|
% DSMH: Dynamic Striated Metropolis-Hastings algorithm
|
|
options_.posterior_sampler_options.dsmh.H = 25 ;
|
|
options_.posterior_sampler_options.dsmh.N = 20 ;
|
|
options_.posterior_sampler_options.dsmh.G = 10 ;
|
|
options_.posterior_sampler_options.dsmh.K = 50 ;
|
|
options_.posterior_sampler_options.dsmh.lambda1 = 0.1 ;
|
|
options_.posterior_sampler_options.dsmh.particles = 20000 ;
|
|
options_.posterior_sampler_options.dsmh.alpha0 = 0.2 ;
|
|
options_.posterior_sampler_options.dsmh.alpha1 = 0.3 ;
|
|
options_.posterior_sampler_options.dsmh.tau = 10 ;
|
|
|
|
options_.trace_plot_ma = 200;
|
|
options_.mh_autocorrelation_function_size = 30;
|
|
options_.plot_priors = 1;
|
|
options_.cova_compute = 1;
|
|
options_.parallel = 0;
|
|
options_.parallel_info.isHybridMatlabOctave = false;
|
|
options_.parallel_info.leaveSlaveOpen = 0;
|
|
options_.parallel_info.RemoteTmpFolder = '';
|
|
options_.parallel_info.use_psexec = true;
|
|
options_.number_of_grid_points_for_kde = 2^9;
|
|
quarter = 1;
|
|
years = [1 2 3 4 5 10 20 30 40 50];
|
|
options_.conditional_variance_decomposition_dates = zeros(1,length(years));
|
|
for i=1:length(years)
|
|
options_.conditional_variance_decomposition_dates(i) = ...
|
|
(years(i)-1)*4+quarter;
|
|
end
|
|
options_.filter_covariance = false;
|
|
options_.filter_decomposition = false;
|
|
options_.selected_variables_only = false;
|
|
options_.contemporaneous_correlation = false;
|
|
options_.initialize_estimated_parameters_with_the_prior_mode = 0;
|
|
options_.estimation.moments_posterior_density.indicator = true;
|
|
options_.estimation.moments_posterior_density.gridpoints = 2^9;
|
|
options_.estimation.moments_posterior_density.bandwidth = 0; % Rule of thumb optimal bandwidth parameter.
|
|
options_.estimation.moments_posterior_density.kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourrier Transform approximaton.
|
|
% Misc
|
|
% options_.conf_sig = 0.6;
|
|
|
|
% homotopy for steady state
|
|
options_.homotopy_mode = 0;
|
|
options_.homotopy_steps = 10;
|
|
options_.homotopy_force_continue = false;
|
|
|
|
% numerical hessian
|
|
hessian.use_penalized_objective = false;
|
|
|
|
% Robust prediction error covariance (kalman filter)
|
|
options_.rescale_prediction_error_covariance = false;
|
|
|
|
options_.hessian = hessian;
|
|
|
|
%csminwel optimization routine
|
|
csminwel.tolerance.f=1e-7;
|
|
csminwel.maxiter=1000;
|
|
csminwel.verbosity=1;
|
|
csminwel.Save_files=false;
|
|
|
|
options_.csminwel=csminwel;
|
|
|
|
%newrat optimization routine
|
|
newrat.hess=1; % dynare numerical hessian
|
|
newrat.robust=false;
|
|
newrat.tolerance.gstep = NaN;
|
|
newrat.tolerance.gstep_rel = NaN;
|
|
newrat.tolerance.f=1e-5;
|
|
newrat.tolerance.f_analytic=1e-7;
|
|
newrat.maxiter=1000;
|
|
newrat.verbosity=1;
|
|
newrat.Save_files=0;
|
|
|
|
options_.newrat=newrat;
|
|
|
|
% Simplex optimization routine (variation on Nelder Mead algorithm).
|
|
simplex.tolerance.x = 1e-4;
|
|
simplex.tolerance.f = 1e-4;
|
|
simplex.maxiter = 10000;
|
|
simplex.maxfcallfactor = 500;
|
|
simplex.maxfcall = [];
|
|
simplex.verbosity = 2;
|
|
simplex.delta_factor=0.05;
|
|
options_.simplex = simplex;
|
|
|
|
% CMAES optimization routine.
|
|
cmaes.SaveVariables='off';
|
|
cmaes.DispFinal='on';
|
|
cmaes.WarnOnEqualFunctionValues='no';
|
|
cmaes.DispModulo='10';
|
|
cmaes.LogModulo='0';
|
|
cmaes.LogTime='0';
|
|
cmaes.TolFun = 1e-7;
|
|
cmaes.TolX = 1e-7;
|
|
cmaes.Resume = 0;
|
|
options_.cmaes = cmaes;
|
|
|
|
% simpsa optimization routine.
