Verified Commit f5ec70a0 authored by Willi Mutschler's avatar Willi Mutschler Committed by Stéphane Adjemian
Browse files

MoM: Refactor AnScho test case

Instead of checking everything in one mod file, this commit separates the checks into individual mod files that test:
- whether the translation from matched_moments works
- whether the duplicate moments are found
- whether GMM and SMM both work with different estimated_params blocks.
wip
parent 31f3bfa3
......@@ -50,6 +50,7 @@ wsOct
!/ep/mean_preserving_spread.m
!/ep/rbcii_steady_state.m
!/estimation/fsdat_simul.m
!/estimation/method_of_moments/AnScho/AnScho_MoM_data_2.mat
!/estimation/method_of_moments/RBC/RBC_MoM_steady_helper.m
!/estimation/method_of_moments/RBC/RBC_Andreasen_Data_2.mat
!/estimation/method_of_moments/AFVRR/AFVRR_data.mat
......
......@@ -62,7 +62,13 @@ MODFILES = \
estimation/heteroskedastic_shocks/fs2000_het_corr.mod \
estimation/t_proposal/fs2000_student.mod \
estimation/tune_mh_jscale/fs2000.mod \
estimation/method_of_moments/AnScho/AnScho_MoM.mod \
estimation/method_of_moments/AnScho/AnScho_matched_moments.mod \
estimation/method_of_moments/AnScho/AnScho_GMM_estimParams0.mod \
estimation/method_of_moments/AnScho/AnScho_GMM_estimParams1.mod \
estimation/method_of_moments/AnScho/AnScho_GMM_estimParams2.mod \
estimation/method_of_moments/AnScho/AnScho_SMM_estimParams0.mod \
estimation/method_of_moments/AnScho/AnScho_SMM_estimParams1.mod \
estimation/method_of_moments/AnScho/AnScho_SMM_estimParams2.mod \
estimation/method_of_moments/RBC/RBC_MoM_Andreasen.mod \
estimation/method_of_moments/RBC/RBC_MoM_SMM_ME.mod \
estimation/method_of_moments/RBC/RBC_MoM_prefilter.mod \
......@@ -1275,6 +1281,8 @@ EXTRA_DIST = \
lmmcp/sw-common-header.inc \
lmmcp/sw-common-footer.inc \
estimation/tune_mh_jscale/fs2000.inc \
estimation/method_of_moments/AnScho/AnScho_MoM_common.inc \
estimation/method_of_moments/AnScho/AnScho_MoM_data_2.mat \
estimation/method_of_moments/RBC/RBC_MoM_common.inc \
estimation/method_of_moments/RBC/RBC_MoM_steady_helper.m \
estimation/method_of_moments/AFVRR/AFVRR_common.inc \
......
% Test estimated_params block using initial values without bounds
@#define estimParams = 0
@#define MoM_Method = "GMM"
@#include "AnScho_MoM_common.inc"
\ No newline at end of file
% Test estimated_params block using initial values and bounds
@#define estimParams = 1
@#define MoM_Method = "GMM"
@#include "AnScho_MoM_common.inc"
\ No newline at end of file
% Test estimated_params block using prior information
@#define estimParams = 2
@#define MoM_Method = "GMM"
@#include "AnScho_MoM_common.inc"
\ No newline at end of file
......@@ -20,13 +20,6 @@
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% =========================================================================
% Define testscenario
@#define orderApp = 2
@#define estimParams = 1
% Note that we set the numerical optimization tolerance levels very large to speed up the testsuite
@#define optimizer = 13
var c p R g y z INFL INT YGR;
varexo e_r e_g e_z;
parameters tau nu kap cyst psi1 psi2 rhor rhog rhoz rrst pist gamst;
......@@ -67,7 +60,7 @@ INT = pist+rrst+4*gamst+400*R;
end;
steady_state_model;
z = 0; p = 0; g = 0; r = 0; c = 0; y = 0;
z = 0; p = 0; g = 0; R = 0; c = 0; y = 0;
YGR = gamst; INFL = pist; INT = pist + rrst + 4*gamst;
end;
......@@ -139,148 +132,148 @@ end;
@#endif
% Simulate data
stoch_simul(order=@{orderApp},pruning,nodisplay,nomoments,periods=750,drop=500);
save('AnScho_MoM_data_@{orderApp}.mat', options_.varobs{:} );
