1function options_ = default_option_values(M_) 2%function default_option_values() 3% Returns structure containing the options for Dynare commands and their 4% default values 5% 6% INPUTS 7% M_ [structure] Model definition 8% 9% OUTPUTS 10% options [structure] Command options 11% 12% SPECIAL REQUIREMENTS 13% none 14 15% Copyright (C) 2018-2020 Dynare Team 16% 17% This file is part of Dynare. 18% 19% Dynare is free software: you can redistribute it and/or modify 20% it under the terms of the GNU General Public License as published by 21% the Free Software Foundation, either version 3 of the License, or 22% (at your option) any later version. 23% 24% Dynare is distributed in the hope that it will be useful, 25% but WITHOUT ANY WARRANTY; without even the implied warranty of 26% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 27% GNU General Public License for more details. 28% 29% You should have received a copy of the GNU General Public License 30% along with Dynare. If not, see <http://www.gnu.org/licenses/>. 31 32options_.datafile = ''; 33options_.dirname = M_.fname; 34options_.dataset = []; 35options_.verbosity = 1; 36options_.terminal_condition = 0; 37options_.rplottype = 0; 38options_.smpl = 0; 39options_.dynatol.f = 1e-5; 40options_.dynatol.x = 1e-5; 41options_.slowc = 1; 42options_.timing = 0; 43options_.gstep = ones(2,1); 44options_.gstep(1) = 1e-2; 45options_.gstep(2) = 1.0; 46options_.scalv = 1; 47options_.debug = false; 48options_.initval_file = false; 49options_.Schur_vec_tol = 1e-11; % used to find nonstationary variables in Schur decomposition of the 50 % transition matrix 51options_.qz_criterium = []; 52options_.qz_zero_threshold = 1e-6; 53options_.lyapunov_complex_threshold = 1e-15; 54options_.solve_tolf = eps^(1/3); 55options_.solve_tolx = eps^(2/3); 56options_.dr_display_tol=1e-6; 57options_.minimal_workspace = false; 58options_.dp.maxit = 3000; 59options_.steady.maxit = 50; 60options_.simul.maxit = 50; 61options_.simul.robust_lin_solve = false; 62 63options_.mode_check.status = false; 64options_.mode_check.neighbourhood_size = .5; 65options_.mode_check.symmetric_plots = true; 66options_.mode_check.number_of_points = 20; 67options_.mode_check.nolik = false; 68 69options_.huge_number = 1e7; 70 71% Default number of threads for parallelized mex files. 72options_.threads.kronecker.sparse_hessian_times_B_kronecker_C = num_procs; 73options_.threads.local_state_space_iteration_2 = 1; 74options_.threads.local_state_space_iteration_k = 1; 75options_.threads.perfect_foresight_problem = num_procs; 76options_.threads.k_order_perturbation = max(1, num_procs/2); 77 78% steady state 79options_.jacobian_flag = true; 80 81% steady state file 82if exist(['+' M_.fname '/steadystate.m'],'file') 83 options_.steadystate_flag = 2; 84elseif exist([M_.fname '_steadystate.m'],'file') 85 options_.steadystate_flag = 1; 86else 87 options_.steadystate_flag = 0; 88end 89options_.steadystate_partial = []; 90options_.steadystate.nocheck = false; 91 92% subset of the estimated deep parameters 93options_.ParamSubSet = 'None'; 94 95% bvar-dsge 96options_.dsge_var = 0; 97options_.dsge_varlag = 4; 98 99% BVAR a la Sims 100options_.bvar_replic = 2000; 101options_.bvar_prior_tau = 3; 102options_.bvar_prior_decay = 0.5; 103options_.bvar_prior_lambda = 5; 104options_.bvar_prior_mu = 2; 105options_.bvar_prior_omega = 1; 106options_.bvar_prior_flat = false; 107options_.bvar_prior_train = 0; 108options_.bvar.conf_sig = 0.6; 109 110% Initialize the field that will contain the optimization algorigthm's options declared in the 111% estimation command (if anny). 