diff --git a/doc/dynare.texi b/doc/dynare.texi
index d7c888d32..c99e583b3 100644
--- a/doc/dynare.texi
+++ b/doc/dynare.texi
@@ -4212,12 +4212,19 @@ graphs of smoothed shocks, smoothed observation errors, smoothed and historical
@algorithmshead
-The Monte Carlo Markov Chain (MCMC) univariate diagnostics are generated
-by the estimation command if @ref{mh_nblocks} is larger than 1, if
-@ref{mh_replic} is larger than 2000, and if option @ref{nodiagnostic} is
-not used. As described in section 3 of @cite{Brooks and Gelman (1998)}
-the convergence diagnostics are based on comparing pooled and within
-MCMC moments (Dynare displays the second and third order moments, and
+The Monte Carlo Markov Chain (MCMC) diagnostics are generated
+by the estimation command if @ref{mh_replic} is larger than 2000 and if
+option @ref{nodiagnostic} is not used. If @ref{mh_nblocks} is equal to one,
+the convergence diagnostics of @cite{Geweke (1992,1999)} is computed. It uses a
+chi square test to compare the means of the first and last draws specified by
+@ref{geweke_interval} after discarding the burnin of @ref{mh_drop}. The test is
+computed using variance estimates under the assumption of no serial correlation
+as well as using tapering windows specified in @ref{taper_steps}.
+If @ref{mh_nblocks} is larger than 1, the convergence diagnostics of
+@cite{Brooks and Gelman (1998)} are used instead.
+As described in section 3 of @cite{Brooks and Gelman (1998)} the univariate
+convergence diagnostics are based on comparing pooled and within MCMC moments
+(Dynare displays the second and third order moments, and
the length of the Highest Probability Density interval covering 80% of
the posterior distribution). Due to computational reasons, the
multivariate convergence diagnostic does not follow @cite{Brooks and
@@ -4358,8 +4365,8 @@ the total number of Metropolis draws available. Default:
@code{2}
@item mh_drop = @var{DOUBLE}
-The fraction of initially generated parameter vectors to be dropped
-before using posterior simulations. Default: @code{0.5}
+@anchor{mh_drop}
+The fraction of initially generated parameter vectors to be dropped as a burnin before using posterior simulations. Default: @code{0.5}
@item mh_jscale = @var{DOUBLE}
The scale to be used for the jumping distribution in
@@ -4758,6 +4765,18 @@ Value used to test if a generalized eigenvalue is 0/0 in the generalized
Schur decomposition (in which case the model does not admit a unique
solution). Default: @code{1e-6}.
+@item taper_steps = [@var{INTEGER1} @var{INTEGER2} @dots{}]
+@anchor{taper_steps}
+Percent tapering used for the spectral window in the @cite{Geweke (1992,1999)}
+convergence diagnostics (requires @ref{mh_nblocks}=1). The tapering is used to
+take the serial correlation of the posterior draws into account. Default: @code{[4 8 15]}.
+
+@item geweke_interval = [@var{DOUBLE} @var{DOUBLE}]
+@anchor{geweke_interval}
+Percentage of MCMC draws at the beginning and end of the MCMC chain taken
+to compute the @cite{Geweke (1992,1999)} convergence diagnostics (requires @ref{mh_nblocks}=1)
+after discarding the first @ref{mh_drop} percent of draws as a burnin. Default: @code{[0.2 0.5]}.
+
@end table
@customhead{Note}
@@ -5048,6 +5067,56 @@ Upper/lower bound of the 90\% HPD interval taking into account both parameter an
@end defvr
+@defvr {MATLAB/Octave variable} oo_.convergence.geweke
+@anchor{convergence.geweke}
+Variable set by the convergence diagnostics of the @code{estimation} command when used with @ref{mh_nblocks}=1 option (@pxref{mh_nblocks}).
