MoM: Improve testsuite
- add Andreasen, Fernandez-Villaverde, Rubio-Ramirez (2017) test models - move models to dedicated folders - add `make m/method_of_moments` and `make o/method_of_moments` commands to run testsuite only for method of momentstime-shift
Willi Mutschler
parent
e2f16b504c
commit
562a9c737f
|
|
@ -50,8 +50,10 @@ wsOct
|
|||
!/ep/mean_preserving_spread.m
|
||||
!/ep/rbcii_steady_state.m
|
||||
!/estimation/fsdat_simul.m
|
||||
!/estimation/method_of_moments/RBC_MoM_steady_helper.m
|
||||
!/estimation/method_of_moments/RBC_Andreasen_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
|
||||
!/estimation/method_of_moments/AFVRR/AFVRR_steady_helper.m
|
||||
!/expectations/expectation_ss_old_steadystate.m
|
||||
!/external_function/extFunDeriv.m
|
||||
!/external_function/extFunNoDerivs.m
|
||||
|
|
|
|||
|
|
@ -50,10 +50,13 @@ MODFILES = \
|
|||
estimation/MH_recover/fs2000_recover_3.mod \
|
||||
estimation/t_proposal/fs2000_student.mod \
|
||||
estimation/tune_mh_jscale/fs2000.mod \
|
||||
estimation/method_of_moments/AnScho_MoM.mod \
|
||||
estimation/method_of_moments/RBC_MoM_Andreasen.mod \
|
||||
estimation/method_of_moments/RBC_MoM_SMM_ME.mod \
|
||||
estimation/method_of_moments/RBC_MoM_prefilter.mod \
|
||||
estimation/method_of_moments/AnScho/AnScho_MoM.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 \
|
||||
estimation/method_of_moments/AFVRR/AFVRR_M0.mod \
|
||||
estimation/method_of_moments/AFVRR/AFVRR_MFB.mod \
|
||||
estimation/method_of_moments/AFVRR/AFVRR_MFB_RRA.mod \
|
||||
moments/example1_var_decomp.mod \
|
||||
moments/example1_bp_test.mod \
|
||||
moments/test_AR1_spectral_density.mod \
|
||||
|
|
@ -835,6 +838,10 @@ particle: m/particle o/particle
|
|||
m/particle: $(patsubst %.mod, %.m.trs, $(PARTICLEFILES))
|
||||
o/particle: $(patsubst %.mod, %.o.trs, $(PARTICLEFILES))
|
||||
|
||||
method_of_moments: m/method_of_moments o/method_of_moments
|
||||
m/method_of_moments: $(patsubst %.mod, %.m.trs, $(filter estimation/method_of_moments/%.mod, $(MODFILES)))
|
||||
o/method_of_moments: $(patsubst %.mod, %.o.trs, $(filter estimation/method_of_moments/%.mod, $(MODFILES)))
|
||||
|
||||
# Matlab TRS Files
|
||||
M_TRS_FILES = $(patsubst %.mod, %.m.trs, $(MODFILES))
|
||||
M_TRS_FILES += run_block_byte_tests_matlab.m.trs \
|
||||
|
|
@ -984,8 +991,10 @@ EXTRA_DIST = \
|
|||
lmmcp/sw-common-header.inc \
|
||||
lmmcp/sw-common-footer.inc \
|
||||
estimation/tune_mh_jscale/fs2000.inc \
|
||||
estimation/method_of_moments/RBC_MoM_common.inc \
|
||||
estimation/method_of_moments/RBC_MoM_steady_helper.m \
|
||||
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 \
|
||||
estimation/method_of_moments/AFVRR/AFVRR_steady_helper.m \
|
||||
histval_initval_file_unit_tests.m \
|
||||
histval_initval_file/my_assert.m \
|
||||
histval_initval_file/ramst_data.xls \
|
||||
|
|
|
|||
|
|
@ -0,0 +1,299 @@
|
|||
% DSGE model based on replication files of
|
||||
% Andreasen, Fernandez-Villaverde, Rubio-Ramirez (2018), The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications, Review of Economic Studies, 85, p. 1-49
|
||||
% Adapted for Dynare by Willi Mutschler (@wmutschl, willi@mutschler.eu), Jan 2021
|
||||
% =========================================================================
|
||||
% Copyright (C) 2021 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 <http://www.gnu.org/licenses/>.
|
||||
% =========================================================================
|
||||
|
||||
% This is the benchmark model with no feedback M_0
|
||||
% Original code RunGMM_standardModel_RRA.m by Martin M. Andreasen, Jan 2016
|
||||
|
||||
@#include "AFVRR_common.inc"
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Parameter calibration taken from RunGMM_standardModel_RRA.m
|
||||
%--------------------------------------------------------------------------
|
||||
% fixed parameters
|
||||
INHABIT = 1;
|
||||
PHI1 = 4;
|
||||
PHI4 = 1;
|
||||
KAPAone = 0;
|
||||
DELTA = 0.025;
|
||||
THETA = 0.36;
|
||||
ETA = 6;
|
||||
CHI = 0;
|
||||
CONSxhr40 = 0;
|
||||
BETTAxhr = 0;
|
||||
BETTAxhr40= 0;
|
||||
RHOD = 0;
|
||||
GAMA = 0.9999;
|
||||
CONSxhr20 = 0;
|
||||
|
||||
% estimated parameters
|
||||
BETTA = 0.999544966118000;
|
||||
B = 0.668859504661000;
|
||||
H = 0.342483445196000;
|
||||
PHI2 = 0.997924964981000;
|
||||
RRA = 662.7953149595370;
|
||||
KAPAtwo = 5.516226495551000;
|
||||
ALFA = 0.809462321180000;
|
||||
RHOR = 0.643873352513000;
|
||||
BETTAPAI = 1.270087844103000;
|
||||
BETTAY = 0.031812764291000;
|
||||
MYYPS = 1.001189151180000;
|
||||
MYZ = 1.005286347928000;
|
||||
RHOA = 0.743239127127000;
|
||||
RHOG = 0.793929380230000;
|
||||
PAI = 1.012163659169000;
|
||||
GoY = 0.206594858866000;
|
||||
STDA = 0.016586292524000;
|
||||
STDG = 0.041220613851000;
|
||||
STDD = 0.013534473123000;
|
||||
|
||||
% endogenous parameters set via steady state, no need to initialize
|
||||
%PHIzero = ;
|
||||
%AA = ;
|
||||
%PHI3 = ;
|
||||
%negVf = ;
|
||||
|
||||
model_diagnostics;
|
||||
% Model diagnostics show that some parameters are endogenously determined
|
||||
% via the steady state, so we run steady to calibrate all parameters
|
||||
steady;
|
||||
model_diagnostics;
|
||||
% Now all parameters are determined
|
||||
|
||||
resid;
|
||||
check;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Shock distribution
|
||||
%--------------------------------------------------------------------------
|
||||
shocks;
|
||||
var eps_a = STDA^2;
|
||||
var eps_d = STDD^2;
|
||||
var eps_g = STDG^2;
|
||||
end;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Estimated Params block - these parameters will be estimated, we
|
||||
% initialize at calibrated values
|
||||
%--------------------------------------------------------------------------
|
||||
estimated_params;
|
||||
BETTA;
|
||||
B;
|
||||
H;
|
||||
PHI2;
|
||||
RRA;
|
||||
KAPAtwo;
|
||||
ALFA;
|
||||
RHOR;
|
||||
BETTAPAI;
|
||||
BETTAY;
|
||||
MYYPS;
|
||||
MYZ;
|
||||
RHOA;
|
||||
RHOG;
|
||||
PAI;
|
||||
GoY;
|
||||
stderr eps_a;
|
||||
stderr eps_g;
|
||||
stderr eps_d;
|
||||
end;
|
||||
|
||||
estimated_params_init(use_calibration);
|
||||
end;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Compare whether toolbox yields equivalent moments at second order
|
||||
%--------------------------------------------------------------------------
|
||||
% Note that we compare results for orderApp=1|2 and not for orderApp=3, because
|
||||
% there is a small error in the replication files of the original article in the
|
||||
% computation of the covariance matrix of the extended innovations vector.
|
||||
% The authors have been contacted, fixed it, and report that the results
|
||||
% change only slightly at orderApp=3 to what they report in the paper. At
|
||||
% orderApp=2 all is correct and so the following part tests whether we get
|
||||
% the same model moments at the calibrated parameters (we do not optimize).