112options_.optim_opt = []; 113 114% Optimization algorithm [6] gmhmaxlik 115gmhmaxlik.iterations = 3; 116gmhmaxlik.number = 20000; 117gmhmaxlik.nclimb = 200000; 118gmhmaxlik.nscale = 200000; 119gmhmaxlik.target = 1/3; % Target for the acceptance rate. 120options_.gmhmaxlik = gmhmaxlik; 121 122% Request user input. 123options_.nointeractive = false; 124 125% Graphics 126options_.graphics.nrows = 3; 127options_.graphics.ncols = 3; 128options_.graphics.line_types = {'b-'}; 129options_.graphics.line_width = 1; 130options_.graph_format = 'eps'; 131options_.nodisplay = false; 132options_.nograph = false; 133options_.no_graph.posterior = false; 134options_.no_graph.shock_decomposition = false; 135options_.no_graph.plot_shock_decomposition = false; 136options_.XTick = []; 137options_.XTickLabel = []; 138options_.console_mode = false; 139if isoctave 140 if sum(get(0,'screensize'))==4 141 options_.console_mode = true; 142 options_.nodisplay = true; 143 end 144else 145 if isunix && (~usejava('jvm') || ~feature('ShowFigureWindows')) 146 options_.console_mode = true; 147 options_.nodisplay = true; 148 end 149end 150 151% IRFs & other stoch_simul output 152options_.irf = 40; 153options_.impulse_responses.plot_threshold=1e-10; 154options_.zero_moments_tolerance=1e-10; 155options_.relative_irf = false; 156options_.ar = 5; 157options_.hp_filter = 0; 158options_.one_sided_hp_filter = 0; 159options_.filtered_theoretical_moments_grid = 512; 160options_.nodecomposition = false; 161options_.nomoments = false; 162options_.nocorr = false; 163options_.periods = 0; 164options_.noprint = false; 165options_.SpectralDensity.trigger = false; 166options_.SpectralDensity.plot = 1; 167options_.SpectralDensity.cutoff = 150; 168options_.SpectralDensity.sdl = 0.01; 169options_.nofunctions = false; 170 171options_.bandpass.indicator = false; 172options_.bandpass.passband = [6; 32]; 173options_.bandpass.K=12; 174 175options_.irf_opt.diagonal_only = false; 176options_.irf_opt.stderr_multiples = false; 177options_.irf_opt.irf_shock_graphtitles = {}; 178options_.irf_opt.irf_shocks = []; 179 180% Extended path options 181% 182% Set debug flag 183ep.debug = 0; 184% Set memory flag 185ep.memory = 0; 186% Set verbose mode 187ep.verbosity = 0; 188% Set bytecode flag 189ep.use_bytecode = 0; 190% Initialization of the perfect foresight equilibrium paths 191% * init=0, previous solution is used. 192% * init=1, a path generated with the first order reduced form is used. 193% * init=2, mix of cases 0 and 1. 194ep.init = 0; 195% Maximum number of iterations for the deterministic solver. 196ep.maxit = 500; 197% Number of periods for the perfect foresight model. 198ep.periods = 200; 199% Default step for increasing the number of periods if needed 200ep.step = 50; 201% Set check_stability flag 202ep.check_stability = 0; 203% Define last periods used to test if the solution is stable with respect to an increase in the number of periods. 204ep.lp = 5; 205% Define first periods used to test if the solution is stable with respect to an increase in the number of periods. 206ep.fp = 2; 207% Define the distribution for the structural innovations. 208ep.innovation_distribution = 'gaussian'; 209% Set flag for the seed 210ep.set_dynare_seed_to_default = 1; 211% Set algorithm for the perfect foresight solver 212ep.stack_solve_algo = 7; 213ep.solve_algo = 9; 214% Number of replications 215ep.replic_nbr = 1; 216% Parallel execution of replications 217ep.parallel = false; 218% Stochastic extended path related options. 