adding new option 'fast_kalman_filter' implementing Ed Herbst 2012 approach
Michel Juillard
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105100c7fe
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@ -5527,6 +5527,12 @@ from the likelihood computations (for details see @cite{Durbin and Koopman (2012
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singularity is encountered, Dynare by default automatically switches to the univariate Kalman filter for this parameter draw. This behavior can be changed via the
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@ref{use_univariate_filters_if_singularity_is_detected} option.
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@item fast_kalman_filter
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@anchor{fast_kalman_filter} Select the fast Kalman filter using Chandrasekhar
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recursions as described by @cite{Herbst, 2012}. This setting is only used with
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@code{kalman_algo=1} or @code{kalman_algo=3}. It is not yet compatible with
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@code{analytical_derivation}.
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@item kalman_tol = @var{DOUBLE}
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@anchor{kalman_tol} Numerical tolerance for determining the singularity of the covariance matrix of the prediction errors during the Kalman filter (minimum allowed reciprocal of the matrix condition number). Default value is @code{1e-10}
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@ -13099,6 +13105,11 @@ Hansen, Nikolaus and Stefan Kern (2004): ``Evaluating the CMA Evolution Strategy
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on Multimodal Test Functions''. In: @i{Eighth International Conference on Parallel
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Problem Solving from Nature PPSN VIII, Proceedings}, Berlin: Springer, 282--291
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@item
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Edward Herbst (2015):
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``Using the “Chandrasekhar Recursions” for Likelihood Evaluation of DSGE
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Models,'' @i{Computational Economics}, 45(4), 693--705.
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@item
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Ireland, Peter (2004): ``A Method for Taking Models to the Data,''
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@i{Journal of Economic Dynamics and Control}, 28, 1205--26
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@ -659,6 +659,13 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
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if DynareOptions.block
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[err, LIK] = block_kalman_filter(T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
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mexErrCheck('block_kalman_filter', err);
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elseif DynareOptions.fast_kalman_filter
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[LIK,lik] = kalman_filter_fast(Y,diffuse_periods+1,size(Y,2), ...
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a,Pstar, ...
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kalman_tol, riccati_tol, ...
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DynareOptions.presample, ...
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T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods, ...
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analytic_deriv_info{:});
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else
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[LIK,lik] = kalman_filter(Y,diffuse_periods+1,size(Y,2), ...
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a,Pstar, ...
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@ -206,7 +206,21 @@ if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && options_.m
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return
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end
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% Estimation of the posterior mode or likelihood mode
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%% Estimation of the posterior mode or likelihood mode
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% analytical derivation is not yet available for kalman_filter_fast
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if options_.analytic_derivation && options_.fast_kalman_filter
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error(['estimation option conflict: analytic_derivation isn''t available ' ...
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'for fast_kalman_filter'])
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end
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% fast kalman filter is only available with kalman_algo == 1,3
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if options_.fast_kalman_filter && ...
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(options_.kalman_algo == 1 || options_.kalman_algo == 3)
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error(['estimation option conflict: fast_kalman_filter is only available ' ...
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'with kalman_algo = 1 or kalman_algo = 3'])
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end
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if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
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%prepare settings for newrat
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if options_.mode_compute==5
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@ -407,7 +407,7 @@ options_.filtered_vars = 0;
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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_.fast_kalman = 0;
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options_.fast_kalman_filter = 0;
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options_.kalman_tol = 1e-10;
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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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@ -101,7 +101,7 @@ class ParsingDriver;
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%token IDENTIFICATION INF_CONSTANT INITVAL INITVAL_FILE BOUNDS JSCALE INIT INFILE INVARS
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%token <string_val> INT_NUMBER
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%token INV_GAMMA_PDF INV_GAMMA1_PDF INV_GAMMA2_PDF IRF IRF_SHOCKS IRF_PLOT_THRESHOLD IRF_CALIBRATION
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%token KALMAN_ALGO KALMAN_TOL DIFFUSE_KALMAN_TOL SUBSAMPLES OPTIONS TOLF
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%token FAST_KALMAN_FILTER KALMAN_ALGO KALMAN_TOL DIFFUSE_KALMAN_TOL SUBSAMPLES OPTIONS TOLF
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%token LAPLACE LIK_ALGO LIK_INIT LINEAR LOAD_IDENT_FILES LOAD_MH_FILE LOAD_PARAMS_AND_STEADY_STATE LOGLINEAR LOGDATA LYAPUNOV LINEAR_APPROXIMATION
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%token LYAPUNOV_FIXED_POINT_TOL LYAPUNOV_DOUBLING_TOL LYAPUNOV_SQUARE_ROOT_SOLVER_TOL LOG_DEFLATOR LOG_TREND_VAR LOG_GROWTH_FACTOR MARKOWITZ MARGINAL_DENSITY MAX MAXIT
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%token MFS MH_CONF_SIG MH_DROP MH_INIT_SCALE MH_JSCALE MH_MODE MH_NBLOCKS MH_REPLIC MH_RECOVER POSTERIOR_MAX_SUBSAMPLE_DRAWS MIN MINIMAL_SOLVING_PERIODS
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@ -1698,6 +1698,7 @@ estimation_options : o_datafile
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| o_moments_varendo
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| o_contemporaneous_correlation
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| o_filtered_vars
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| o_fast_kalman_filter
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| o_kalman_algo
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| o_kalman_tol
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| o_diffuse_kalman_tol
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@ -2746,6 +2747,7 @@ o_moments_varendo : MOMENTS_VARENDO { driver.option_num("moments_varendo", "1");
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o_contemporaneous_correlation : CONTEMPORANEOUS_CORRELATION { driver.option_num("contemporaneous_correlation", "1"); };
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o_filtered_vars : FILTERED_VARS { driver.option_num("filtered_vars", "1"); };
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o_relative_irf : RELATIVE_IRF { driver.option_num("relative_irf", "1"); };
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o_fast_kalman_filter : FAST_KALMAN_FILTER { driver.option_num("fast_kalman_filter", "1"); };
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o_kalman_algo : KALMAN_ALGO EQUAL INT_NUMBER { driver.option_num("kalman_algo", $3); };
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o_kalman_tol : KALMAN_TOL EQUAL non_negative_number { driver.option_num("kalman_tol", $3); };
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o_diffuse_kalman_tol : DIFFUSE_KALMAN_TOL EQUAL non_negative_number { driver.option_num("diffuse_kalman_tol", $3); };
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@ -295,6 +295,7 @@ 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>logdata {return token::LOGDATA;}
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<DYNARE_STATEMENT>nodiagnostic {return token::NODIAGNOSTIC;}
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<DYNARE_STATEMENT>kalman_algo {return token::KALMAN_ALGO;}
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<DYNARE_STATEMENT>fast_kalman_filter {return token::FAST_KALMAN_FILTER;}
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<DYNARE_STATEMENT>kalman_tol {return token::KALMAN_TOL;}
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<DYNARE_STATEMENT>diffuse_kalman_tol {return token::DIFFUSE_KALMAN_TOL;}
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<DYNARE_STATEMENT>forecast {return token::FORECAST;}
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@ -4,6 +4,7 @@ MODFILES = \
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estimation/fs2000_initialize_from_calib.mod \
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estimation/fs2000_calibrated_covariance.mod \
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estimation/fs2000_model_comparison.mod \
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estimation/fs2000_fast.mod \
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estimation/MH_recover/fs2000_recover.mod \
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estimation/t_proposal/fs2000_student.mod \
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estimation/TaRB/fs2000_tarb.mod \
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