|
||||
% We compare it to the replication file RunGMM_standardModel_RRA.m with the
|
||||
% following settings: orderApp=1|2, seOn=0, q_lag=10, weighting=1;
|
||||
% scaled=0; optimizer=0; estimator=1; momentSet=2;
|
||||
%
|
||||
% Output of the replication files for orderApp=1
|
||||
AndreasenEtAl.Q1 = 23893.072;
|
||||
AndreasenEtAl.moments1 =[ % note that we reshuffeled to be compatible with our matched moments block
|
||||
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023764' }
|
||||
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.028517' }
|
||||
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.048361' }
|
||||
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.073945' }
|
||||
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.073945' }
|
||||
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0' }
|
||||
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.577' }
|
||||
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.042861' }
|
||||
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0011816' }
|
||||
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0016052' }
|
||||
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00090947' }
|
||||
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0016016' }
|
||||
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0017076' }
|
||||
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0013997' }
|
||||
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0055317' }
|
||||
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00050106' }
|
||||
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0018178' }
|
||||
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0020186' }
|
||||
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0064471' }
|
||||
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0030519' }
|
||||
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0042181' }
|
||||
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0039217' }
|
||||
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0019975' }
|
||||
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0061403' }
|
||||
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0058343' }
|
||||
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'-0.00089501'}
|
||||
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0056883' }
|
||||
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'-0.00041184'}
|
||||
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.016255' }
|
||||
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4919' }
|
||||
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018384' }
|
||||
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00065543' }
|
||||
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0033626' }
|
||||
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0029033' }
|
||||
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.006112' }
|
||||
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.005683' }
|
||||
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'3.3307e-16' }
|
||||
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4912' }
|
||||
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018378' }
|
||||
];
|
||||
|
||||
% Output of the replication files for orderApp=2
|
||||
AndreasenEtAl.Q2 = 65.8269;
|
||||
AndreasenEtAl.moments2 =[ % note that we reshuffeled to be compatible with our matched moments block
|
||||
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023764' }
|
||||
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.028517' }
|
||||
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.034882' }
|
||||
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.056542' }
|
||||
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.070145' }
|
||||
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0.020825' }
|
||||
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.5748' }
|
||||
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.04335' }
|
||||
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.001205' }
|
||||
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0016067' }
|
||||
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00059406'}
|
||||
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0011949' }
|
||||
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0016104' }
|
||||
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0020245' }
|
||||
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0060254' }
|
||||
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'8.3563e-05'}
|
||||
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0013176' }
|
||||
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0019042' }
|
||||
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0064261' }
|
||||
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0020735' }
|
||||
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0027621' }
|
||||
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0029257' }
|
||||
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0012165'}
|
||||
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0040235' }
|
||||
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0044702' }
|
||||
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'0.00030542'}
|
||||
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0052718' }
|
||||
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'0.0010045' }
|
||||
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.018416' }
|
||||
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4853' }
|
||||
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018806' }
|
||||
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00067309'}
|
||||
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0033293' }
|
||||
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0019223' }
|
||||
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0039949' }
|
||||
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0052659' }
|
||||
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'0.0004337' }
|
||||
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4846' }
|
||||
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.00188' }
|
||||
];
|
||||
|
||||
@#for orderApp in 1:2
|
||||
|
||||
method_of_moments(
|
||||
mom_method = GMM % method of moments method; possible values: GMM|SMM
|
||||
, datafile = 'AFVRR_data.mat' % name of filename with data
|
||||
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
|
||||
, order = @{orderApp} % order of Taylor approximation in perturbation
|
||||
, pruning % use pruned state space system at higher-order
|
||||
% , verbose % display and store intermediate estimation results
|
||||
, weighting_matrix = ['DIAGONAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
|
||||
% , TeX % print TeX tables and graphics
|
||||
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
|
||||
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
|
||||
, mode_compute = 0 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
|
||||
, optim = ('TolFun', 1e-6
|
||||
,'TolX', 1e-6
|
||||
,'MaxIter', 3000
|
||||
,'MaxFunEvals', 1D6
|
||||
,'UseParallel' , 1
|
||||
%,'Jacobian' , 'on'
|
||||
) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
|
||||
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
|
||||
%, analytic_standard_errors
|
||||
, se_tolx=1e-10
|
||||
);
|
||||
|
||||
% Check results
|
||||
|
||||
fprintf('****************************************************************\n')
|
||||
fprintf('Compare Results for perturbation order @{orderApp}\n')
|
||||
fprintf('****************************************************************\n')
|
||||
dev_Q = AndreasenEtAl.Q@{orderApp} - oo_.mom.Q;
|
||||
dev_datamoments = str2double(AndreasenEtAl.moments@{orderApp}(:,5)) - oo_.mom.data_moments;
|
||||
dev_modelmoments = str2double(AndreasenEtAl.moments@{orderApp}(:,6)) - oo_.mom.model_moments;
|
||||
|
||||
table([AndreasenEtAl.Q@{orderApp} ; str2double(AndreasenEtAl.moments@{orderApp}(:,5)) ; str2double(AndreasenEtAl.moments@{orderApp}(:,6))],...
|
||||
[oo_.mom.Q ; oo_.mom.data_moments ; oo_.mom.model_moments ],...
|
||||
[dev_Q ; dev_datamoments ; dev_modelmoments ],...
|
||||
'VariableNames', {'Andreasen et al', 'Dynare', 'dev'},...
|
||||
'RowNames', ['Q'; strcat('Data_', M_.matched_moments(:,4)); strcat('Model_', M_.matched_moments(:,4))])
|
||||
|
||||
if norm(dev_modelmoments)> 1e-4
|
||||
error('Something wrong in the computation of moments at order @{orderApp}')
|
||||
end
|
||||
|
||||
@#endfor
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Replicate estimation at orderApp=3
|
||||
%--------------------------------------------------------------------------
|
||||
@#ifdef DoEstimation
|
||||
method_of_moments(
|
||||
mom_method = GMM % method of moments method; possible values: GMM|SMM
|
||||
, datafile = 'AFVRR_data.mat' % name of filename with data
|
||||
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
|
||||
, order = 3 % order of Taylor approximation in perturbation
|
||||
, pruning % use pruned state space system at higher-order
|
||||
% , verbose % display and store intermediate estimation results
|
||||
, weighting_matrix = ['DIAGONAL', 'OPTIMAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
|
||||
% , TeX % print TeX tables and graphics
|
||||
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
|
||||
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
|
||||
, mode_compute = 13 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
|
||||
, additional_optimizer_steps = [13]
|
||||
, optim = ('TolFun', 1e-6
|
||||
,'TolX', 1e-6
|
||||
,'MaxIter', 3000
|
||||
,'MaxFunEvals', 1D6
|
||||
,'UseParallel' , 1
|
||||
%,'Jacobian' , 'on'
|
||||
) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
|
||||
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
|
||||
%, analytic_standard_errors
|
||||
, se_tolx=1e-10
|
||||
);
|
||||
@#endif
|
||||
|
|
@ -0,0 +1,300 @@
|
|||
% DSGE model based on replication files of
|
||||
% Andreasen, Fernandez-Villaverde, Rubio-Ramirez (2018), The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications, Review of Economic Studies, 85, p. 1-49
|
||||
% Adapted for Dynare by Willi Mutschler (@wmutschl, willi@mutschler.eu), Jan 2021
|
||||
% =========================================================================
|
||||
% Copyright (C) 2021 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 <http://www.gnu.org/licenses/>.
|
||||
% =========================================================================
|
||||
|
||||
% This is the model with Feedback M_FB
|
||||
% Original code RunGMM_Feedback_estim_RRA.m by Martin M. Andreasen, Jan 2016
|
||||
|
||||
@#include "AFVRR_common.inc"
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Parameter calibration taken from RunGMM_Feedback_estim_RRA.m
|
||||
%--------------------------------------------------------------------------
|
||||
% fixed parameters
|
||||
INHABIT = 1;
|
||||
PHI1 = 4;
|
||||
PHI4 = 1;
|
||||
KAPAone = 0;
|
||||
DELTA = 0.025;
|
||||
THETA = 0.36;
|
||||
ETA = 6;
|
||||
CHI = 0;
|
||||
BETTAxhr = 0;
|
||||
BETTAxhr40= 0;
|
||||
RHOD = 0;
|
||||
GAMA = 0.9999;
|
||||
CONSxhr20 = 0;
|
||||
|
||||
% estimated parameters
|
||||
BETTA = 0.997007023687000;
|
||||
B = 0.692501768577000;
|
||||
H = 0.339214495653000;
|
||||
PHI2 = 0.688555040951000;
|
||||
RRA = 24.346514272871001;
|
||||
KAPAtwo = 10.018421876923000;
|
||||
ALFA = 0.792507553312000;
|
||||
RHOR = 0.849194030384000;
|
||||
BETTAPAI = 2.060579322980000;
|
||||
BETTAY = 0.220573712342000;
|
||||
MYYPS = 1.001016690133000;
|
||||
MYZ = 1.005356313981000;
|
||||
RHOA = 0.784141391843000;
|
||||
RHOG = 0.816924540497000;
|
||||
PAI = 1.011924196487000;
|
||||
CONSxhr40 = 0.878774662208000;
|
||||
GoY = 0.207110300602000;
|
||||
STDA = 0.013024450606000;
|
||||
STDG = 0.051049871928000;
|
||||
STDD = 0.008877423780000;
|
||||
|
||||
% endogenous parameters set via steady state, no need to initialize
|
||||
%PHIzero = ;
|
||||
%AA = ;
|
||||
%PHI3 = ;
|
||||
%negVf = ;
|
||||
|
||||
model_diagnostics;
|
||||
% Model diagnostics show that some parameters are endogenously determined
|
||||
% via the steady state, so we run steady to calibrate all parameters
|
||||
steady;
|
||||
model_diagnostics;
|
||||
% Now all parameters are determined
|
||||
|
||||
resid;
|
||||
check;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Shock distribution
|
||||
%--------------------------------------------------------------------------
|
||||
shocks;
|
||||
var eps_a = STDA^2;
|
||||
var eps_d = STDD^2;
|
||||
var eps_g = STDG^2;
|
||||
end;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Estimated Params block - these parameters will be estimated, we
|
||||
% initialize at calibrated values
|
||||
%--------------------------------------------------------------------------
|
||||
estimated_params;
|
||||
BETTA;
|
||||
B;
|
||||
H;
|
||||
PHI2;
|
||||
RRA;
|
||||
KAPAtwo;
|
||||
ALFA;
|
||||
RHOR;
|
||||
BETTAPAI;
|
||||
BETTAY;
|
||||
MYYPS;
|
||||
MYZ;
|
||||
RHOA;
|
||||
RHOG;
|
||||
PAI;
|
||||
CONSxhr40;
|
||||
GoY;
|
||||
stderr eps_a;
|
||||
stderr eps_g;
|
||||
stderr eps_d;
|
||||
end;
|
||||
|
||||
estimated_params_init(use_calibration);
|
||||
end;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Compare whether toolbox yields equivalent moments at second order
|
||||
%--------------------------------------------------------------------------
|
||||
% Note that we compare results for orderApp=1|2 and not for orderApp=3, because
|
||||
% there is a small error in the replication files of the original article in the
|
||||
% computation of the covariance matrix of the extended innovations vector.
|
||||
% The authors have been contacted, fixed it, and report that the results
|
||||
% change only slightly at orderApp=3 to what they report in the paper. At
|
||||
% orderApp=2 all is correct and so the following part tests whether we get
|
||||
% the same model moments at the calibrated parameters (we do not optimize).