219ep.stochastic.IntegrationAlgorithm = 'Tensor-Gaussian-Quadrature'; % Other possible values are 'Stroud-Cubature-3' and 'Stroud-Cubature-5' 220ep.stochastic.method = ''; 221ep.stochastic.algo = 0; 222ep.stochastic.quadrature.ortpol = 'hermite'; 223ep.stochastic.order = 0; 224ep.stochastic.quadrature.nodes = 5; 225ep.stochastic.quadrature.pruned.status = 0; 226ep.stochastic.quadrature.pruned.relative = 1e-5; 227ep.stochastic.quadrature.pruned.level = 1e-5; 228ep.stochastic.hybrid_order = 0; 229% homotopic step in extended path simulations 230ep.stochastic.homotopic_steps = true; 231% Copy ep structure in options_ global structure 232options_.ep = ep; 233 234 235% Simulations of backward looking models options 236% 237bnlms.set_dynare_seed_to_default = 1; 238bnlms.innovation_distribution = 'gaussian'; 239options_.bnlms = bnlms; 240 241 242% Particle filter 243% 244% Default is that we do not use the non linear kalman filter 245particle.status = false; 246% How do we initialize the states? 247particle.initialization = 1; 248particle.initial_state_prior_std = .1; 249% Set the default order of approximation of the model (perturbation). 250particle.perturbation = 2; 251% Set the default number of particles. 252particle.number_of_particles = 5000; 253% Set the default approximation order (Smolyak) 254particle.smolyak_accuracy = 3; 255% By default we don't use pruning 256particle.pruning = 0; 257% Set the Gaussian approximation method for particles distributions 258particle.approximation_method = 'unscented'; 259% Set unscented parameters alpha, beta and kappa for gaussian approximation 260particle.unscented.alpha = 1; 261particle.unscented.beta = 2; 262particle.unscented.kappa = 1; 263% Configuration of resampling in case of particles 264particle.resampling.status.systematic = true; 265particle.resampling.status.none = false; 266particle.resampling.status.generic = false; 267particle.resampling.threshold = .5; 268particle.resampling.method.kitagawa = true; 269particle.resampling.method.smooth = false; 270particle.resampling.method.stratified = false; 271% Set default algorithm 272particle.filter_algorithm = 'sis'; 273% Approximation of the proposal distribution 274particle.proposal_approximation.cubature = false; 275particle.proposal_approximation.unscented = true; 276particle.proposal_approximation.montecarlo = false; 277% Approximation of the particle distribution 278particle.distribution_approximation.cubature = false; 279particle.distribution_approximation.unscented = true; 280particle.distribution_approximation.montecarlo = false; 281% Number of partitions for the smoothed resampling method 282particle.resampling.number_of_partitions = 200; 283% Configuration of the mixture filters 284%particle.mixture_method = 'particles' ; 285% Size of the different mixtures 286particle.mixture_state_variables = 5 ; 287particle.mixture_structural_shocks = 1 ; 288particle.mixture_measurement_shocks = 1 ; 289% Online approach 290particle.liu_west_delta = 0.99 ; 291particle.liu_west_chol_sigma_bar = .01 ; 292% Options for setting the weights in conditional particle filters. 293particle.cpf_weights_method.amisanotristani = true; 294particle.cpf_weights_method.murrayjonesparslow = false; 295% Copy ep structure in options_ global structure 296options_.particle = particle; 297options_.rwgmh.init_scale = 1e-4 ; 298options_.rwgmh.scale_chain = 1 ; 299options_.rwgmh.scale_shock = 1e-5 ; 300 301% TeX output 302options_.TeX = false; 303 304% Exel 305options_.xls_sheet = 1; % Octave does not support the empty string, rather use first sheet 306options_.xls_range = ''; 307 308% Prior draws 309options_.prior_draws = 10000; 310 311% Prior posterior function sampling draws 312options_.sampling_draws = 500; 313 314options_.forecast = 0; 315options_.forecasts.conf_sig = 0.9; 316options_.conditional_forecast.conf_sig = 0.9; 317 318% Model 319options_.linear = false; 320 321% Deterministic simulation 322options_.stack_solve_algo = 0; 323options_.markowitz = 0.5; 324options_.minimal_solving_periods = 1; 325options_.endogenous_terminal_period = false; 326options_.no_homotopy = false; 327 328% Solution 329options_.order = 2; 330options_.pruning = false; 331options_.solve_algo = 4; 332options_.replic = 50; 333options_.simul_replic = 1; 334options_.drop = 100; 335options_.aim_solver = false; % i.e. by default do not use G.Anderson's AIM solver, use mjdgges instead 336options_.k_order_solver = false; % by default do not use k_order_perturbation but mjdgges 337options_.partial_information = false; 338options_.ACES_solver = false; 339options_.conditional_variance_decomposition = []; 340 341% Ramsey policy 342options_.ramsey_policy = false; 343options_.instruments = {}; 344options_.timeless = 0; 345options_.ramsey.maxit = 500; 346 347% estimation 348options_.initial_period = NaN; %dates(1,1); 349options_.dataset.file = []; 350options_.dataset.series = []; 351options_.dataset.firstobs = dates(); 352options_.dataset.lastobs = dates(); 353options_.dataset.nobs = NaN; 354options_.dataset.xls_sheet = []; 355options_.dataset.xls_range = []; 356options_.Harvey_scale_factor = 10; 357options_.MaxNumberOfBytes = 1e8; 358options_.MaximumNumberOfMegaBytes = 111; 359options_.analytic_derivation = 0; % Not a boolean, can also take values -1 or 2 360options_.analytic_derivation_mode = 0; 361options_.bayesian_irf = false; 362options_.bayesian_th_moments = 0; 363options_.diffuse_filter = false; 364options_.filter_step_ahead = []; 365options_.filtered_vars = false; 366options_.smoothed_state_uncertainty = false; 367options_.first_obs = NaN; 368options_.nobs = NaN; 369options_.kalman_algo = 0; 370options_.fast_kalman_filter = false; 371options_.kalman_tol = 1e-10; 372options_.kalman.keep_kalman_algo_if_singularity_is_detected = false; 373options_.diffuse_kalman_tol = 1e-6; 374options_.use_univariate_filters_if_singularity_is_detected = 1; 375options_.riccati_tol = 1e-6; 376options_.lik_algo = 1; 377options_.lik_init = 1; 378options_.load_mh_file = false; 379options_.load_results_after_load_mh = false; 380options_.logdata = false; 381options_.loglinear = false; 382options_.linear_approximation = false; 383options_.logged_steady_state = 0; 384options_.mh_conf_sig = 0.90; 385options_.prior_interval = 0.90; 386options_.mh_drop = 0.5; 387options_.mh_jscale = 0.2; 388options_.mh_tune_jscale.status = false; 389options_.mh_tune_jscale.guess = .2; 390options_.mh_tune_jscale.target = .33; 391options_.mh_tune_jscale.maxiter = 200000; 392options_.mh_tune_jscale.rho = .7; 393options_.mh_tune_jscale.stepsize = 1000; 394options_.mh_tune_jscale.c1 = .02; 395options_.mh_tune_jscale.c2 = .05; 396options_.mh_tune_jscale.c3 = 4; 397options_.mh_init_scale = 2*options_.mh_jscale; 398options_.mh_mode = 1; 399options_.mh_nblck = 2; 400options_.mh_recover = false; 401options_.mh_replic = 20000; 402options_.recursive_estimation_restart = 0; 403options_.MCMC_jumping_covariance='hessian'; 404options_.use_calibration_initialization = 0; 405options_.endo_vars_for_moment_computations_in_estimation=[]; 406 407% Run optimizer silently 408options_.silent_optimizer = false; 409 410% Prior restrictions 411options_.prior_restrictions.status = 0; 412options_.prior_restrictions.routine = []; 413 414options_.mode_compute = 4; 415options_.mode_file = ''; 416options_.moments_varendo = false; 417options_.nk = 1; 418options_.noconstant = false; 419options_.nodiagnostic = false; 420options_.mh_posterior_mode_estimation = 0; 421options_.prefilter = 0; 422options_.presample = 0; 423options_.prior_trunc = 1e-10; 424options_.smoother = false; 425options_.posterior_max_subsample_draws = 1200; 426options_.sub_draws = []; 427options_.ME_plot_tol=1e-6; 428options_.use_mh_covariance_matrix = false; 429options_.gradient_method = 2; %used by csminwel and newrat 430options_.gradient_epsilon = 1e-6; %used by csminwel and newrat 431options_.posterior_sampler_options.sampling_opt = []; %extended set of options for individual posterior samplers 432 % Random Walk Metropolis-Hastings 433options_.posterior_sampler_options.posterior_sampling_method = 'random_walk_metropolis_hastings'; 434options_.posterior_sampler_options.rwmh.proposal_distribution = 'rand_multivariate_normal'; 435options_.posterior_sampler_options.rwmh.student_degrees_of_freedom = 3; 436options_.posterior_sampler_options.rwmh.use_mh_covariance_matrix=0; 437options_.posterior_sampler_options.rwmh.save_tmp_file=0; 438% Tailored Random Block Metropolis-Hastings 439options_.posterior_sampler_options.tarb.proposal_distribution = 'rand_multivariate_normal'; 440options_.posterior_sampler_options.tarb.student_degrees_of_freedom = 3; 441options_.posterior_sampler_options.tarb.mode_compute=4; 442options_.posterior_sampler_options.tarb.new_block_probability=0.25; %probability that next parameter belongs to new block 443options_.posterior_sampler_options.tarb.optim_opt=''; %probability that next parameter belongs to new block 444options_.posterior_sampler_options.tarb.save_tmp_file=1; 445% Slice 446options_.posterior_sampler_options.slice.proposal_distribution = ''; 447options_.posterior_sampler_options.slice.rotated=0; 448options_.posterior_sampler_options.slice.slice_initialize_with_mode=0; 449options_.posterior_sampler_options.slice.use_mh_covariance_matrix=0; 450options_.posterior_sampler_options.slice.WR=[]; 451options_.posterior_sampler_options.slice.mode_files=[]; 452options_.posterior_sampler_options.slice.mode=[]; 453options_.posterior_sampler_options.slice.initial_step_size=0.8; 454options_.posterior_sampler_options.slice.save_tmp_file=1; 455% Independent Metropolis-Hastings 456options_.posterior_sampler_options.imh.proposal_distribution = 'rand_multivariate_normal'; 457options_.posterior_sampler_options.imh.use_mh_covariance_matrix=0; 458options_.posterior_sampler_options.imh.save_tmp_file=0; 459 460options_.trace_plot_ma = 200; 461options_.mh_autocorrelation_function_size = 30; 462options_.plot_priors = 1; 463options_.cova_compute = 1; 464options_.parallel = 0; 465options_.parallel_info.isHybridMatlabOctave = false; 466options_.parallel_info.leaveSlaveOpen = 0; 467options_.parallel_info.RemoteTmpFolder = ''; 468options_.number_of_grid_points_for_kde = 2^9; 469quarter = 1; 470years = [1 2 3 4 5 10 20 30 40 50]; 471options_.conditional_variance_decomposition_dates = zeros(1,length(years)); 472for i=1:length(years) 473 options_.conditional_variance_decomposition_dates(i) = ... 474 (years(i)-1)*4+quarter; 475end 476options_.filter_covariance = false; 477options_.filter_decomposition = false; 478options_.selected_variables_only = false; 479options_.contemporaneous_correlation = false; 480options_.initialize_estimated_parameters_with_the_prior_mode = 0; 481options_.estimation_dll = false; 482options_.estimation.moments_posterior_density.indicator = true; 483options_.estimation.moments_posterior_density.gridpoints = 2^9; 484options_.estimation.moments_posterior_density.bandwidth = 0; % Rule of thumb optimal bandwidth parameter. 485options_.estimation.moments_posterior_density.kernel_function = 'gaussian'; % Gaussian kernel for Fast Fourrier Transform approximaton. 486 % Misc 487 % options_.conf_sig = 0.6; 488 489% homotopy for steady state 490options_.homotopy_mode = 0; 491options_.homotopy_steps = 1; 492options_.homotopy_force_continue = false; 493 494% numerical hessian 495hessian.use_penalized_objective = false; 496 497% Robust prediction error covariance (kalman filter) 498options_.rescale_prediction_error_covariance = false; 499 500options_.hessian = hessian; 501 502%csminwel optimization routine 503csminwel.tolerance.f=1e-7; 504csminwel.maxiter=1000; 505csminwel.verbosity=1; 506csminwel.Save_files=1; 507 508options_.csminwel=csminwel; 509 510%newrat optimization routine 511newrat.hess=1; % dynare numerical hessian 512newrat.tolerance.f=1e-5; 513newrat.tolerance.f_analytic=1e-7; 514newrat.maxiter=1000; 515newrat.verbosity=1; 516newrat.Save_files=1; 517 518options_.newrat=newrat; 519 520% Simplex optimization routine (variation on Nelder Mead algorithm). 