pause(1);
% % Simulate data
% stoch_simul(order=2,pruning,nodisplay,nomoments,periods=750,drop=500);
% save('AnScho_MoM_data_2.mat', options_.varobs{:} );
% pause(1);
%--------------------------------------------------------------------------
% Method of Moments Estimation
%--------------------------------------------------------------------------
matched_moments;
%first-order product moments
YGR;
INFL;
INT;
YGR(-1); %redundant
YGR(0)^1; %redundant
YGR^1; %redundant
YGR^1*INFL^0*INT^0; %redundant
INFL(+2)^1;
INT(-2);
INT(2); %redundant
%second-order contemporenous product moments
YGR*YGR;
YGR*INFL;
YGR*INT;
INFL*INFL;
INFL*INT;
INT*INT;
YGR^2;
YGR(-1)^2; %redundant
YGR*YGR; %redundant
YGR(1)*YGR(1); %redundant
INFL*YGR;
YGR*INFL; %redundant
YGR(2)*INT(2);
INT(2)*YGR(2); %redundant
INT(2)^1*YGR(2)^1; %redundant
YGR(2)^1*INT(2)^1; %redundant
INFL(-2)^1*INFL(-2)^1;
INFL(-1)*INT(-1);
INT(0)^1*INT^1;
INT(0)^1*INT^1*YGR(2)^0*INT(-2)^0*INFL^0; %redundant
%second-order temporal product moments
YGR*YGR(-1);
INT*INT(-1);
INFL*INFL(-1);
end;
YGR(3)*YGR(2); %redundant
YGR(2)*YGR(3); %redundant
YGR(-2)*YGR(-1); %redundant
INT(3)^1*INT(-2)^1;
YGR(5)^0*INFL*INFL(2)^1;
YGR(5)^1*INFL(-3)^1;
INFL(-3)^1*YGR(5)^1; %redundant
INFL(3)*INT(2);
% get indices in declaration order
iYGR = strmatch('YGR', M_.endo_names,'exact');
iINFL = strmatch('INFL', M_.endo_names,'exact');
iINT = strmatch('INT', M_.endo_names,'exact');
% first entry: number of variable in declaration order
% second entry: lag
% third entry: power
matched_moments_ = {
%first-order product moments
[iYGR ] [0 ], [1 ];
[iINFL ] [0 ], [1 ];
[iINT ] [0 ], [1 ];
%second-order contemporenous product moments
[iYGR iYGR ] [0 0], [1 1];
[iYGR iINFL] [0 0], [1 1];
[iYGR iINT ] [0 0], [1 1];
[iINFL iINFL] [0 0], [1 1];
[iINFL iINT ] [0 0], [1 1];
[iINT iINT ] [0 0], [1 1];
%second-order temporal product moments
[iYGR iYGR ] [0 -1], [1 1];
%[iINT iYGR ] [0 -1], [1 1];
%[iINFL iYGR ] [0 -1], [1 1];
%[iYGR iINT ] [0 -1], [1 1];
[iINT iINT ] [0 -1], [1 1];
%[iINFL iINT ] [0 -1], [1 1];
%[iYGR iINFL] [0 -1], [1 1];
%[iINT iINFL] [0 -1], [1 1];
[iINFL iINFL] [0 -1], [1 1];
};
@#ifdef Extended_Matched_Moments_Checks
YGR^3;
YGR(-3)^2*YGR(-3); %redundant
YGR(-3)*YGR(-3)^2; %redundant
YGR(0)^1*YGR(0)*YGR; %redundant
INT(-2)^2*INFL(-1)^4;
INFL(1)^4*INT(0)^2; %redundant
INT(0)^2*INFL(1)^4; %redundant
YGR^2*INFL(-3)^4*INT(5)^6;
YGR^2*INT(5)^6*INFL(-3)^4;%redundant
INT(5)^6*YGR^2*INFL(-3)^4;%redundant
@#endif
end;
if ~isequal(M_.matched_moments,matched_moments_)
error('Translation to matched_moments-block failed')
end
@#for mommethod in ["GMM", "SMM"]
method_of_moments(
% Necessery options
mom_method = @{mommethod} % method of moments method; possible values: GMM|SMM
, datafile = 'AnScho_MoM_data_@{orderApp}.mat' % name of filename with data
method_of_moments(
% Necessery options
mom_method = @{MoM_Method} % method of moments method; possible values: GMM|SMM
, datafile = 'AnScho_MoM_data_2.mat' % name of filename with data
% Options for both GMM and SMM
% , bartlett_kernel_lag = 20 % bandwith in optimal weighting matrix
, order = @{orderApp} % order of Taylor approximation in perturbation
% , penalized_estimator % include deviation from prior mean as additional moment restriction and use prior precision as weight
, pruning % use pruned state space system at higher-order
% , verbose % display and store intermediate estimation results
, weighting_matrix = ['optimal'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename. Size of cell determines stages in iterated estimation, e.g. two state with ['DIAGONAL','OPTIMAL']