+
+Fields are of the form:
+@example
+@code{oo_.convergence.geweke.@var{VARIABLE_NAME}.@var{DIAGNOSTIC_OBJECT}}
+@end example
+where @var{DIAGNOSTIC_OBJECT} is one of the following:
+
+@table @code
+
+@item posteriormean
+Mean of the posterior parameter distribution
+
+@item posteriorstd
+Standard deviation of the posterior parameter distribution
+
+@item nse_iid
+Numerical standard error (NSE) under the assumption of iid draws
+
+@item rne_iid
+Relative numerical efficiency (RNE) under the assumption of iid draws
+
+@item nse_x
+Numerical standard error (NSE) when using an x% taper
+
+@item rne_x
+Relative numerical efficiency (RNE) when using an x% taper
+
+@item pooled_mean
+Mean of the parameter when pooling the beginning and end parts of the chain
+specified in @ref{geweke_interval} and weighting them with their relative precision.
+It is a vector containing the results under the iid assumption followed by the ones
+using the @ref{taper_steps} (@pxref{taper_steps}).
+
+@item pooled_nse
+NSE of the parameter when pooling the beginning and end parts of the chain and weighting them with their relative precision. See @code{pooled_mean}
+
+@item prob_chi2_test
+p-value of a chi squared test for equality of means in the beginning and the end
+of the MCMC chain. See @code{pooled_mean}. A value above 0.05 indicates that
+the null hypothesis of equal means and thus convergence cannot be rejected
+at the 5 percent level. Differing values along the @ref{taper_steps} signal
+the presence of significant autocorrelation in draws. In this case, the
+estimates using a higher tapering are usually more reliable.
+
+@end table
+@end defvr
+
@deffn Command model_comparison @var{FILENAME}[(@var{DOUBLE})]@dots{};
@deffnx Command model_comparison (marginal_density = laplace | modifiedharmonicmean) @var{FILENAME}[(@var{DOUBLE})]@dots{};
@@ -8692,6 +8761,16 @@ Fernández-Villaverde, Jesús and Juan Rubio-Ramírez (2005): ``Estimating
Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood,''
@i{Journal of Applied Econometrics}, 20, 891--910
+@item
+Geweke, John (1992): ``Evaluating the accuracy of sampling-based approaches
+to the calculation of posterior moments'', in J.O. Berger, J.M. Bernardo,
+A.P. Dawid, and A.F.M. Smith (eds.) Proceedings of the Fourth Valencia
+International Meeting on Bayesian Statistics, pp. 169--194, Oxford University Press
+
+@item
+Geweke, John (1999): ``Using simulation methods for Bayesian econometric models:
+Inference, development and communication,'' @i{Econometric Reviews}, 18(1), 1--73
+
@item
Ireland, Peter (2004): ``A Method for Taking Models to the Data,''
@i{Journal of Economic Dynamics and Control}, 28, 1205--26
diff --git a/matlab/McMCDiagnostics.m b/matlab/McMCDiagnostics.m
index d4a26cb8c..6491be936 100644
--- a/matlab/McMCDiagnostics.m
+++ b/matlab/McMCDiagnostics.m
@@ -1,4 +1,4 @@
-function McMCDiagnostics(options_, estim_params_, M_)
+function oo_ = McMCDiagnostics(options_, estim_params_, M_, oo_)
% function McMCDiagnostics
% Computes convergence tests
%
@@ -8,7 +8,7 @@ function McMCDiagnostics(options_, estim_params_, M_)
% M_ [structure]
%
% OUTPUTS
-% none
+% oo_ [structure]
%
% SPECIAL REQUIREMENTS
% none
@@ -38,10 +38,6 @@ MhDirectoryName = CheckPath('metropolis',M_.dname);
TeX = options_.TeX;
nblck = options_.mh_nblck;
-% Brooks and Gelman tests need more than one block
-if nblck == 1
- return;
-end
npar = estim_params_.nvx;
npar = npar + estim_params_.nvn;
npar = npar + estim_params_.ncx;
@@ -56,6 +52,57 @@ NumberOfMcFilesPerBlock = size(dir([MhDirectoryName ,filesep, M_.fname '_mh*_blc
% check if all previous files are there for block 1
check_presence_consecutive_MC_files(MhDirectoryName,M_.fname,1)
+if nblck == 1 % Brooks and Gelman tests need more than one block
+ convergence_diagnostics_geweke=zeros(npar,4+2*length(options_.convergence.geweke.taper_steps));
+ if any(options_.convergence.geweke.geweke_interval<0) || any(options_.convergence.geweke.geweke_interval>1) || length(any(options_.convergence.geweke.geweke_interval<0))~=2 ...