521simplex.tolerance.x = 1e-4; 522simplex.tolerance.f = 1e-4; 523simplex.maxiter = 10000; 524simplex.maxfcallfactor = 500; 525simplex.maxfcall = []; 526simplex.verbosity = 2; 527simplex.delta_factor=0.05; 528options_.simplex = simplex; 529 530% CMAES optimization routine. 531cmaes.SaveVariables='on'; 532cmaes.DispFinal='on'; 533cmaes.WarnOnEqualFunctionValues='no'; 534cmaes.DispModulo='10'; 535cmaes.LogModulo='0'; 536cmaes.LogTime='0'; 537cmaes.TolFun = 1e-7; 538cmaes.TolX = 1e-7; 539cmaes.Resume = 0; 540options_.cmaes = cmaes; 541 542% simpsa optimization routine. 543simpsa.TOLFUN = 1e-4; 544simpsa.TOLX = 1e-4; 545simpsa.TEMP_END = .1; 546simpsa.COOL_RATE = 10; 547simpsa.INITIAL_ACCEPTANCE_RATIO = .95; 548simpsa.MIN_COOLING_FACTOR = .9; 549simpsa.MAX_ITER_TEMP_FIRST = 50; 550simpsa.MAX_ITER_TEMP_LAST = 2000; 551simpsa.MAX_ITER_TEMP = 10; 552simpsa.MAX_ITER_TOTAL = 5000; 553simpsa.MAX_TIME = 2500; 554simpsa.MAX_FUN_EVALS = 20000; 555simpsa.DISPLAY = 'iter'; 556options_.simpsa = simpsa; 557 558%solveopt optimizer 559solveopt.minimizer_indicator=-1; %use minimizer 560solveopt.TolX=1e-6; %accuracy of argument 561solveopt.TolFun=1e-6; %accuracy of function 562solveopt.MaxIter=15000; 563solveopt.verbosity=1; 564solveopt.TolXConstraint=1.e-8; 565solveopt.SpaceDilation=2.5; 566solveopt.LBGradientStep=1.e-11; 567options_.solveopt=solveopt; 568 569%simulated annealing 570options_.saopt.neps=10; 571options_.saopt.maximizer_indicator=0; 572options_.saopt.rt=0.10; 573options_.saopt.MaxIter=100000; 574options_.saopt.verbosity=1; 575options_.saopt.TolFun=1.0e-8; 576options_.saopt.initial_temperature=15; 577options_.saopt.ns=10; 578options_.saopt.nt=10; 579options_.saopt.step_length_c=0.1; 580options_.saopt.initial_step_length=1; 581 582% particleswarm (global optimization toolbox needed) 583particleswarm.Display = 'iter'; 584particleswarm.DisplayInterval = 1; 585particleswarm.FunctionTolerance = 1e-6; 586particleswarm.FunValCheck = 'on'; 587particleswarm.HybridFcn = []; 588particleswarm.InertiaRange = [0.1, 1.1]; 589particleswarm.MaxIterations = 100000; 590particleswarm.MaxStallIterations = 20; 591particleswarm.MaxStallTime = Inf; 592particleswarm.MaxTime = Inf; 593particleswarm.MinNeighborsFraction = .25; 594particleswarm.ObjectiveLimit = -Inf; 595particleswarm.UseParallel = false; 596particleswarm.UseVectorized = false; 597options_.particleswarm = particleswarm; 598 599% prior analysis 600options_.prior_mc = 20000; 601options_.prior_analysis_endo_var_list = {}; 602 603% did model undergo block decomposition + minimum feedback set computation ? 604options_.block = false; 605 606% model evaluated using a compiled MEX 607options_.use_dll = false; 608 609% model evaluated using bytecode.dll 610options_.bytecode = false; 611 612% if true, use a fixed point method to solve Sylvester equation (for large scale models) 613options_.sylvester_fp = false; 614 615% convergence criteria to solve iteratively a sylvester equations 616options_.sylvester_fixed_point_tol = 1e-12; 617 618% if true, use a fixed point method to solve Lyapunov equation (for large scale models) 619options_.lyapunov_fp = false; 620% if true, use a doubling algorithm to solve Lyapunov equation (for large scale models) 621options_.lyapunov_db = false; 622% if true, use a square root solver to solve Lyapunov equation (for large scale models) 623options_.lyapunov_srs = false; 624 625% convergence criterion