%, weighting_matrix_scaling_factor=1 % scaling of weighting matrix in objective function
, se_tolx=1e-6 % step size for numerical computation of standard errors
% Options for both GMM and SMM
% , bartlett_kernel_lag = 20 % bandwith in optimal weighting matrix
, order = 2 % order of Taylor approximation in perturbation
% , penalized_estimator % include deviation from prior mean as additional moment restriction and use prior precision as weight
, pruning % use pruned state space system at higher-order
, verbose % display and store intermediate estimation results
, weighting_matrix = ['optimal'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename. Size of cell determines stages in iterated estimation, e.g. two state with ['DIAGONAL','OPTIMAL']
%, weighting_matrix_scaling_factor=1 % scaling of weighting matrix in objective function
, se_tolx=1e-6 % step size for numerical computation of standard errors
% Options for SMM
% Options for SMM
% , burnin=500 % number of periods dropped at beginning of simulation
% , bounded_shock_support % trim shocks in simulation to +- 2 stdev
, bounded_shock_support % trim shocks in simulation to +- 2 stdev
% , seed = 24051986 % seed used in simulations
% , simulation_multiple = 5 % multiple of the data length used for simulation
% Options for GMM
@#if mommethod == "GMM"
, analytic_standard_errors % compute standard errors using analytical derivatives
@#endif
% General options
% , dirname = 'MM' % directory in which to store estimation output
% , graph_format = EPS % specify the file format(s) for graphs saved to disk
% , nodisplay % do not display the graphs, but still save them to disk
% , nograph % do not create graphs (which implies that they are not saved to the disk nor displayed)
% , noprint % do not print stuff to console
% , plot_priors = 1 % control plotting of priors
% , prior_trunc = 1e-10 % probability of extreme values of the prior density that is ignored when computing bounds for the parameters
% , TeX % print TeX tables and graphics
% Data and model options
% , first_obs = 501 % number of first observation
% , logdata % if data is already in logs
, nobs = 250 % number of observations
% , prefilter=0 % demean each data series by its empirical mean and use centered moments
% Options for GMM
@#if MoM_Method == "GMM"
, analytic_standard_errors % compute standard errors using analytical derivatives
@#endif
% General options
% , dirname = 'MM' % directory in which to store estimation output
% , graph_format = EPS % specify the file format(s) for graphs saved to disk
% , nodisplay % do not display the graphs, but still save them to disk
% , nograph % do not create graphs (which implies that they are not saved to the disk nor displayed)
% , noprint % do not print stuff to console
% , plot_priors = 1 % control plotting of priors
% , prior_trunc = 1e-10 % probability of extreme values of the prior density that is ignored when computing bounds for the parameters
% , TeX % print TeX tables and graphics
% , xls_sheet = data % name/number of sheet with data in Excel
% , xls_range = B2:D200 % range of data in Excel sheet
% Data and model options
% , first_obs = 501 % number of first observation
% , logdata % if data is already in logs
, nobs = 250 % number of observations
% , prefilter=0 % demean each data series by its empirical mean and use centered moments
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