|
||||
% We compare it to the replication file RunGMM_Feedback_estim_RRA.m with the
|
||||
% following settings: orderApp=1|2, seOn=0, q_lag=10, weighting=1;
|
||||
% scaled=0; optimizer=0; estimator=1; momentSet=2;
|
||||
%
|
||||
% Output of the replication files for orderApp=1
|
||||
AndreasenEtAl.Q1 = 201778.9697;
|
||||
AndreasenEtAl.moments1 =[ % note that we reshuffeled to be compatible with our matched moments block
|
||||
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023654' }
|
||||
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.027719' }
|
||||
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.047415' }
|
||||
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.083059' }
|
||||
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.083059' }
|
||||
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0' }
|
||||
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.5745' }
|
||||
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.043245' }
|
||||
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0012253' }
|
||||
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0015117' }
|
||||
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00080078' }
|
||||
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.00182' }
|
||||
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.001913' }
|
||||
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0016326' }
|
||||
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0040112' }
|
||||
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00060604' }
|
||||
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0021426' }
|
||||
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0022348' }
|
||||
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0039852' }
|
||||
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0030058' }
|
||||
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0044951' }
|
||||
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0042225' }
|
||||
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0021222' }
|
||||
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0074776' }
|
||||
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0071906' }
|
||||
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'-0.0006736' }
|
||||
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0070599' }
|
||||
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'-0.00036735'}
|
||||
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.014516' }
|
||||
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4866' }
|
||||
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018713' }
|
||||
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00076856' }
|
||||
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.002163' }
|
||||
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0028078' }
|
||||
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0074583' }
|
||||
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0070551' }
|
||||
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'7.2164e-16' }
|
||||
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4856' }
|
||||
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018708' }
|
||||
];
|
||||
|
||||
% Output of the replication files for orderApp=2
|
||||
AndreasenEtAl.Q2 = 59.3323;
|
||||
AndreasenEtAl.moments2 =[ % note that we reshuffeled to be compatible with our matched moments block
|
||||
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023654' }
|
||||
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.027719' }
|
||||
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.034565' }
|
||||
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.056419' }
|
||||
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.07087' }
|
||||
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0.01517' }
|
||||
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.5743' }
|
||||
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.043352' }
|
||||
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0012464' }
|
||||
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0015247' }
|
||||
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.0004867' }
|
||||
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0011867' }
|
||||
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0016146' }
|
||||
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0021395' }
|
||||
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0043272' }
|
||||
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00021752'}
|
||||
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0013919' }
|
||||
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0018899' }
|
||||
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0037854' }
|
||||
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0021043' }
|
||||
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0026571' }
|
||||
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0028566' }
|
||||
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0016279'}
|
||||
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0039136' }
|
||||
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0044118' }
|
||||
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'0.00016791'}
|
||||
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0052851' }
|
||||
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'0.00062143'}
|
||||
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.018126' }
|
||||
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4863' }
|
||||
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018806' }
|
||||
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00078586'}
|
||||
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0021519' }
|
||||
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0019046' }
|
||||
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0038939' }
|
||||
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0052792' }
|
||||
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'0.00023012'}
|
||||
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4852' }
|
||||
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018801' }
|
||||
];
|
||||
|
||||
@#for orderApp in 1:2
|
||||
|
||||
method_of_moments(
|
||||
mom_method = GMM % method of moments method; possible values: GMM|SMM
|
||||
, datafile = 'AFVRR_data.mat' % name of filename with data
|
||||
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
|
||||
, order = @{orderApp} % order of Taylor approximation in perturbation
|
||||
, pruning % use pruned state space system at higher-order
|
||||
% , verbose % display and store intermediate estimation results
|
||||
, weighting_matrix = ['DIAGONAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
|
||||
% , TeX % print TeX tables and graphics
|
||||
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
|
||||
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
|
||||
, mode_compute = 0 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
|
||||
, optim = ('TolFun', 1e-6
|
||||
,'TolX', 1e-6
|
||||
,'MaxIter', 3000
|
||||
,'MaxFunEvals', 1D6
|
||||
,'UseParallel' , 1
|
||||
%,'Jacobian' , 'on'
|
||||
) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
|
||||
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
|
||||
%, analytic_standard_errors
|
||||
, se_tolx=1e-10
|
||||
);
|
||||
|
||||
% Check results
|
||||
|
||||
fprintf('****************************************************************\n')
|
||||
fprintf('Compare Results for perturbation order @{orderApp}\n')
|
||||
fprintf('****************************************************************\n')
|
||||
dev_Q = AndreasenEtAl.Q@{orderApp} - oo_.mom.Q;
|
||||
dev_datamoments = str2double(AndreasenEtAl.moments@{orderApp}(:,5)) - oo_.mom.data_moments;
|
||||
dev_modelmoments = str2double(AndreasenEtAl.moments@{orderApp}(:,6)) - oo_.mom.model_moments;
|
||||
|
||||
table([AndreasenEtAl.Q@{orderApp} ; str2double(AndreasenEtAl.moments@{orderApp}(:,5)) ; str2double(AndreasenEtAl.moments@{orderApp}(:,6))],...
|
||||
[oo_.mom.Q ; oo_.mom.data_moments ; oo_.mom.model_moments ],...
|
||||
[dev_Q ; dev_datamoments ; dev_modelmoments ],...
|
||||
'VariableNames', {'Andreasen et al', 'Dynare', 'dev'},...
|
||||
'RowNames', ['Q'; strcat('Data_', M_.matched_moments(:,4)); strcat('Model_', M_.matched_moments(:,4))])
|
||||
|
||||
if norm(dev_modelmoments)> 1e-4
|
||||
warning('Something wrong in the computation of moments at order @{orderApp}')
|
||||
end
|
||||
|
||||
@#endfor
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Replicate estimation at orderApp=3
|
||||
%--------------------------------------------------------------------------
|
||||
@#ifdef DoEstimation
|
||||
method_of_moments(
|
||||
mom_method = GMM % method of moments method; possible values: GMM|SMM
|
||||
, datafile = 'AFVRR_data.mat' % name of filename with data
|
||||
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
|
||||
, order = 3 % order of Taylor approximation in perturbation
|
||||
, pruning % use pruned state space system at higher-order
|
||||
% , verbose % display and store intermediate estimation results
|
||||
, weighting_matrix = ['DIAGONAL', 'Optimal'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
|
||||
% , TeX % print TeX tables and graphics
|
||||
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
|
||||
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
|
||||
, mode_compute = 13 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
|
||||
, additional_optimizer_steps = [13]
|
||||
, optim = ('TolFun', 1e-6
|
||||
,'TolX', 1e-6
|
||||
,'MaxIter', 3000
|
||||
,'MaxFunEvals', 1D6
|
||||
,'UseParallel' , 1
|
||||
%,'Jacobian' , 'on'
|
||||
) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
|
||||
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
|
||||
%, analytic_standard_errors
|
||||
, se_tolx=1e-10
|
||||
);
|
||||
@#endif
|
||||
|
|
@ -0,0 +1,299 @@
|
|||
% DSGE model based on replication files of
|
||||
% Andreasen, Fernandez-Villaverde, Rubio-Ramirez (2018), The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications, Review of Economic Studies, 85, p. 1-49
|
||||
% Adapted for Dynare by Willi Mutschler (@wmutschl, willi@mutschler.eu), Jan 2021
|
||||
% =========================================================================
|
||||
% Copyright (C) 2021 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 <http://www.gnu.org/licenses/>.
|
||||
% =========================================================================
|
||||
|
||||
% This is the model with feedback and calibrated RRA
|
||||
% Original code RunGMM_Feedback_estim_RRA_5.m by Martin M. Andreasen, Jan 2016
|
||||
|
||||
@#include "AFVRR_common.inc"
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Parameter calibration taken from RunGMM_Feedback_estim_RRA_5.m
|
||||
%--------------------------------------------------------------------------
|
||||
% fixed parameters
|
||||
INHABIT = 1;
|
||||
PHI1 = 4;
|
||||
PHI4 = 1;
|
||||
KAPAone = 0;
|
||||
DELTA = 0.025;
|
||||
THETA = 0.36;
|
||||
ETA = 6;
|
||||
CHI = 0;
|
||||
BETTAxhr = 0;
|
||||
BETTAxhr40= 0;
|
||||
RHOD = 0;
|
||||
GAMA = 0.9999;
|
||||
CONSxhr20 = 0;
|
||||
RRA = 5;
|
||||
|
||||
% estimated parameters
|
||||
BETTA = 0.996850651147000;
|
||||
B = 0.684201133923000;
|
||||
H = 0.338754441432000;
|
||||
PHI2 = 0.738293581320000;
|
||||
KAPAtwo = 11.664785970704999;
|
||||
ALFA = 0.831836572237000;
|
||||
RHOR = 0.772754520116000;
|
||||
BETTAPAI = 3.020381242896000;
|
||||
BETTAY = 0.288367683973000;
|
||||
MYYPS = 1.000911709188000;
|
||||
MYZ = 1.005433723022000;
|
||||
RHOA = 0.749465413198000;
|
||||
RHOG = 0.847225569814000;
|
||||
PAI = 1.010428794858000;
|
||||
CONSxhr40 = 0.992863217133000;
|
||||
GoY = 0.207099399789000;
|
||||
STDA = 0.015621059978000;
|
||||
STDG = 0.047539390956000;
|
||||
STDD = 0.008623441943000;
|
||||
|
||||
% endogenous parameters set via steady state, no need to initialize
|
||||
%PHIzero = ;
|
||||
%AA = ;
|
||||
%PHI3 = ;
|
||||
%negVf = ;
|
||||
|
||||
model_diagnostics;
|
||||
% Model diagnostics show that some parameters are endogenously determined
|
||||
% via the steady state, so we run steady to calibrate all parameters
|
||||
steady;
|
||||
model_diagnostics;
|
||||
% Now all parameters are determined
|
||||
|
||||
resid;
|
||||
check;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Shock distribution
|
||||
%--------------------------------------------------------------------------
|
||||
shocks;
|
||||
var eps_a = STDA^2;
|
||||
var eps_d = STDD^2;
|
||||
var eps_g = STDG^2;
|
||||
end;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Estimated Params block - these parameters will be estimated, we
|
||||
% initialize at calibrated values
|
||||
%--------------------------------------------------------------------------
|
||||
estimated_params;
|
||||
BETTA;
|
||||
B;
|
||||
H;
|
||||
PHI2;
|
||||
KAPAtwo;
|
||||
ALFA;
|
||||
RHOR;
|
||||
BETTAPAI;
|
||||
BETTAY;
|
||||
MYYPS;
|
||||
MYZ;
|
||||
RHOA;
|
||||
RHOG;
|
||||
PAI;
|
||||
CONSxhr40;
|
||||
GoY;
|
||||
stderr eps_a;
|
||||
stderr eps_g;
|
||||
stderr eps_d;
|
||||
end;
|
||||
|
||||
estimated_params_init(use_calibration);
|
||||
end;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Compare whether toolbox yields equivalent moments at second order
|
||||
%--------------------------------------------------------------------------
|
||||
% Note that we compare results for orderApp=1|2 and not for orderApp=3, because
|
||||
% there is a small error in the replication files of the original article in the
|
||||
% computation of the covariance matrix of the extended innovations vector.