+ || (options_.convergence.geweke.geweke_interval(2)-options_.convergence.geweke.geweke_interval(1)<0)
+ fprintf('\nCONVERGENCE DIAGNOSTICS: Invalid option for geweke_interval. Using the default of [0.2 0.5].\n')
+ options_.convergence.geweke.geweke_interval=[0.2 0.5];
+ end
+ first_obs_begin_sample = max(1,ceil(options_.mh_drop*options_.mh_replic));
+ last_obs_begin_sample = first_obs_begin_sample+round(options_.convergence.geweke.geweke_interval(1)*options_.mh_replic*options_.mh_drop);
+ first_obs_end_sample = first_obs_begin_sample+round(options_.convergence.geweke.geweke_interval(2)*options_.mh_replic*options_.mh_drop);
+ param_name=[];
+ for jj=1:npar
+ param_name = strvcat(param_name,get_the_name(jj,options_.TeX,M_,estim_params_,options_));
+ end
+ fprintf('\nGeweke (1992) Convergence Tests, based on means of draws %d to %d vs %d to %d.\n',first_obs_begin_sample,last_obs_begin_sample,first_obs_end_sample,options_.mh_replic);
+ fprintf('p-values are for Chi2-test for equality of means.\n');
+ Geweke_header={'Parameter', 'Post. Mean', 'Post. Std', 'p-val No Taper'};
+ print_string=['%',num2str(size(param_name,2)+3),'s \t %12.3f \t %12.3f \t %12.3f'];
+ print_string_header=['%',num2str(size(param_name,2)+3),'s \t %12s \t %12s \t %12s'];
+ for ii=1:length(options_.convergence.geweke.taper_steps)
+ Geweke_header=[Geweke_header, ['p-val ' num2str(options_.convergence.geweke.taper_steps(ii)),'% Taper']];
+ print_string=[print_string,'\t %12.3f'];
+ print_string_header=[print_string_header,'\t %12s'];
+ end
+ print_string=[print_string,'\n'];
+ print_string_header=[print_string_header,'\n'];
+ fprintf(print_string_header,Geweke_header{1,:});
+ for jj=1:npar
+ startline=0;
+ for n = 1:NumberOfMcFilesPerBlock
+ load([MhDirectoryName '/' M_.fname '_mh',int2str(n),'_blck1.mat'],'x2');
+ nx2 = size(x2,1);
+ param_draws(startline+(1:nx2),1) = x2(:,jj);
+ startline = startline + nx2;
+ end
+ [results_vec, results_struct] = geweke_moments(param_draws,options_);
+ convergence_diagnostics_geweke(jj,:)=results_vec;
+
+ param_draws1 = param_draws(first_obs_begin_sample:last_obs_begin_sample,:);
+ param_draws2 = param_draws(first_obs_end_sample:end,:);
+ [results_vec1] = geweke_moments(param_draws1,options_);
+ [results_vec2] = geweke_moments(param_draws2,options_);
+
+ results_struct = geweke_chi2_test(results_vec1,results_vec2,results_struct,options_);
+ eval(['oo_.convergence.geweke.',param_name(jj,:),'=results_struct;'])
+ fprintf(print_string,param_name(jj,:),results_struct.posteriormean,results_struct.posteriorstd,results_struct.prob_chi2_test)
+ end
+ skipline(2);
+ return;
+end
+
for blck = 2:nblck
tmp = size(dir([MhDirectoryName ,filesep, M_.fname '_mh*_blck' int2str(blck) '.mat']),1);
if tmp~=NumberOfMcFilesPerBlock
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 7e5071ded..73e24df17 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -611,8 +611,8 @@ if (any(bayestopt_.pshape >0 ) && options_.mh_replic) || ...