% , huge_number=1e7 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
, mode_compute = @{optimizer} % specifies the optimizer for minimization of moments distance
, additional_optimizer_steps = [1] % vector of additional mode-finders run after mode_compute
% optim: a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute, some exemplary common options:
, optim = ('TolFun' , 1e-6 % termination tolerance on the function value, a positive scalar
,'TolX' , 1e-6 % termination tolerance on x, a positive scalar
,'MaxIter' , 3000 % maximum number of iterations allowed, a positive integer
,'MaxFunEvals' , 1D6 % maximum number of function evaluations allowed, a positive integer
% ,'UseParallel' , 1 % when true (and supported by optimizer) solver estimates gradients in parallel (using Matlab/Octave's parallel toolbox)
% ,'Jacobian' , 'off' % when 'off' gradient-based solvers approximate Jacobian using finite differences; for GMM we can also pass the analytical Jacobian to gradient-based solvers by setting this 'on'
)
, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
% , xls_sheet = data % name/number of sheet with data in Excel
% , xls_range = B2:D200 % range of data in Excel sheet
% Numerical algorithms options
% , aim_solver % Use AIM algorithm to compute perturbation approximation
% , k_order_solver % use k_order_solver in higher order perturbation approximations
% , dr=default % method used to compute the decision rule; possible values are DEFAULT, CYCLE_REDUCTION, LOGARITHMIC_REDUCTION
% , dr_cycle_reduction_tol = 1e-7 % convergence criterion used in the cycle reduction algorithm
% , dr_logarithmic_reduction_tol = 1e-12 % convergence criterion used in the logarithmic reduction algorithm
% , dr_logarithmic_reduction_maxiter = 100 % maximum number of iterations used in the logarithmic reduction algorithm
% , lyapunov = DEFAULT % algorithm used to solve lyapunov equations; possible values are DEFAULT, FIXED_POINT, DOUBLING, SQUARE_ROOT_SOLVER
% , lyapunov_complex_threshold = 1e-15 % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
% , lyapunov_fixed_point_tol = 1e-10 % convergence criterion used in the fixed point Lyapunov solver
% , lyapunov_doubling_tol = 1e-16 % convergence criterion used in the doubling algorithm
% , sylvester = default % algorithm to solve Sylvester equation; possible values are DEFAULT, FIXED_POINT
% , sylvester_fixed_point_tol = 1e-12 % convergence criterion used in the fixed point Sylvester solver
% , qz_criterium = 0.999999 % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
% , qz_zero_threshold = 1e-6 % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
% , schur_vec_tol=1e-11 % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix
% , mode_check % plot the target function for values around the computed minimum for each estimated parameter in turn
% , mode_check_neighbourhood_size = 5 % width of the window (expressed in percentage deviation) around the computed minimum to be displayed on the diagnostic plots
% , mode_check_symmetric_plots=1 % ensure that the check plots are symmetric around the minimum
% , mode_check_number_of_points = 20 % number of points around the minimum where the target function is evaluated (for each parameter)
);
@#endfor
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
% , huge_number=1e7 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
@#ifdef NoEstim
, mode_compute = 0
@#else
, mode_compute = 13 % specifies the optimizer for minimization of moments distance