|
||||
% The authors have been contacted, fixed it, and report that the results
|
||||
% change only slightly at orderApp=3 to what they report in the paper. At
|
||||
% orderApp=2 all is correct and so the following part tests whether we get
|
||||
% the same model moments at the calibrated parameters (we do not optimize).
|
||||
% We compare it to the replication file RunGMM_Feedback_estim_RRA.m with the
|
||||
% following settings: orderApp=1|2, seOn=1, q_lag=10, weighting=1+1;
|
||||
% scaled=0; optimizer=0; estimator=1; momentSet=2;
|
||||
%
|
||||
% Output of the replication files for orderApp=1
|
||||
AndreasenEtAl.Q1 = 60275.3715;
|
||||
AndreasenEtAl.moments1 =[ % note that we reshuffeled to be compatible with our matched moments block
|
||||
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023726' }
|
||||
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.027372' }
|
||||
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.041499' }
|
||||
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.077843' }
|
||||
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.077843' }
|
||||
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0' }
|
||||
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.5746' }
|
||||
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.043299' }
|
||||
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0012763' }
|
||||
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0017759' }
|
||||
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00077354' }
|
||||
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0016538' }
|
||||
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0017949' }
|
||||
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.0017847' }
|
||||
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0053424' }
|
||||
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00064897' }
|
||||
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0019533' }
|
||||
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0020602' }
|
||||
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0064856' }
|
||||
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0020922' }
|
||||
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0036375' }
|
||||
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.0034139' }
|
||||
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.0011665' }
|
||||
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0066074' }
|
||||
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.0062959' }
|
||||
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'-0.00075499'}
|
||||
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0061801' }
|
||||
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'-0.00030456'}
|
||||
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.012048' }
|
||||
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4872' }
|
||||
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018759' }
|
||||
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00080528' }
|
||||
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0017036' }
|
||||
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0020185' }
|
||||
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0065788' }
|
||||
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0061762' }
|
||||
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'-4.5519e-15'}
|
||||
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4863' }
|
||||
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018755' }
|
||||
];
|
||||
|
||||
% Output of the replication files for orderApp=2
|
||||
AndreasenEtAl.Q2 = 140.8954;
|
||||
AndreasenEtAl.moments2 =[ % note that we reshuffeled to be compatible with our matched moments block
|
||||
{[ 1]} {'Ex' } {'Gr_C '} {' ' } {'0.024388' } {'0.023726' }
|
||||
{[ 2]} {'Ex' } {'Gr_I '} {' ' } {'0.031046' } {'0.027372' }
|
||||
{[ 3]} {'Ex' } {'Infl ' } {' ' } {'0.03757' } {'0.034618' }
|
||||
{[ 4]} {'Ex' } {'r1 ' } {' ' } {'0.056048' } {'0.056437' }
|
||||
{[ 5]} {'Ex' } {'r40 ' } {' ' } {'0.069929' } {'0.07051' }
|
||||
{[ 6]} {'Ex' } {'xhr40 '} {' ' } {'0.017237' } {'0.014242' }
|
||||
{[ 7]} {'Ex' } {'GoY '} {' ' } {'-1.5745' } {'-1.574' }
|
||||
{[ 8]} {'Ex' } {'hours '} {' ' } {'-0.043353' } {'-0.043351' }
|
||||
{[ 9]} {'Exx' } {'Gr_C '} {'Gr_C '} {'0.0013159' } {'0.0012917' }
|
||||
{[17]} {'Exx' } {'Gr_C '} {'Gr_I '} {'0.0021789' } {'0.0017862' }
|
||||
{[18]} {'Exx' } {'Gr_C '} {'Infl ' } {'0.00067495' } {'0.00061078' }
|
||||
{[19]} {'Exx' } {'Gr_C '} {'r1 ' } {'0.0011655' } {'0.0011494' }
|
||||
{[20]} {'Exx' } {'Gr_C '} {'r40 ' } {'0.0015906' } {'0.0016149' }
|
||||
{[21]} {'Exx' } {'Gr_C '} {'xhr40 '} {'0.0020911' } {'0.002203' }
|
||||
{[10]} {'Exx' } {'Gr_I '} {'Gr_I '} {'0.0089104' } {'0.0054317' }
|
||||
{[22]} {'Exx' } {'Gr_I '} {'Infl ' } {'0.00063139' } {'0.00045278' }
|
||||
{[23]} {'Exx' } {'Gr_I '} {'r1 ' } {'0.0011031' } {'0.0013672' }
|
||||
{[24]} {'Exx' } {'Gr_I '} {'r40 ' } {'0.0018445' } {'0.0018557' }
|
||||
{[25]} {'Exx' } {'Gr_I '} {'xhr40 '} {'0.00095556' } {'0.0067742' }
|
||||
{[11]} {'Exx' } {'Infl ' } {'Infl ' } {'0.0020268' } {'0.0016583' }
|
||||
{[26]} {'Exx' } {'Infl ' } {'r1 ' } {'0.0025263' } {'0.0024521' }
|
||||
{[27]} {'Exx' } {'Infl ' } {'r40 ' } {'0.0029126' } {'0.002705' }
|
||||
{[28]} {'Exx' } {'Infl ' } {'xhr40 '} {'-0.00077101'} {'-0.00065007'}
|
||||
{[12]} {'Exx' } {'r1 ' } {'r1 ' } {'0.0038708' } {'0.0038274' }
|
||||
{[29]} {'Exx' } {'r1 ' } {'r40 ' } {'0.0044773' } {'0.004297' }
|
||||
{[30]} {'Exx' } {'r1 ' } {'xhr40 '} {'-0.00048202'} {'6.3243e-05' }
|
||||
{[13]} {'Exx' } {'r40 ' } {'r40 ' } {'0.0054664' } {'0.0051686' }
|
||||
{[31]} {'Exx' } {'r40 ' } {'xhr40 '} {'0.00053864' } {'0.00066645' }
|
||||
{[14]} {'Exx' } {'xhr40 '} {'xhr40 '} {'0.053097' } {'0.013543' }
|
||||
{[15]} {'Exx' } {'GoY '} {'GoY '} {'2.4863' } {'2.4858' }
|
||||
{[16]} {'Exx' } {'hours '} {'hours '} {'0.0018799' } {'0.0018804' }
|
||||
{[32]} {'Exx1'} {'Gr_C '} {'Gr_C '} {'0.00077917' } {'0.00081772' }
|
||||
{[33]} {'Exx1'} {'Gr_I '} {'Gr_I '} {'0.0050104' } {'0.0017106' }
|
||||
{[34]} {'Exx1'} {'Infl ' } {'Infl ' } {'0.0019503' } {'0.0015835' }
|
||||
{[35]} {'Exx1'} {'r1 ' } {'r1 ' } {'0.0038509' } {'0.0037985' }
|
||||
{[36]} {'Exx1'} {'r40 ' } {'r40 ' } {'0.0054699' } {'0.0051642' }
|
||||
{[37]} {'Exx1'} {'xhr40 '} {'xhr40 '} {'-0.00098295'} {'0.00020285' }
|
||||
{[38]} {'Exx1'} {'GoY '} {'GoY '} {'2.4868' } {'2.4848' }
|
||||
{[39]} {'Exx1'} {'hours '} {'hours '} {'0.0018799' } {'0.0018799' }
|
||||
];
|
||||
|
||||
@#for orderApp in 1:2
|
||||
|
||||
method_of_moments(
|
||||
mom_method = GMM % method of moments method; possible values: GMM|SMM
|
||||
, datafile = 'AFVRR_data.mat' % name of filename with data
|
||||
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
|
||||
, order = @{orderApp} % order of Taylor approximation in perturbation
|
||||
, pruning % use pruned state space system at higher-order
|
||||
% , verbose % display and store intermediate estimation results
|
||||
, weighting_matrix = ['DIAGONAL'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
|
||||
% , TeX % print TeX tables and graphics
|
||||
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
|
||||
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
|
||||
, mode_compute = 0 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
|
||||
, optim = ('TolFun', 1e-6
|
||||
,'TolX', 1e-6
|
||||
,'MaxIter', 3000
|
||||
,'MaxFunEvals', 1D6
|
||||
,'UseParallel' , 1
|
||||
%,'Jacobian' , 'on'
|
||||
) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
|
||||
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
|
||||
%, analytic_standard_errors
|
||||
, se_tolx=1e-10
|
||||
);
|
||||
|
||||
% Check results
|
||||
|
||||
fprintf('****************************************************************\n')
|
||||
fprintf('Compare Results for perturbation order @{orderApp}\n')
|
||||
fprintf('****************************************************************\n')
|
||||
dev_Q = AndreasenEtAl.Q@{orderApp} - oo_.mom.Q;
|
||||
dev_datamoments = str2double(AndreasenEtAl.moments@{orderApp}(:,5)) - oo_.mom.data_moments;
|
||||
dev_modelmoments = str2double(AndreasenEtAl.moments@{orderApp}(:,6)) - oo_.mom.model_moments;
|
||||
|
||||
table([AndreasenEtAl.Q@{orderApp} ; str2double(AndreasenEtAl.moments@{orderApp}(:,5)) ; str2double(AndreasenEtAl.moments@{orderApp}(:,6))],...