CutSample(M_, options_, estim_params_);
return
else
- if ~options_.nodiagnostic && options_.mh_replic > 2000 && options_.mh_nblck > 1
- McMCDiagnostics(options_, estim_params_, M_);
+ if ~options_.nodiagnostic && options_.mh_replic > 2000
+ oo_= McMCDiagnostics(options_, estim_params_, M_,oo_);
end
%% Here i discard first half of the draws:
CutSample(M_, options_, estim_params_);
diff --git a/matlab/geweke_chi2_test.m b/matlab/geweke_chi2_test.m
new file mode 100644
index 000000000..7ed383139
--- /dev/null
+++ b/matlab/geweke_chi2_test.m
@@ -0,0 +1,74 @@
+function results_struct = geweke_chi2_test(results1,results2,results_struct,options)
+% results_struct = geweke_chi2_test(results1,results2,results_struct,options)
+% PURPOSE: computes Geweke's chi-squared test for two sets of MCMC sample draws
+%
+% INPUTS
+% results1 [1 by (4+n_taper*2) vector] vector with post. mean,
+% std, NSE_iid, RNE_iid, and tapered NSE and RNE
+% for chain part 1
+% results2 [1 by (4+n_taper*2) vector] vector with post. mean,
+% std, NSE_iid, RNE_iid, and tapered NSE and RNE
+% for chain part 2
+% results_struct [structure] results structure generated by geweke_moments
+% Dynareoptions [structure]
+%
+% OUTPUTS
+% results_struct [structure] containing the following fields:
+% pooled_mean Pooled mean of the chain parts, weighted
+% with precision
+% rpooled_nse Pooled NSE of the chain parts, weighted
+% with precision
+% prob_chi2_test p-value of Chi2-test for equality of
+% means in both chain parts
+% -----------------------------------------------------------------
+
+%
+% SPECIAL REQUIREMENTS
+% None.
+
+% Copyright (C) 2013 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see .
+%
+% ------------------------------------------------
+% REFERENCES: Geweke (1992), `Evaluating the accuracy of sampling-based
+% approaches to the calculation of posterior moments', in J.O. Berger,
+% J.M. Bernardo, A.P. Dawid, and A.F.M. Smith (eds.) Proceedings of
+% the Fourth Valencia International Meeting on Bayesian Statistics,
+% pp. 169-194, Oxford University Press
+% Geweke (1999): `Using simulation methods for Bayesian econometric models:
+% Inference, development and communication', Econometric Reviews, 18(1),
+% 1-73
+
+% written by: Johannes Pfeifer,
+% based on code by James P. LeSage, who in turn
+% drew on MATLAB programs written by Siddartha Chib
+
+for k=1:length(options.convergence.geweke.taper_steps)+1;
+ NSE=[results1(:,3+(k-1)*2) results2(:,3+(k-1)*2)];
+ means=[results1(:,1) results2(:,1)];
+ diff_Means=means(:,1)-means(:,2);
+ sum_of_weights=sum(1./(NSE.^2),2);
+ pooled_mean=sum(means./(NSE.^2),2)./sum_of_weights;
+ pooled_NSE=1./sqrt(sum_of_weights);
+
+ test_stat=diff_Means.^2./sum(NSE.^2,2);
+ p = 1-chi2cdf(test_stat,1);
+ results_struct.pooled_mean(:,k) = pooled_mean;
+ results_struct.pooled_nse(:,k) = pooled_NSE;
+ results_struct.prob_chi2_test(:,k) = p;
+end;
+
diff --git a/matlab/geweke_moments.m b/matlab/geweke_moments.m
new file mode 100644
index 000000000..242a9d550
--- /dev/null
+++ b/matlab/geweke_moments.m
@@ -0,0 +1,109 @@
+function [results_vec, results_struct] = geweke_moments(draws,Dynareoptions)
+%[results_vec, results_struct] = geweke_moments(draws,Dynareoptions)
+% PURPOSE: computes Gewke's convergence diagnostics NSE and RNE
+% (numerical std error and relative numerical efficiencies)
+
+% INPUTS
+% draws [ndraws by 1 vector]
+% Dynareoptions [structure]
+%
+% OUTPUTS
+% results_vec
+% results_struct [structure] containing the following fields:
+% posteriormean= posterior parameter mean
+% posteriorstd = posterior standard deviation
+% nse_iid = nse assuming no serial correlation for variable i
+% rne_iid = rne assuming no serial correlation for variable i
+% nse_x = nse using x% autocovariance tapered estimate
+% rne_x = rne using x% autocovariance taper
+% -----------------------------------------------------------------
+
+%
+% SPECIAL REQUIREMENTS
+% None.