, additional_optimizer_steps = [1] % vector of additional mode-finders run after mode_compute
@#endif
% optim: a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute, some exemplary common options:
, optim = ('TolFun' , 1e-6 % termination tolerance on the function value, a positive scalar
,'TolX' , 1e-6 % termination tolerance on x, a positive scalar
,'MaxIter' , 3000 % maximum number of iterations allowed, a positive integer
,'MaxFunEvals' , 1D6 % maximum number of function evaluations allowed, a positive integer
% ,'UseParallel' , 1 % when true (and supported by optimizer) solver estimates gradients in parallel (using Matlab/Octave's parallel toolbox)
% ,'Jacobian' , 'off' % when 'off' gradient-based solvers approximate Jacobian using finite differences; for GMM we can also pass the analytical Jacobian to gradient-based solvers by setting this 'on'
)
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
% Numerical algorithms options
% , aim_solver % Use AIM algorithm to compute perturbation approximation
% , k_order_solver % use k_order_solver in higher order perturbation approximations
% , dr=default % method used to compute the decision rule; possible values are DEFAULT, CYCLE_REDUCTION, LOGARITHMIC_REDUCTION
% , dr_cycle_reduction_tol = 1e-7 % convergence criterion used in the cycle reduction algorithm
% , dr_logarithmic_reduction_tol = 1e-12 % convergence criterion used in the logarithmic reduction algorithm
% , dr_logarithmic_reduction_maxiter = 100 % maximum number of iterations used in the logarithmic reduction algorithm
% , lyapunov = DEFAULT % algorithm used to solve lyapunov equations; possible values are DEFAULT, FIXED_POINT, DOUBLING, SQUARE_ROOT_SOLVER
% , lyapunov_complex_threshold = 1e-15 % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
% , lyapunov_fixed_point_tol = 1e-10 % convergence criterion used in the fixed point Lyapunov solver
% , lyapunov_doubling_tol = 1e-16 % convergence criterion used in the doubling algorithm
% , sylvester = default % algorithm to solve Sylvester equation; possible values are DEFAULT, FIXED_POINT
% , sylvester_fixed_point_tol = 1e-12 % convergence criterion used in the fixed point Sylvester solver
% , qz_criterium = 0.999999 % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
% , qz_zero_threshold = 1e-6 % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
% , schur_vec_tol=1e-11 % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix
, mode_check % plot the target function for values around the computed minimum for each estimated parameter in turn
% , mode_check_neighbourhood_size = 5 % width of the window (expressed in percentage deviation) around the computed minimum to be displayed on the diagnostic plots
% , mode_check_symmetric_plots=1 % ensure that the check plots are symmetric around the minimum
% , mode_check_number_of_points = 20 % number of points around the minimum where the target function is evaluated (for each parameter)
);
% Test estimated_params block using initial values without bounds
@#define estimParams = 0
@#define MoM_Method = "SMM"
@#include "AnScho_MoM_common.inc"
\ No newline at end of file
% Test estimated_params block using initial values and bounds
@#define estimParams = 1
@#define MoM_Method = "SMM"
@#include "AnScho_MoM_common.inc"
\ No newline at end of file
% Test estimated_params block using prior information
@#define estimParams = 2
@#define MoM_Method = "SMM"
@#include "AnScho_MoM_common.inc"
\ No newline at end of file
% Test translation from matched_moments block to M_.matched_moments