|
||||
[oo_.mom.Q ; oo_.mom.data_moments ; oo_.mom.model_moments ],...
|
||||
[dev_Q ; dev_datamoments ; dev_modelmoments ],...
|
||||
'VariableNames', {'Andreasen et al', 'Dynare', 'dev'},...
|
||||
'RowNames', ['Q'; strcat('Data_', M_.matched_moments(:,4)); strcat('Model_', M_.matched_moments(:,4))])
|
||||
|
||||
if norm(dev_modelmoments)> 1e-4
|
||||
warning('Something wrong in the computation of moments at order @{orderApp}')
|
||||
end
|
||||
|
||||
@#endfor
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Replicate estimation at orderApp=3
|
||||
%--------------------------------------------------------------------------
|
||||
@#ifdef DoEstimation
|
||||
method_of_moments(
|
||||
mom_method = GMM % method of moments method; possible values: GMM|SMM
|
||||
, datafile = 'AFVRR_data.mat' % name of filename with data
|
||||
, bartlett_kernel_lag = 10 % bandwith in optimal weighting matrix
|
||||
, order = 3 % order of Taylor approximation in perturbation
|
||||
, pruning % use pruned state space system at higher-order
|
||||
% , verbose % display and store intermediate estimation results
|
||||
, weighting_matrix = ['DIAGONAL', 'Optimal'] % weighting matrix in moments distance objective function; possible values: OPTIMAL|IDENTITY_MATRIX|DIAGONAL|filename
|
||||
% , TeX % print TeX tables and graphics
|
||||
% Optimization options that can be set by the user in the mod file, otherwise default values are provided
|
||||
%, huge_number=1D10 % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
|
||||
, mode_compute = 13 % specifies the optimizer for minimization of moments distance, note that by default there is a new optimizer
|
||||
, additional_optimizer_steps = [13]
|
||||
, optim = ('TolFun', 1e-6
|
||||
,'TolX', 1e-6
|
||||
,'MaxIter', 3000
|
||||
,'MaxFunEvals', 1D6
|
||||
,'UseParallel' , 1
|
||||
%,'Jacobian' , 'on'
|
||||
) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
|
||||
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
|
||||
%, analytic_standard_errors
|
||||
, se_tolx=1e-10
|
||||
);
|
||||
@#endif
|
||||
|
|
@ -0,0 +1,540 @@
|
|||
% DSGE model based on replication files of
|
||||
% Andreasen, Fernandez-Villaverde, Rubio-Ramirez (2018), The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications, Review of Economic Studies, 85, p. 1-49
|
||||
% Original code by Martin M. Andreasen, Jan 2016
|
||||
% Adapted for Dynare by Willi Mutschler (@wmutschl, willi@mutschler.eu), Jan 2021
|
||||
% =========================================================================
|
||||
% Copyright (C) 2021 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 <http://www.gnu.org/licenses/>.
|
||||
% =========================================================================
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Variable declaration
|
||||
%--------------------------------------------------------------------------
|
||||
var
|
||||
ln_k
|
||||
ln_s
|
||||
ln_a
|
||||
ln_g
|
||||
ln_d
|
||||
|
||||
ln_c
|
||||
ln_r
|
||||
ln_pai
|
||||
ln_h
|
||||
ln_q
|
||||
ln_evf
|
||||
ln_iv
|
||||
ln_x2
|
||||
ln_la
|
||||
ln_goy
|
||||
ln_Esdf
|
||||
|
||||
xhr20
|
||||
xhr40
|
||||
Exhr
|
||||
|
||||
@#for i in 1:40
|
||||
ln_p@{i}
|
||||
@#endfor
|
||||
|
||||
Obs_Gr_C
|
||||
Obs_Gr_I
|
||||
Obs_Infl
|
||||
Obs_r1
|
||||
Obs_r40
|
||||
Obs_xhr40
|
||||
Obs_GoY
|
||||
Obs_hours
|
||||
;
|
||||
|
||||
predetermined_variables ln_k ln_s;
|
||||
|
||||
varobs Obs_Gr_C Obs_Gr_I Obs_Infl Obs_r1 Obs_r40 Obs_xhr40 Obs_GoY Obs_hours;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Exogenous shocks
|
||||
%--------------------------------------------------------------------------
|
||||
varexo
|
||||
eps_a
|
||||
eps_d
|
||||
eps_g
|
||||
;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Parameter declaration
|
||||
%--------------------------------------------------------------------------
|
||||
parameters
|
||||
BETTA
|
||||
B
|
||||
INHABIT
|
||||
H
|
||||
PHI1
|
||||
PHI2
|
||||
RRA
|
||||
PHI4
|
||||
KAPAone
|
||||
KAPAtwo
|
||||
DELTA
|
||||
THETA
|
||||
ETA
|
||||
ALFA
|
||||
CHI
|
||||
RHOR
|
||||
BETTAPAI
|
||||
BETTAY
|
||||
MYYPS
|
||||
MYZ
|
||||
RHOA
|
||||
%STDA
|
||||
RHOG
|
||||
%STDG
|
||||
RHOD
|
||||
%STDD
|
||||
CONSxhr40
|
||||
BETTAxhr
|
||||
BETTAxhr40
|
||||
CONSxhr20
|
||||
PAI
|
||||
GAMA
|
||||
GoY
|
||||
|
||||
%auxiliary
|
||||
PHIzero
|
||||
AA
|
||||
PHI3
|
||||
negVf
|
||||
;
|
||||
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Model equations
|
||||
%--------------------------------------------------------------------------
|
||||
% Based on DSGE_model_NegVf_yieldCurve.m and DSGE_model_PosVf_yieldCurve.m
|
||||
% Note that we include an auxiliary parameter negVf to distinguish whether
|
||||
% the steady state value function is positive (negVf=0) or negative (negVf=1).
|
||||
% This parameter is endogenously determined in the steady_state_model block.
|
||||
|
||||
model;
|
||||
%--------------------------------------------------------------------------
|
||||
% Auxiliary expressions
|
||||
%--------------------------------------------------------------------------
|
||||
% do exp transform such that variables are logged variables
|
||||
@#for var in [ "k", "s", "c", "r", "a", "g", "d", "pai", "h", "q", "evf", "iv", "x2", "la", "goy", "Esdf" ]
|
||||
#@{var}_ba1 = exp(ln_@{var}(-1));
|
||||
#@{var}_cu = exp(ln_@{var});
|
||||
#@{var}_cup = exp(ln_@{var}(+1));
|
||||
@#endfor
|
||||
@#for i in 1:40
|
||||
#p@{i}_cu = exp(ln_p@{i});
|
||||
#p@{i}_cup = exp(ln_p@{i}(+1));
|
||||
@#endfor
|
||||
% these variables are not transformed
|
||||
#xhr20_cu = xhr20;
|
||||
#xhr20_cup = xhr20(+1);
|
||||
#xhr40_cu = xhr40;
|
||||
#xhr40_cup = xhr40(+1);
|
||||
#Exhr_cu = Exhr;
|
||||
#Exhr_cup = Exhr(+1);
|
||||
|
||||
% auxiliary steady state variables
|
||||
#K = exp(steady_state(ln_k));
|
||||
#IV = exp(steady_state(ln_iv));
|
||||
#C = exp(steady_state(ln_c));
|
||||
#Y = (C + IV)/(1-GoY);
|
||||
#R = exp(steady_state(ln_r));
|
||||
#G = Y-C-IV;
|
||||
|
||||
#removeMeanXhr = 1;
|
||||
|
||||
% The atemporal relations if possible
|
||||
% No stochastic trend in investment specific shocks
|
||||
#myyps_cu = MYYPS;
|
||||
#myyps_cup = MYYPS;
|
||||
|
||||
% No stochastic trend in non-stationary technology shocks
|
||||
#myz_cu = MYZ;
|
||||
#myz_cup = MYZ;
|
||||
|
||||
% Defining myzstar
|
||||
#MYZSTAR = MYYPS^(THETA/(1-THETA))*MYZ;
|
||||
#myzstar_cu = myyps_cu ^(THETA/(1-THETA))*myz_cu;
|
||||
#myzstar_cup= myyps_cup^(THETA/(1-THETA))*myz_cup;
|
||||
|
||||
% The expression for the value function (only valid for deterministic trends!)