+
+% Copyright (C) 2013 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare. If not, see .
+
+% REFERENCES: Geweke (1992), `Evaluating the accuracy of sampling-based
+% approaches to the calculation of posterior moments', in J.O. Berger,
+% J.M. Bernardo, A.P. Dawid, and A.F.M. Smith (eds.) Proceedings of
+% the Fourth Valencia International Meeting on Bayesian Statistics,
+% pp. 169-194, Oxford University Press
+% Geweke (1999): `Using simulation methods for Bayesian econometric models:
+% Inference, development and communication', Econometric Reviews, 18(1),
+% 1-73
+% -----------------------------------------------------------------
+
+% written by: Johannes Pfeifer,
+% based on code by James P. LeSage, who in turn
+% drew on MATLAB programs written by Siddartha Chib
+
+
+ndraw = size(draws,1);
+n_groups=100;
+taper_steps=Dynareoptions.convergence.geweke.taper_steps;
+results_vec=zeros(1,4+2*length(taper_steps));
+
+ns = floor(ndraw/n_groups); %step_size
+n_draws_used = ns*n_groups; %effective number of draws used after rounding down
+
+window_means= zeros(n_groups,1);
+window_uncentered_variances= zeros(n_groups,1);
+for ig=1:n_groups;
+ window_means(ig,1)=sum(draws((ig-1)*ns+1:ig*ns,1))/ns;
+ window_uncentered_variances(ig,1)=sum(draws((ig-1)*ns+1:ig*ns,1).^2)/ns;
+end; %for ig
+total_mean=mean(window_means);
+total_variance=mean(window_uncentered_variances)-total_mean^2;
+
+% save posterior means and std deviations to results_struct structure
+results_vec(1,1)=total_mean;
+results_vec(1,2)=sqrt(total_variance);
+results_struct.posteriormean = total_mean;
+results_struct.posteriorstd = results_vec(1,2);
+
+% numerical standard error assuming no serial correlation
+NSE=std(draws(1:n_draws_used,1),1)/sqrt(n_draws_used);
+% save to results_struct structure
+results_vec(1,3)=NSE;
+results_vec(1,4)=total_variance/(n_draws_used*NSE^2);
+results_struct.nse_iid = NSE;
+results_struct.rne_iid = results_vec(1,4);
+
+%get autocovariance of grouped means
+centered_window_means=window_means-total_mean;
+autocov_grouped_means=zeros(n_groups,1);
+for lag=0:n_groups-1;
+ autocov_grouped_means(lag+1)=centered_window_means(lag+1:n_groups,1)'*centered_window_means(1:n_groups-lag,1)/100;
+end;
+
+% numerical standard error with tapered autocovariance functions
+for taper_index=1:length(taper_steps)
+ taper=taper_steps(taper_index);
+ taper_lags=(1:taper-1)';
+ taper_lag_weight=1-taper_lags/taper;
+ tapered_sum_of_covariances=autocov_grouped_means(1)+sum(2*taper_lag_weight.*autocov_grouped_means(1+taper_lags));
+ NSE_taper=sqrt(tapered_sum_of_covariances/n_groups);
+ % save results_struct in structure
+ results_vec(1,4+taper_index*2-1)=NSE_taper;
+ results_vec(1,4+taper_index*2)=total_variance/(n_draws_used*NSE_taper^2);
+
+ eval(['results_struct.nse_taper_',num2str(taper),'= NSE_taper;']);
+ eval(['results_struct.rne_taper_',num2str(taper),'= total_variance/(n_draws_used*NSE_taper^2);']);
+end; % end of for mm loop
+