for iteratives methods to solve lyapunov equations 626options_.lyapunov_fixed_point_tol = 1e-10; 627options_.lyapunov_doubling_tol = 1e-16; 628 629% if true, use a cycle reduction method to compute the decision rule (for large scale models) 630options_.dr_cycle_reduction = false; 631 632% convergence criterion for iteratives methods to solve the decision rule 633options_.dr_cycle_reduction_tol = 1e-7; 634 635% if true, use a logarithmic reduction method to compute the decision rule (for large scale models) 636options_.dr_logarithmic_reduction = false; 637 638% convergence criterion for iteratives methods to solve the decision rule 639options_.dr_logarithmic_reduction_tol = 1e-12; 640 641% convergence criterion for iteratives methods to solve the decision rule 642options_.dr_logarithmic_reduction_maxiter = 100; 643 644% dates for historical time series 645options_.initial_date = dates(); 646 647% discretionary policy 648options_.discretionary_policy = 0; 649options_.discretionary_tol = 1e-7; 650 651% Shock decomposition 652options_.parameter_set = []; 653options_.use_shock_groups = ''; 654options_.shock_decomp.colormap = ''; 655options_.shock_decomp.init_state = 0; 656options_.shock_decomp.with_epilogue = false; 657 658% Shock decomposition realtime 659options_.shock_decomp.forecast = 0; 660options_.shock_decomp.presample = NaN; 661options_.shock_decomp.save_realtime = 0; % saves memory 662options_ = set_default_plot_shock_decomposition_options(options_); 663 664% Nonlinearfilters 665options_.nonlinear_filter = []; 666 667% SBVAR & MS SBVAR initializations: 668% SBVAR 669options_.ms.vlistlog = []; 670options_.ms.restriction_fname = 0; 671options_.ms.cross_restrictions = false; 672options_.ms.contemp_reduced_form = false; 673options_.ms.real_pseudo_forecast = 0; 674options_.ms.dummy_obs = 0; 675options_.ms.ncsk = 0; 676options_.ms.indxgforhat = 1; 677options_.ms.indxgimfhat = 1; 678options_.ms.indxestima = 1; 679options_.ms.indxgdls = 1; 680options_.ms.cms =0; 681options_.ms.ncms = 0; 682options_.ms.eq_cms = 1; 683options_.ms.banact = 1; 684options_.ms.log_var = []; 685options_.ms.Qi = []; 686options_.ms.Ri = []; 687options_.ms.lower_cholesky = 0; 688options_.ms.upper_cholesky = 0; 689options_.ms.constants_exclusion = 0; 690 691% MS SBVAR (and some SBVAR) 692options_ = initialize_ms_sbvar_options(M_, options_); 693 694% saved graph formats 695options_.graph_save_formats.eps = 1; 696options_.graph_save_formats.pdf = 0; 697options_.graph_save_formats.fig = 0; 698 699% risky steady state 700options_.risky_steadystate = false; 701 702% endogenous prior 703options_.endogenous_prior = false; 704options_.endogenous_prior_restrictions.irf={}; 705options_.endogenous_prior_restrictions.moment={}; 706 707% OSR Optimal Simple Rules 708options_.osr.opt_algo=4; 709 710% use GPU 711options_.gpu = false; 712 713%Geweke convergence diagnostics 714options_.convergence.geweke.taper_steps=[4 8 15]; 715options_.convergence.geweke.geweke_interval=[0.2 0.5]; 716%Raftery/Lewis convergence diagnostics; 717options_.convergence.rafterylewis.indicator=false; 718options_.convergence.rafterylewis.qrs=[0.025 0.005 0.95]; 719 720%tolerance for Modified Harmonic Mean estimator 721options_.marginal_data_density.harmonic_mean.tolerance = 0.01; 722 723% Options for lmmcp solver 724options_.lmmcp.status = false; 725 726% Options for lcppath solver 727options_.lcppath.A = []; 728options_.lcppath.b = []; 729options_.lcppath.t = []; 730options_.lcppath.mu0 = []; 731 732% Options for mcppath solver 733options_.mcppath.A = []; 734options_.mcppath.b = []; 735options_.mcppath.t = []; 736options_.mcppath.mu0 = []; 737 738%Figure options 739options_.figures.textwidth=0.8; 740 741options_.varobs_id=[]; %initialize field 742 743end 744