|
||||
% Note that we make use of auxiliary parameter negVf to switch signs
|
||||
#mvf_cup = -negVf*(d_cup/(1-PHI2)*((c_cup-B*c_cu*MYZSTAR^-1)^(1-PHI2)-1) + d_cup*PHIzero/(1-PHI1)*(1-h_cup)^(1-PHI1) - negVf* BETTA*MYZSTAR^((1-PHI4)*(1-PHI2))*AA*evf_cup^(1/(1-PHI3)));
|
||||
|
||||
% The growth rate in lambda
|
||||
#myla_cup = (la_cup/la_cu)*(AA*evf_cu^(1/(1-PHI3))/mvf_cup)^PHI3*myzstar_cup^(-PHI2*(1-PHI4)-PHI4);
|
||||
|
||||
% The relation between the optimal price for the firms and the pris and inflation
|
||||
%ptil_cu = ((1-ALFA*(pai_ba1^CHI/pai_cu )^(1-ETA))/(1-ALFA))^(1/(1-ETA));
|
||||
%ptil_cup = ((1-ALFA*(pai_cu ^CHI/pai_cup)^(1-ETA))/(1-ALFA))^(1/(1-ETA));
|
||||
#ptil_cu = ((1-ALFA*(1/pai_cu )^(1-ETA))/(1-ALFA))^(1/(1-ETA));
|
||||
#ptil_cup = ((1-ALFA*(1/pai_cup)^(1-ETA))/(1-ALFA))^(1/(1-ETA));
|
||||
|
||||
% From the households' FOC for labor
|
||||
#w_cu = d_cu*PHIzero*(1-h_cu )^(-PHI1)/la_cu;
|
||||
#w_cup = d_cu*PHIzero*(1-h_cup)^(-PHI1)/la_cup;
|
||||
% Shouldn't w_cup include d_cup? Let's stick to the original (wrong) code in the replication files as results don't change dramatically... [@wmutschl]
|
||||
|
||||
% The firms' FOC for labor
|
||||
#mc_cu = w_cu /((1-THETA)*a_cu *myyps_cu ^(-THETA/(1-THETA))*myz_cu ^-THETA *k_cu ^THETA*h_cu ^(-THETA));
|
||||
#mc_cup = w_cup/((1-THETA)*a_cup*myyps_cup^(-THETA/(1-THETA))*myz_cup^-THETA *k_cup^THETA*h_cup^(-THETA));
|
||||
|
||||
% The firms' FOC for capital
|
||||
#rk_cu = mc_cu *THETA* a_cu *myyps_cu *myz_cu ^(1-THETA)*k_cu ^(THETA-1)*h_cu ^(1-THETA);
|
||||
#rk_cup = mc_cup*THETA* a_cup*myyps_cup*myz_cup^(1-THETA)*k_cup^(THETA-1)*h_cup^(1-THETA);
|
||||
|
||||
% The income identity
|
||||
#y_cu = c_cu + iv_cu + g_cu;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Actual model equations
|
||||
%--------------------------------------------------------------------------
|
||||
|
||||
[name='Expected value of the value function']
|
||||
0 = -evf_cu + (mvf_cup/AA)^(1-PHI3);
|
||||
|
||||
[name='Households FOC for capital']
|
||||
0 = -q_cu+BETTA*myla_cup/myyps_cup*(rk_cup+q_cup*(1-DELTA) -q_cup*KAPAtwo/2*(iv_cup/k_cup*myyps_cup*myzstar_cup - IV/K*MYYPS*MYZSTAR)^2 +q_cup*KAPAtwo*(iv_cup/k_cup*myyps_cup*myzstar_cup - IV/K*MYYPS*MYZSTAR)*iv_cup/k_cup*myyps_cup*myzstar_cup);
|
||||
|
||||
[name='Households FOC for investments']
|
||||
0 = -1+q_cu*(1-KAPAone/2*(iv_cu/IV-1)^2-iv_cu/IV*KAPAone*(iv_cu/IV-1)-KAPAtwo*(iv_cu/k_cu*myyps_cu*myzstar_cu - IV/K*MYYPS*MYZSTAR));
|
||||
|
||||
[name='Euler equation for consumption']
|
||||
0 = -1+BETTA*r_cu*exp(CONSxhr40*xhr40_cu + CONSxhr20*xhr20_cu)*myla_cup/pai_cup;
|
||||
|
||||
[name='Households FOC for consumption']
|
||||
0 = -la_cu + d_cu*(c_cu -B*c_ba1*myzstar_cu^-1)^(-PHI2) -INHABIT*B*BETTA*d_cup*(AA*evf_cu^(1/(1-PHI3))/mvf_cup)^PHI3*(c_cup -B*c_cu*myzstar_cup^-1)^(-PHI2)*myzstar_cup^(-PHI2*(1-PHI4)-PHI4);
|
||||
|
||||
[name='Nonlinear pricing, relation for x1 = (ETA-1)/ETA*x2']
|
||||
0= -(ETA-1)/ETA*x2_cu+y_cu*mc_cu*ptil_cu^(-ETA-1) +ALFA*BETTA*myla_cup*(ptil_cu/ptil_cup)^(-ETA-1)*(1/pai_cup)^(-ETA)*(ETA-1)/ETA*x2_cup*myzstar_cup;
|
||||
|
||||
[name='Nonlinear pricing, relation for x2']
|
||||
0=-x2_cu+y_cu*ptil_cu^-ETA +ALFA*BETTA*myla_cup*(ptil_cu/ptil_cup)^(-ETA)*(1/pai_cup)^(1-ETA)*x2_cup*myzstar_cup;
|
||||
|
||||
[name='Nonlinear pricing, relation for s']
|
||||
0= -s_cup+(1-ALFA)*ptil_cu^(-ETA)+ALFA*(pai_cu/1)^ETA*s_cu;
|
||||
|
||||
[name='Interest rate rule']
|
||||
0 = -log(r_cu/R)+RHOR*log(r_ba1/R)+(1-RHOR)*(BETTAPAI*log(pai_cu/PAI)+BETTAY*log(y_cu/Y) + BETTAxhr*(BETTAxhr40*xhr40_cu - removeMeanXhr*Exhr_cu));
|
||||
|
||||
[name='Production function']
|
||||
0 = -y_cu*s_cup + a_cu *(k_cu *myyps_cu ^(-1/(1-THETA))*myz_cu ^-1)^THETA*h_cu ^(1-THETA);
|
||||
|
||||
[name='Relation for physical capital stock']
|
||||
0= -k_cup + (1-DELTA)*k_cu*(myyps_cu*myzstar_cu)^-1 + iv_cu - iv_cu*KAPAone/2*(iv_cu/IV-1)^2 - k_cu*(myyps_cu*myzstar_cu)^-1*KAPAtwo/2*(iv_cu/k_cu*myyps_cu*myzstar_cu - IV/K*MYYPS*MYZSTAR)^2;
|
||||
|
||||
[name='Goverment spending over output']
|
||||
0=-goy_cu + g_cu/y_cu;
|
||||
|
||||
[name='The yield curve: p1']
|
||||
0= -p1_cu + 1/r_cu;
|
||||
|
||||
@#for i in 2:40
|
||||
[name='The yield curve: p@{i}']
|
||||
0= -p@{i}_cu + BETTA*myla_cup/pai_cup*p@{i-1}_cup;
|
||||
@#endfor
|
||||
|
||||
[name='Stochastic discount factor']
|
||||
0= -Esdf_cu+ BETTA*myla_cup/pai_cup;
|
||||
|
||||
[name='Expected 5 year excess holding period return']
|
||||
0= -xhr20_cu+ log(p19_cup) - log(p20_cu) - log(r_cu);
|
||||
|
||||
[name='Expected 10 year excess holding period return']
|
||||
0= -xhr40_cu+ log(p39_cup) - log(p40_cu) - log(r_cu);
|
||||
|
||||
[name='Mean of expected excess holding period return in Taylor rule']
|
||||
0= -Exhr_cu + (1-GAMA)*(BETTAxhr40*xhr40_cu) + GAMA*Exhr_cup;
|
||||
|
||||
[name='Exogenous process for productivity']
|
||||
0 = -log(a_cu)+RHOA*log(a_ba1) + eps_a;
|
||||
|
||||
[name='Exogenous process for government spending']
|
||||
0 = -log(g_cu/G)+RHOG*log(g_ba1/G) + eps_g;
|
||||
|
||||
[name='Exogenous process for discount factor shifter']
|
||||
0 = -log(d_cu)+RHOD*log(d_ba1) + eps_d;
|
||||
|
||||
[name='Observable annualized consumption growth']
|
||||
Obs_Gr_C = 4*( ln_c -ln_c(-1) + log(MYZSTAR));
|
||||
|
||||
[name='Observable annualized investment growth']
|
||||
Obs_Gr_I = 4*( ln_iv - ln_iv(-1) + log(MYZSTAR)+log(MYYPS));
|
||||
|
||||
[name='Observable annualized inflation']
|
||||
Obs_Infl = 4*( ln_pai);
|
||||
|
||||
[name='Observable annualized one-quarter nominal yield']
|
||||
Obs_r1 = 4*( ln_r);
|
||||
|
||||
[name='Observable annualized 10-year nominal yield']
|
||||
Obs_r40 = 4*( -1/40*ln_p40);
|
||||
|
||||
[name='Observable annualized 10-year ex post excess holding period return']
|
||||
Obs_xhr40 = 4*( ln_p39 - ln_p40(-1) - ln_r(-1) );
|
||||
|
||||
[name='Observable annualized log ratio of government spending to GDP']
|
||||
Obs_GoY = 4*( 1/4*ln_goy);
|
||||
|
||||
[name='Observable annualized log of hours']
|
||||
Obs_hours = 4*( 1/100*ln_h);
|
||||
end;
|
||||
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Steady State Computations
|
||||
%--------------------------------------------------------------------------
|
||||
% Based on DSGE_model_yieldCurve_ss.m, getPHI3.m, ObjectGMM.m
|
||||
% Note that we include an auxiliary parameter negVf to distinguish whether
|
||||
% the steady state value function is positive (negVf=0) or negative (negVf=1).
|
||||
% This parameter is endogenously determined in the steady_state_model block.