|
|
simpsa.TOLFUN = 1e-4;
|
|
simpsa.TOLX = 1e-4;
|
|
simpsa.TEMP_END = .1;
|
|
simpsa.COOL_RATE = 10;
|
|
simpsa.INITIAL_ACCEPTANCE_RATIO = .95;
|
|
simpsa.MIN_COOLING_FACTOR = .9;
|
|
simpsa.MAX_ITER_TEMP_FIRST = 50;
|
|
simpsa.MAX_ITER_TEMP_LAST = 2000;
|
|
simpsa.MAX_ITER_TEMP = 10;
|
|
simpsa.MAX_ITER_TOTAL = 5000;
|
|
simpsa.MAX_TIME = 2500;
|
|
simpsa.MAX_FUN_EVALS = 20000;
|
|
simpsa.DISPLAY = 'iter';
|
|
options_.simpsa = simpsa;
|
|
|
|
%solveopt optimizer
|
|
solveopt.minimizer_indicator=-1; %use minimizer
|
|
solveopt.TolX=1e-6; %accuracy of argument
|
|
solveopt.TolFun=1e-6; %accuracy of function
|
|
solveopt.MaxIter=15000;
|
|
solveopt.verbosity=1;
|
|
solveopt.TolXConstraint=1.e-8;
|
|
solveopt.SpaceDilation=2.5;
|
|
solveopt.LBGradientStep=1.e-11;
|
|
options_.solveopt=solveopt;
|
|
|
|
%simulated annealing
|
|
options_.saopt.neps=10;
|
|
options_.saopt.maximizer_indicator=0;
|
|
options_.saopt.rt=0.10;
|
|
options_.saopt.MaxIter=100000;
|
|
options_.saopt.verbosity=1;
|
|
options_.saopt.TolFun=1.0e-8;
|
|
options_.saopt.initial_temperature=15;
|
|
options_.saopt.ns=10;
|
|
options_.saopt.nt=10;
|
|
options_.saopt.step_length_c=0.1;
|
|
options_.saopt.initial_step_length=1;
|
|
|
|
% particleswarm (global optimization toolbox needed)
|
|
particleswarm.Display = 'iter';
|
|
particleswarm.DisplayInterval = 1;
|
|
particleswarm.FunctionTolerance = 1e-6;
|
|
particleswarm.FunValCheck = 'on';
|
|
particleswarm.HybridFcn = [];
|
|
particleswarm.InertiaRange = [0.1, 1.1];
|
|
particleswarm.MaxIterations = 100000;
|
|
particleswarm.MaxStallIterations = 20;
|
|
particleswarm.MaxStallTime = Inf;
|
|
particleswarm.MaxTime = Inf;
|
|
particleswarm.MinNeighborsFraction = .25;
|
|
particleswarm.ObjectiveLimit = -Inf;
|
|
particleswarm.UseParallel = false;
|
|
particleswarm.UseVectorized = false;
|
|
options_.particleswarm = particleswarm;
|
|
|
|
% prior analysis
|
|
options_.prior_mc = 20000;
|
|
options_.prior_analysis_endo_var_list = {};
|
|
|
|
% did model undergo block decomposition + minimum feedback set computation ?