@#define estimParams = 0
@#define MoM_Method = "SMM"
@#define NoEstim = 1
@#define Extended_Matched_Moments_Checks
@#include "AnScho_MoM_common.inc"
% get indices in declaration order
iYGR = strmatch('YGR', M_.endo_names,'exact');
iINFL = strmatch('INFL', M_.endo_names,'exact');
iINT = strmatch('INT', M_.endo_names,'exact');
% M_.matched_moments has the following structure:
% - first entry: index number of variable in declaration order
% - second entry: lead or lag
% - third entry: power
matched_moments_orig = {
%first-order product moments
[iYGR ] [0 ], [1];
[iYGR ] [-1], [1];
[iYGR ] [0 ], [1];
[iYGR ] [0 ], [1];
[iYGR ] [0 ], [1];
[iINFL ] [2 ], [1];
[iINT ] [-2], [1];
[iINT ] [2 ], [1];
%second-order contemporenous product moments
[iYGR ] [0 ], [2 ];
[iYGR ] [-1 ], [2 ];
[iYGR iYGR ] [0 0], [1 1];
[iYGR iYGR ] [1 1], [1 1];
[iYGR iINFL] [0 0], [1 1];
[iYGR iINFL] [0 0], [1 1];
[iINT iYGR ] [2 2], [1 1];
[iINT iYGR ] [2 2], [1 1];
[iINT iYGR ] [2 2], [1 1];
[iINT iYGR ] [2 2], [1 1];
[iINFL iINFL] [-2 -2], [1 1];
[iINFL iINT ] [-1 -1], [1 1];
[iINT iINT ] [0 0], [1 1];
[iINT iINT ] [0 0], [1 1];
%second-order temporal product moments
[iYGR iYGR ] [0 -1], [1 1];
[iYGR iYGR ] [2 3], [1 1];
[iYGR iYGR ] [2 3], [1 1];
[iYGR iYGR ] [-1 -2], [1 1];
[iINT iINT ] [-2 3], [1 1];
[iINFL iINFL] [0 2], [1 1];
[iYGR iINFL] [5 -3], [1 1];
[iYGR iINFL] [5 -3], [1 1];
[iINT iINFL] [2 3], [1 1];
[iYGR ] [0 ], [3 ];
[iYGR iYGR ] [-3 -3 ], [1 2 ];
[iYGR iYGR ] [-3 -3 ], [1 2 ];
[iYGR iYGR iYGR] [0 0 0], [1 1 1];
[iINT iINFL ] [-2 -1 ], [2 4 ];
[iINFL iINT ] [1 0 ], [4 2 ];
[iINFL iINT ] [1 0 ], [4 2 ];
[iYGR iINFL iINT] [0 -3 5], [2 4 6];
[iINFL iYGR iINT] [-3 0 5], [4 2 6];
[iINFL iYGR iINT] [-3 0 5], [4 2 6];
};
% Removed duplicate moment conditions
matched_moments_no_duplicate= {
%first-order product moments
[iYGR ] [0 ], [1];
% [iYGR ] [-1], [1];
% [iYGR ] [0 ], [1];
% [iYGR ] [0 ], [1];
% [iYGR ] [0 ], [1];
[iINFL ] [2 ], [1];
[iINT ] [-2], [1];
% [iINT ] [2 ], [1];
%second-order contemporenous product moments
[iYGR ] [0 ], [2 ];
% [iYGR ] [-1 ], [2 ];
% [iYGR iYGR ] [0 0], [1 1];
% [iYGR iYGR ] [1 1], [1 1];
[iYGR iINFL] [0 0], [1 1];
% [iYGR iINFL] [0 0], [1 1];
[iINT iYGR ] [2 2], [1 1];
% [iINT iYGR ] [2 2], [1 1];
% [iINT iYGR ] [2 2], [1 1];
% [iINT iYGR ] [2 2], [1 1];
[iINFL iINFL] [-2 -2], [1 1];
[iINFL iINT ] [-1 -1], [1 1];
[iINT iINT ] [0 0], [1 1];
% [iINT iINT ] [0 0], [1 1];
%second-order temporal product moments
[iYGR iYGR ] [0 -1], [1 1];
% [iYGR iYGR ] [2 3], [1 1];
% [iYGR iYGR ] [2 3], [1 1];
% [iYGR iYGR ] [-1 -2], [1 1];
[iINT iINT ] [-2 3], [1 1];
[iINFL iINFL] [0 2], [1 1];
[iYGR iINFL] [5 -3], [1 1];
% [iYGR iINFL] [5 -3], [1 1];
[iINT iINFL] [2 3], [1 1];
[iYGR ] [0 ], [3 ];
% [iYGR iYGR ] [-3 -3 ], [1 2 ];
% [iYGR iYGR ] [-3 -3 ], [1 2 ];
% [iYGR iYGR iYGR] [0 0 0], [1 1 1];
[iINT iINFL ] [-2 -1 ], [2 4 ];
% [iINFL iINT ] [1 0 ], [4 2 ];
% [iINFL iINT ] [1 0 ], [4 2 ];
[iYGR iINFL iINT] [0 -3 5], [2 4 6];
% [iINFL iYGR iINT] [-3 0 5], [4 2 6];
% [iINFL iYGR iINT] [-3 0 5], [4 2 6];
};
if ~isequal(M_.matched_moments_orig,matched_moments_orig)
error('Translation to matched_moments-block failed!')
else
fprintf('Translation to matched_moments-block successful!\n\n')
end
if ~isequal(M_.matched_moments,matched_moments_no_duplicate)
error('Removal of duplicate moment conditions failed!')
else
fprintf('Removal of duplicate moment conditions was successful!\n\n')
end
\ No newline at end of file
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