|
||||
|
||||
|
||||
steady_state_model;
|
||||
|
||||
% The growth rate in the firms' fixed costs
|
||||
MYZSTARBAR = MYYPS^(THETA/(1-THETA))*MYZ;
|
||||
|
||||
% The growth rate for lampda
|
||||
MYLABAR = MYZSTARBAR^(-PHI2*(1-PHI4)-PHI4);
|
||||
|
||||
% The relative optimal price for firms
|
||||
PTILBAR = ((1-ALFA*PAI^((CHI-1)*(1-ETA)))/(1-ALFA))^(1/(1-ETA));
|
||||
|
||||
% The state variable s for distortions between output and produktion
|
||||
SBAR = ((1-ALFA)*PTILBAR^(-ETA))/(1-ALFA*PAI^((1-CHI)*ETA));
|
||||
|
||||
% The 1-period interest rate
|
||||
RBAR = PAI/(BETTA*MYLABAR);
|
||||
|
||||
% The market price of capital
|
||||
QBAR = 1;
|
||||
|
||||
% The real price of renting capital
|
||||
RKBAR = QBAR*(MYYPS/(BETTA*MYLABAR)-(1-DELTA));
|
||||
|
||||
% The marginal costs in the firms
|
||||
MCBAR = (1-ALFA*BETTA*MYLABAR*PAI^((1-CHI)*ETA)*MYZSTARBAR)*(ETA-1)/ETA*PTILBAR/(1-ALFA*BETTA*MYLABAR*PAI^((CHI-1)*(1-ETA))*MYZSTARBAR);
|
||||
|
||||
% The capital stock
|
||||
KBAR = H*(RKBAR/(MCBAR*THETA*MYYPS*MYZ^(1-THETA)))^(1/(THETA-1));
|
||||
|
||||
% The wage level
|
||||
WBAR = MCBAR*(1-THETA)*MYYPS^(-THETA/(1-THETA))*MYZ^-THETA*(KBAR/H)^THETA;
|
||||
|
||||
% The level of investment
|
||||
IVBAR = KBAR - (1-DELTA)*KBAR*MYYPS^(-1/(1-THETA))*MYZ^-1;
|
||||
|
||||
% The consumption level
|
||||
CBAR = ((1-GoY)*(KBAR*MYYPS^(-1/(1-THETA))*MYZ^-1)^THETA*H^(1-THETA))/SBAR-IVBAR;
|
||||
|
||||
% The output level
|
||||
YBAR = (CBAR + IVBAR)/(1-GoY);
|
||||
|
||||
% The value of lambda
|
||||
LABAR = (CBAR-B*CBAR*MYZSTARBAR^-1)^-PHI2 - INHABIT*B*BETTA*(CBAR-B*CBAR*MYZSTARBAR^-1)^-PHI2*MYZSTARBAR^(-PHI2*(1-PHI4)-PHI4);
|
||||
|
||||
% The value of PHIzero
|
||||
PHIzero = LABAR*WBAR*(1-H)^PHI1;
|
||||
|
||||
% The level of the value function
|
||||
VFBAR = 1/(1-BETTA*MYZSTARBAR^((1-PHI4)*(1-PHI2)))*(1/(1-PHI2)*((CBAR-B*CBAR*MYZSTARBAR^-1)^(1-PHI2)-1)+PHIzero/(1-PHI1)*(1-H)^(1-PHI1));
|
||||
UBAR = 1/(1-PHI2)*((CBAR-B*CBAR*MYZSTARBAR^-1)^(1-PHI2)-1)+PHIzero/(1-PHI1)*(1-H)^(1-PHI1);
|
||||
[AA, EVFBAR, PHI3, negVf, info]= AFVRR_steady_helper(VFBAR,RBAR,IVBAR,CBAR,KBAR,LABAR,QBAR,YBAR, BETTA,B,PAI,H,PHIzero,PHI1,PHI2,THETA,MYYPS,MYZ,INHABIT,RRA,CONSxhr40);
|
||||
% The value of X2
|
||||
X2BAR = YBAR*PTILBAR^(-ETA)/(1-BETTA*ALFA*MYLABAR*PAI^((CHI-1)*(1-ETA))*MYZSTARBAR);
|
||||
|
||||
% Government spending
|
||||
GBAR = GoY*YBAR;
|
||||
%**************************************************************************
|
||||
|
||||
% map into model variables
|
||||
ln_k = log(KBAR);
|
||||
ln_s = log(SBAR);
|
||||
ln_c_ba1 = log(CBAR);
|
||||
ln_r_ba1 = log(RBAR);
|
||||
ln_a = log(1);
|
||||
ln_g = log(GBAR);
|
||||
ln_d = log(1);
|
||||
|
||||
ln_c = log(CBAR);
|
||||
ln_r = log(RBAR);
|
||||
ln_pai = log(PAI);
|
||||
ln_h = log(H);
|
||||
ln_q = log(QBAR);
|
||||
ln_evf = log(EVFBAR);
|
||||
ln_iv = log(IVBAR);
|
||||
ln_x2 = log(X2BAR);
|
||||
ln_la = log(LABAR);
|
||||
ln_goy = log(GoY);
|
||||
ln_Esdf = log(1/RBAR);
|
||||
xhr20 = 0;
|
||||
xhr40 = 0;
|
||||
Exhr = 0;
|
||||
% The yield curve
|
||||
ln_p1 = log((1/RBAR)^1);
|
||||
ln_p2 = log((1/RBAR)^2);
|
||||
ln_p3 = log((1/RBAR)^3);
|
||||
ln_p4 = log((1/RBAR)^4);
|
||||
ln_p5 = log((1/RBAR)^5);
|
||||
ln_p6 = log((1/RBAR)^6);
|
||||
ln_p7 = log((1/RBAR)^7);
|
||||
ln_p8 = log((1/RBAR)^8);
|
||||
ln_p9 = log((1/RBAR)^9);
|
||||
ln_p10 = log((1/RBAR)^10);
|
||||
ln_p11 = log((1/RBAR)^11);
|
||||
ln_p12 = log((1/RBAR)^12);
|
||||
ln_p13 = log((1/RBAR)^13);
|
||||
ln_p14 = log((1/RBAR)^14);
|
||||
ln_p15 = log((1/RBAR)^15);
|
||||
ln_p16 = log((1/RBAR)^16);
|
||||
ln_p17 = log((1/RBAR)^17);
|
||||
ln_p18 = log((1/RBAR)^18);
|
||||
ln_p19 = log((1/RBAR)^19);
|
||||
ln_p20 = log((1/RBAR)^20);
|
||||
ln_p21 = log((1/RBAR)^21);
|
||||
ln_p22 = log((1/RBAR)^22);
|
||||
ln_p23 = log((1/RBAR)^23);
|
||||
ln_p24 = log((1/RBAR)^24);
|
||||
ln_p25 = log((1/RBAR)^25);
|
||||
ln_p26 = log((1/RBAR)^26);
|
||||
ln_p27 = log((1/RBAR)^27);
|
||||
ln_p28 = log((1/RBAR)^28);
|
||||
ln_p29 = log((1/RBAR)^29);
|
||||
ln_p30 = log((1/RBAR)^30);
|
||||
ln_p31 = log((1/RBAR)^31);
|
||||
ln_p32 = log((1/RBAR)^32);
|
||||
ln_p33 = log((1/RBAR)^33);
|
||||
ln_p34 = log((1/RBAR)^34);
|
||||
ln_p35 = log((1/RBAR)^35);
|
||||
ln_p36 = log((1/RBAR)^36);
|
||||
ln_p37 = log((1/RBAR)^37);
|
||||
ln_p38 = log((1/RBAR)^38);
|
||||
ln_p39 = log((1/RBAR)^39);
|
||||
ln_p40 = log((1/RBAR)^40);
|
||||
|
||||
Obs_Gr_C = 4*( log(MYZSTARBAR) );
|
||||
Obs_Gr_I = 4*( log(MYZSTARBAR)+log(MYYPS) );
|
||||
Obs_Infl = 4*( ln_pai );
|
||||
Obs_r1 = 4*( ln_r );
|
||||
Obs_r40 = 4*( -1/40*ln_p40 );
|
||||
Obs_xhr40 = 4*( xhr40 );
|
||||
Obs_GoY = 4*( 1/4*ln_goy );
|
||||
Obs_hours = 4*( 1/100*ln_h );
|
||||
end;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Declare moments to use in estimation
|
||||
%--------------------------------------------------------------------------
|
||||
% These are the moments used in the paper; corresponds to momentSet=2 in the replication files
|
||||
|
||||
matched_moments;
|
||||
%mean
|
||||
Obs_Gr_C;
|
||||
Obs_Gr_I;
|
||||
Obs_Infl;
|
||||
Obs_r1;
|
||||
Obs_r40;
|
||||
Obs_xhr40;
|
||||
Obs_GoY;
|
||||
Obs_hours;
|
||||
|
||||
% all variances
|
||||
Obs_Gr_C*Obs_Gr_C;
|
||||
Obs_Gr_I*Obs_Gr_I;
|
||||
Obs_Infl*Obs_Infl;
|
||||
Obs_r1*Obs_r1;
|
||||
Obs_r40*Obs_r40;
|
||||
Obs_xhr40*Obs_xhr40;
|
||||
Obs_GoY*Obs_GoY;
|
||||
Obs_hours*Obs_hours;
|
||||
|
||||
% covariance excluding GoY and hours
|
||||
Obs_Gr_C*Obs_Gr_I;
|
||||
Obs_Gr_C*Obs_Infl;
|
||||
Obs_Gr_C*Obs_r1;
|
||||
Obs_Gr_C*Obs_r40;
|
||||
Obs_Gr_C*Obs_xhr40;
|
||||
%Obs_Gr_C*Obs_GoY;
|
||||
%Obs_Gr_C*Obs_hours;
|
||||
|
||||
Obs_Gr_I*Obs_Infl;
|
||||
Obs_Gr_I*Obs_r1;
|
||||
Obs_Gr_I*Obs_r40;
|
||||
Obs_Gr_I*Obs_xhr40;
|
||||
%Obs_Gr_I*Obs_GoY;
|
||||
%Obs_Gr_I*Obs_hours;
|
||||
|
||||
Obs_Infl*Obs_r1;
|
||||
Obs_Infl*Obs_r40;
|
||||
Obs_Infl*Obs_xhr40;
|
||||
%Obs_Infl*Obs_GoY;
|
||||
%Obs_Infl*Obs_hours;
|
||||
|
||||
Obs_r1*Obs_r40;
|
||||
Obs_r1*Obs_xhr40;
|
||||
%Obs_r1*Obs_GoY;
|
||||
%Obs_r1*Obs_hours;
|
||||
|
||||
Obs_r40*Obs_xhr40;
|
||||
%Obs_r40*Obs_GoY;
|
||||
%Obs_r40*Obs_hours;
|
||||
|
||||
%Obs_xhr40*Obs_GoY;
|
||||
%Obs_xhr40*Obs_hours;
|
||||
|
||||
%Obs_GoY*Obs_hours;
|
||||
|
||||
%first autocovariance
|
||||
Obs_Gr_C*Obs_Gr_C(-1);
|
||||
Obs_Gr_I*Obs_Gr_I(-1);
|
||||
Obs_Infl*Obs_Infl(-1);
|
||||
Obs_r1*Obs_r1(-1);
|
||||
Obs_r40*Obs_r40(-1);
|
||||
Obs_xhr40*Obs_xhr40(-1);
|
||||
Obs_GoY*Obs_GoY(-1);
|
||||
Obs_hours*Obs_hours(-1);
|
||||
end;
|
||||
|
||||
%--------------------------------------------------------------------------
|
||||
% Create Data
|
||||
%--------------------------------------------------------------------------
|
||||
@#ifdef CreateData
|
||||
verbatim;
|
||||
% From 1961Q3 to 2007Q4
|
||||
DataUS = xlsread('Data_PruningPaper_v5.xlsx','Data_used','E3:M188');
|
||||
% ANNUALIZED (except for hours and GoY)
|
||||
% 1 2 3 4 5 6 7 8 9
|
||||
% Lables: Date Gr_C Gr_I GoY hours Infl_C r1 r40 xhr40
|
||||
%label_data = {'Gr_C ', 'Gr_I ','Infl ', 'r1 ', 'r40 ', 'xhr40 ','GoY ', 'hours '};
|
||||
%DataUS = [DataUS(:,2:3) DataUS(:,6:8) DataUS(:,9) log(DataUS(:,4)) 4*log(DataUS(:,5))/100];
|
||||
Obs_Gr_C = DataUS(:,2);
|
||||
Obs_Gr_I = DataUS(:,3);
|
||||
Obs_Infl = DataUS(:,6);
|
||||
Obs_r1 = DataUS(:,7);
|
||||
Obs_r40 = DataUS(:,8);
|
||||
Obs_xhr40 = DataUS(:,9);
|
||||
Obs_GoY = log(DataUS(:,4));
|
||||
Obs_hours = 4*log(DataUS(:,5))/100;
|
||||
|
||||
save('AFVRR_data.mat','Obs_Gr_C','Obs_Gr_I','Obs_Infl','Obs_r1','Obs_r40','Obs_xhr40','Obs_GoY','Obs_hours');
|
||||
pause(1);
|
||||
end;
|
||||
@#endif
|
||||
Binary file not shown.