|
|
options_.block = false;
|
|
|
|
% model evaluated using a compiled MEX
|
|
options_.use_dll = false;
|
|
|
|
% model evaluated using bytecode.dll
|
|
options_.bytecode = false;
|
|
|
|
% if true, use a fixed point method to solve Lyapunov equation (for large scale models)
|
|
options_.lyapunov_fp = false;
|
|
% if true, use a doubling algorithm to solve Lyapunov equation (for large scale models)
|
|
options_.lyapunov_db = false;
|
|
% if true, use a square root solver to solve Lyapunov equation (for large scale models)
|
|
options_.lyapunov_srs = false;
|
|
|
|
% convergence criterion for iteratives methods to solve lyapunov equations
|
|
options_.lyapunov_fixed_point_tol = 1e-10;
|
|
options_.lyapunov_doubling_tol = 1e-16;
|
|
|
|
% if true, use a cycle reduction method to compute the decision rule (for large scale models)
|
|
options_.dr_cycle_reduction = false;
|
|
|
|
% convergence criterion for iteratives methods to solve the decision rule
|
|
options_.dr_cycle_reduction_tol = 1e-7;
|
|
|
|
% if true, use a logarithmic reduction method to compute the decision rule (for large scale models)
|
|
options_.dr_logarithmic_reduction = false;
|
|
|
|
% convergence criterion for iteratives methods to solve the decision rule
|
|
options_.dr_logarithmic_reduction_tol = 1e-12;
|
|
|
|
% convergence criterion for iteratives methods to solve the decision rule
|
|
options_.dr_logarithmic_reduction_maxiter = 100;
|
|
|
|
% dates for historical time series
|
|
options_.initial_date = dates();
|
|
|
|
% discretionary policy
|
|
options_.discretionary_policy = false;
|
|
options_.discretionary_tol = 1e-7;
|
|
|
|
% Shock decomposition
|
|
options_.parameter_set = [];
|
|
options_.use_shock_groups = '';
|
|
options_.shock_decomp.colormap = '';
|
|
options_.shock_decomp.init_state = 0;
|
|
options_.shock_decomp.with_epilogue = false;
|
|
|
|
% Shock decomposition realtime
|
|
options_.shock_decomp.forecast = 0;
|
|
options_.shock_decomp.presample = NaN;
|
|
options_.shock_decomp.save_realtime = 0; % saves memory
|
|
options_ = set_default_plot_shock_decomposition_options(options_);
|
|
|
|
% Nonlinearfilters
|
|
options_.nonlinear_filter = [];
|
|
|
|
% SBVAR & MS SBVAR initializations:
|
|
% SBVAR
|
|
options_.ms.vlistlog = [];
|
|
options_.ms.restriction_fname = 0;
|
|
options_.ms.cross_restrictions = false;
|
|
options_.ms.contemp_reduced_form = false;
|
|
options_.ms.real_pseudo_forecast = 0;
|
|
options_.ms.dummy_obs = 0;
|
|
options_.ms.ncsk = 0;
|
|
options_.ms.indxgforhat = 1;
|
|
options_.ms.indxgimfhat = 1;
|
|
options_.ms.indxestima = 1;
|
|
options_.ms.indxgdls = 1;
|
|
options_.ms.cms =0;
|
|
options_.ms.ncms = 0;
|
|
options_.ms.eq_cms = 1;
|
|
options_.ms.banact = 1;
|
|
options_.ms.log_var = [];
|
|
options_.ms.Qi = [];
|
|
options_.ms.Ri = [];
|
|
options_.ms.lower_cholesky = 0;
|
|
options_.ms.upper_cholesky = 0;
|
|
options_.ms.constants_exclusion = 0;
|
|
|
|
% MS SBVAR (and some SBVAR)
|
|
options_ = initialize_ms_sbvar_options(M_, options_);
|
|
|
|
% saved graph formats
|
|
options_.graph_save_formats.eps = 1;
|
|
options_.graph_save_formats.pdf = 0;
|
|
options_.graph_save_formats.fig = 0;
|
|
|
|
% endogenous prior
|
|
options_.endogenous_prior = false;
|
|
options_.endogenous_prior_restrictions.irf={};
|
|
options_.endogenous_prior_restrictions.moment={};
|
|
|
|
% OSR Optimal Simple Rules
|
|
options_.osr.opt_algo=4;
|
|
|
|
%Geweke convergence diagnostics
|
|
options_.convergence.geweke.taper_steps=[4 8 15];
|
|
options_.convergence.geweke.geweke_interval=[0.2 0.5];
|
|
%Raftery/Lewis convergence diagnostics;
|
|
options_.convergence.rafterylewis.indicator=false;
|
|
options_.convergence.rafterylewis.qrs=[0.025 0.005 0.95];
|
|
%Brooks Gelman convergence plots
|
|
options_.convergence.brooksgelman.plotrows=3;
|
|
|
|
%tolerance for Modified Harmonic Mean estimator
|
|
options_.marginal_data_density.harmonic_mean.tolerance = 0.01;
|
|
|
|
% Options for lmmcp solver
|
|
options_.lmmcp.status = false;
|
|
|
|
% Options for lcppath solver
|
|
options_.lcppath.A = [];
|
|
options_.lcppath.b = [];
|
|
options_.lcppath.t = [];
|
|
options_.lcppath.mu0 = [];
|
|
|
|
% Options for mcppath solver
|
|
options_.mcppath.A = [];
|
|
options_.mcppath.b = [];
|
|
options_.mcppath.t = [];
|
|
options_.mcppath.mu0 = [];
|
|
|
|
%Figure options
|
|
options_.figures.textwidth=0.8;
|
|
|
|
options_.varobs_id=[]; %initialize field
|
|
|
|
options_.pac.estimation.ols.share_of_optimizing_agents.lb = 0.0;
|
|
options_.pac.estimation.ols.share_of_optimizing_agents.ub = 1.0;
|
|
|
|
options_.conditional_likelihood.status = false;
|
|
options_.conditional_likelihood.order = 1;
|