diff --git a/matlab/global_initialization.m b/matlab/global_initialization.m
index d06a054d9..c067ef33f 100644
--- a/matlab/global_initialization.m
+++ b/matlab/global_initialization.m
@@ -577,6 +577,10 @@ options_.osr.verbose=2;
% use GPU
options_.gpu = 0;
+%Geweke convergence diagnostics
+options_.convergence.geweke.taper_steps=[4 8 15];
+options_.convergence.geweke.geweke_interval=[0.2 0.5];
+
% initialize persistent variables in priordens()
priordens([],[],[],[],[],[],1);
% initialize persistent variables in dyn_first_order_solver()
diff --git a/preprocessor/DynareBison.yy b/preprocessor/DynareBison.yy
index 5207b3a04..dd2192a79 100644
--- a/preprocessor/DynareBison.yy
+++ b/preprocessor/DynareBison.yy
@@ -113,7 +113,7 @@ class ParsingDriver;
%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
%token MFS MH_DROP MH_INIT_SCALE MH_JSCALE MH_MODE MH_NBLOCKS MH_REPLIC MH_RECOVER MIN MINIMAL_SOLVING_PERIODS SOLVE_MAXIT
%token MODE_CHECK MODE_CHECK_NEIGHBOURHOOD_SIZE MODE_CHECK_SYMMETRIC_PLOTS MODE_CHECK_NUMBER_OF_POINTS MODE_COMPUTE MODE_FILE MODEL MODEL_COMPARISON MODEL_INFO MSHOCKS ABS SIGN
-%token MODEL_DIAGNOSTICS MODIFIEDHARMONICMEAN MOMENTS_VARENDO DIFFUSE_FILTER SUB_DRAWS
+%token MODEL_DIAGNOSTICS MODIFIEDHARMONICMEAN MOMENTS_VARENDO DIFFUSE_FILTER SUB_DRAWS TAPER_STEPS GEWEKE_INTERVAL
%token NAME
%token NAN_CONSTANT NO_STATIC NOBS NOCONSTANT NODISPLAY NOCORR NODIAGNOSTIC NOFUNCTIONS
%token NOGRAPH NOMOMENTS NOPRINT NORMAL_PDF
@@ -1555,7 +1555,9 @@ estimation_options : o_datafile
| o_ar
| o_endogenous_prior
| o_use_univariate_filters_if_singularity_is_detected
- | o_qz_zero_threshold
+ | o_qz_zero_threshold
+ | o_taper_steps
+ | o_geweke_interval
;
list_optim_option : QUOTED_STRING COMMA QUOTED_STRING
@@ -2388,6 +2390,8 @@ o_noprint : NOPRINT { driver.option_num("noprint", "1"); };
o_xls_sheet : XLS_SHEET EQUAL symbol { driver.option_str("xls_sheet", $3); };
o_xls_range : XLS_RANGE EQUAL range { driver.option_str("xls_range", $3); };
o_filter_step_ahead : FILTER_STEP_AHEAD EQUAL vec_int { driver.option_vec_int("filter_step_ahead", $3); };
+o_taper_steps : TAPER_STEPS EQUAL vec_int { driver.option_vec_int("taper_steps", $3); };
+o_geweke_interval : GEWEKE_INTERVAL EQUAL vec_value { driver.option_num("geweke_interval",$3); };
o_constant : CONSTANT { driver.option_num("noconstant", "0"); };
o_noconstant : NOCONSTANT { driver.option_num("noconstant", "1"); };
o_mh_recover : MH_RECOVER { driver.option_num("mh_recover", "1"); };
diff --git a/preprocessor/DynareFlex.ll b/preprocessor/DynareFlex.ll
index e40450354..2024f625c 100644
--- a/preprocessor/DynareFlex.ll
+++ b/preprocessor/DynareFlex.ll
@@ -222,6 +222,8 @@ string eofbuff;
presample {return token::PRESAMPLE;}
lik_algo {return token::LIK_ALGO;}
lik_init {return token::LIK_INIT;}
+taper_steps {return token::TAPER_STEPS;}
+geweke_interval {return token::GEWEKE_INTERVAL;}
graph {return token::GRAPH;}
nograph {return token::NOGRAPH;}
nodisplay {return token::NODISPLAY;}