|
|
@ -0,0 +1,80 @@
|
|||
% DSGE model based on replication files of
|
||||
% Andreasen, Fernandez-Villaverde, Rubio-Ramirez (2018), The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications, Review of Economic Studies, 85, p. 1-49
|
||||
% Adapted for Dynare by Willi Mutschler (@wmutschl, willi@mutschler.eu), Jan 2021
|
||||
% =========================================================================
|
||||
% Copyright (C) 2021 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 <http://www.gnu.org/licenses/>.
|
||||
% =========================================================================
|
||||
|
||||
% This is a helper function to compute steady state values and endogenous parameters
|
||||
% Based on DSGE_model_yieldCurve_ss.m, getPHI3.m, ObjectGMM.m
|
||||
|
||||
function [AA, EVFBAR, PHI3, negVf, info]= AFVRR_steady_helper(VFBAR,RBAR,IVBAR,CBAR,KBAR,LABAR,QBAR,YBAR, BETTA,B,PAI,H,PHIzero,PHI1,PHI2,THETA,MYYPS,MYZ,INHABIT,RRA,CONSxhr40)
|
||||
% We get nice values of EVF by setting AA app. equal to VF.
|
||||
% The value of the expected value function raised to the power 1-PHI3
|
||||
% Also we check bounds on other variables
|
||||
% % Adding PHI3 to params. Note that PHI3 only affects the value function in
|
||||
% % steady state, hence the value we assign to PHI3 is irrelevant
|
||||
% PHI3 = -100;
|
||||
|
||||
info=0;
|
||||
AA = NaN;
|
||||
EVFBAR = NaN;
|
||||
PHI3 = NaN;
|
||||
negVf = NaN;
|
||||
|
||||
MYZSTAR = MYYPS^(THETA/(1-THETA))*MYZ;
|
||||
% The wage level
|
||||
WBAR = PHIzero*(1-H)^(-PHI1)/LABAR;
|
||||
RRAc = RRA;
|
||||
if INHABIT == 1
|
||||
PHI3 = (RRAc - PHI2/((1-B*MYZSTAR^-1)/(1-BETTA*B)+PHI2/PHI1*WBAR*(1-H)/CBAR))/((1-PHI2)/((1-B*MYZSTAR^-1)/(1-BETTA*B)-(CBAR-B*CBAR*MYZSTAR^-1)^PHI2/((1-BETTA*B)*CBAR)+WBAR*(1-H)/CBAR*(1-PHI2)/(1-PHI1)));
|
||||
else
|
||||
PHI3 = (RRAc - PHI2/(1-B*MYZSTAR^-1+PHI2/PHI1*WBAR*(1-H)/CBAR))/((1-PHI2)/(1-B*MYZSTAR^-1-(CBAR-B*CBAR*MYZSTAR^-1)^PHI2/((1-BETTA*B)*CBAR)+WBAR*(1-H)/CBAR*(1-PHI2)/(1-PHI1)));
|
||||
end
|
||||
if abs(PHI3) > 30000
|
||||
disp('abs of PHI3 exceeds 30000')
|
||||
info=1;
|
||||
return
|
||||
end
|
||||
|
||||
if CONSxhr40 > 1
|
||||
info=1;
|
||||
return
|
||||
end
|
||||
|
||||
|
||||
if VFBAR < 0
|
||||
AA = -VFBAR;
|
||||
EVFBAR = (-VFBAR/AA)^(1-PHI3);
|
||||
negVf = 1;
|
||||
else
|
||||
AA = VFBAR;
|
||||
EVFBAR = (VFBAR/AA)^(1-PHI3);
|
||||
negVf = -1;
|
||||
disp('Positive Value Function');
|
||||
end
|
||||
|
||||
|
||||
if RBAR < 1 || IVBAR < 0 || CBAR < 0 || KBAR < 0 || PAI < 1 || H < 0 || H > 1 || QBAR < 0 || YBAR < 0
|
||||
info = 1;
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
|
||||
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
% By Willi Mutschler, September 26, 2016. Email: willi@mutschler.eu
|
||||
function [ys,params,check] = RBCmodel_steadystate(ys,exo,M_,options_)
|
||||
%% Step 0: initialize indicator and set options for numerical solver
|
||||
check = 0;
|
||||
options = optimset('Display','off','TolX',1e-12,'TolFun',1e-12);
|
||||
params = M_.params;
|
||||
|
||||
%% Step 1: read out parameters to access them with their name
|
||||
for ii = 1:M_.param_nbr
|
||||
eval([ M_.param_names{ii} ' = M_.params(' int2str(ii) ');']);
|
||||
end
|
||||
|
||||
%% Step 2: Check parameter restrictions
|
||||
if ETAc*ETAl<1 % parameter violates restriction (here it is artifical)
|
||||
check=1; %set failure indicator
|
||||
return; %return without updating steady states
|
||||
end
|
||||
|
||||
%% Step 3: Enter model equations here
|
||||
A = 1;
|
||||
RK = 1/BETTA - (1-DELTA);
|
||||
K_O_N = (RK/(A*(1-ALFA)))^(-1/ALFA);
|
||||
if K_O_N <= 0
|
||||
check = 1; % set failure indicator
|
||||
return; % return without updating steady states
|
||||
end
|
||||
W = A*ALFA*(K_O_N)^(1-ALFA);
|
||||
IV_O_N = DELTA*K_O_N;
|
||||
Y_O_N = A*K_O_N^(1-ALFA);
|
||||
C_O_N = Y_O_N - IV_O_N;
|
||||
if C_O_N <= 0
|
||||
check = 1; % set failure indicator
|
||||
return; % return without updating steady states
|
||||
end
|
||||
|
||||
% The labor level
|
||||
if ETAc == 1 && ETAl == 1
|
||||
N = (1-BETTA*B)*(C_O_N*(1-B))^-1*W/THETA/(1+(1-BETTA*B)*(C_O_N*(1-B))^-1*W/THETA);
|
||||
else
|
||||
% No closed-form solution use a fixed-point algorithm
|
||||
N0 = 1/3;
|
||||
[N,~,exitflag] = fsolve(@(N) THETA*(1-N)^(-ETAl)*N^ETAc - (1-BETTA*B)*(C_O_N*(1-B))^(-ETAc)*W, N0,options);
|
||||
if exitflag <= 0
|
||||
check = 1; % set failure indicator
|
||||
return % return without updating steady states
|
||||
end
|
||||
end
|
||||
|
||||
C=C_O_N*N;
|
||||
Y=Y_O_N*N;
|
||||
IV=IV_O_N*N;
|
||||
K=K_O_N*N;
|
||||
LA = (C-B*C)^(-ETAc)-BETTA*B*(C-B*C)^(-ETAc);
|
||||
|
||||
k=log(K);
|
||||
c=log(C);
|
||||
a=log(A);
|
||||
iv=log(IV);
|
||||
y=log(Y);
|
||||
la=log(LA);
|
||||
n=log(N);
|
||||
rk=log(RK);
|
||||
w=log(W);
|
||||
%% Step 4: Update parameters and variables
|
||||
params=NaN(M_.param_nbr,1);
|
||||
for iter = 1:M_.param_nbr %update parameters set in the file
|
||||
eval([ 'params(' num2str(iter) ') = ' M_.param_names{iter} ';' ])
|
||||
end
|
||||
|
||||
for ii = 1:M_.orig_endo_nbr %auxiliary variables are set automatically
|
||||
eval(['ys(' int2str(ii) ') = ' M_.endo_names{ii} ';']);
|
||||
end
|
||||
|
||||
end
|
||||
Loading…
Reference in New Issue