Added option nonlinear_filter_initialization.
Default value is 1 (initialization with the ergodic variance of the reduced form solution of the model approximated at order one). If the model has unit roots, the user must use `nonlinear_filter_initialization=3`, which select an identity matrix for the initial covariance matrix of the state variables. A side effect of this option is to temporarily change the value of options_.qz_criterium to a value above one (ie 1+1e-6) so that the unit roots are not rejected. If the model has unit roots and if the and if the option nonlinear_filter_initialization has a value less than 3, the evaluation of the likelihood will fail, because by default the unit root is counted as an unstable root.issue#70
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@ -111,7 +111,7 @@ class ParsingDriver;
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%token MODEL_DIAGNOSTICS MODIFIEDHARMONICMEAN MOMENTS_VARENDO CONTEMPORANEOUS_CORRELATION DIFFUSE_FILTER SUB_DRAWS TAPER_STEPS GEWEKE_INTERVAL RAFTERY_LEWIS_QRS RAFTERY_LEWIS_DIAGNOSTICS MCMC_JUMPING_COVARIANCE MOMENT_CALIBRATION
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%token NUMBER_OF_PARTICLES RESAMPLING SYSTEMATIC GENERIC RESAMPLING_THRESHOLD RESAMPLING_METHOD KITAGAWA STRATIFIED SMOOTH
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%token CPF_WEIGHTS AMISANOTRISTANI MURRAYJONESPARSLOW
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%token FILTER_ALGORITHM PROPOSAL_APPROXIMATION CUBATURE UNSCENTED MONTECARLO DISTRIBUTION_APPROXIMATION
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%token NONLINEAR_FILTER_INITIALIZATION FILTER_ALGORITHM PROPOSAL_APPROXIMATION CUBATURE UNSCENTED MONTECARLO DISTRIBUTION_APPROXIMATION
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%token <string_val> NAME
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%token USE_PENALIZED_OBJECTIVE_FOR_HESSIAN INIT_STATE
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%token NAN_CONSTANT NO_STATIC NOBS NOCONSTANT NODISPLAY NOCORR NODIAGNOSTIC NOFUNCTIONS NO_HOMOTOPY
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@ -1818,6 +1818,7 @@ estimation_options : o_datafile
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| o_resampling_threshold
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| o_resampling_method
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| o_filter_algorithm
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| o_nonlinear_filter_initialization
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| o_cpf_weights
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| o_proposal_approximation
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| o_distribution_approximation
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@ -3085,6 +3086,7 @@ o_resampling_method : RESAMPLING_METHOD EQUAL KITAGAWA {driver.option_num("parti
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o_cpf_weights : CPF_WEIGHTS EQUAL AMISANOTRISTANI {driver.option_num("particle.cpf_weights_method.amisanotristani", "1"); driver.option_num("particle.cpf_weights_method.murrayjonesparslow", "0"); }
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| CPF_WEIGHTS EQUAL MURRAYJONESPARSLOW {driver.option_num("particle.cpf_weights_method.amisanotristani", "0"); driver.option_num("particle.cpf_weights_method.murrayjonesparslow", "1"); };
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o_filter_algorithm : FILTER_ALGORITHM EQUAL symbol { driver.option_str("particle.filter_algorithm", $3); };
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o_nonlinear_filter_initialization : NONLINEAR_FILTER_INITIALIZATION EQUAL INT_NUMBER { driver.option_num("particle.initialization", $3); };
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o_proposal_approximation : PROPOSAL_APPROXIMATION EQUAL CUBATURE {driver.option_num("particle.proposal_approximation.cubature", "1"); driver.option_num("particle.proposal_approximation.unscented", "0"); driver.option_num("particle.proposal_approximation.montecarlo", "0");}
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| PROPOSAL_APPROXIMATION EQUAL UNSCENTED {driver.option_num("particle.proposal_approximation.cubature", "0"); driver.option_num("particle.proposal_approximation.unscented", "1"); driver.option_num("particle.proposal_approximation.montecarlo", "0");}
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| PROPOSAL_APPROXIMATION EQUAL MONTECARLO {driver.option_num("particle.proposal_approximation.cubature", "0"); driver.option_num("particle.proposal_approximation.unscented", "0"); driver.option_num("particle.proposal_approximation.montecarlo", "1");} ;
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@ -398,6 +398,7 @@ DATE -?[0-9]+([YyAa]|[Mm]([1-9]|1[0-2])|[Qq][1-4]|[Ww]([1-9]{1}|[1-4][0-9]|5[0-2
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<DYNARE_STATEMENT>amisanotristani {return token::AMISANOTRISTANI;}
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<DYNARE_STATEMENT>murrayjonesparslow {return token::MURRAYJONESPARSLOW;}
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<DYNARE_STATEMENT>filter_algorithm {return token::FILTER_ALGORITHM;}
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<DYNARE_STATEMENT>nonlinear_filter_initialization {return token::NONLINEAR_FILTER_INITIALIZATION;}
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<DYNARE_STATEMENT>proposal_approximation {return token::PROPOSAL_APPROXIMATION;}
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<DYNARE_STATEMENT>cubature {return token::CUBATURE;}
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<DYNARE_STATEMENT>unscented {return token::UNSCENTED;}
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