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Merge branch 'loglinear'

time-shift
Stéphane Adjemian (Scylla) 2014-01-30 13:06:43 +01:00
parent 4c4cfd441c 62aa9b8fa5
commit 2e1ad9c51b
18 changed files with 266 additions and 36 deletions
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6
doc/dynare.texi
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@ -3611,7 +3611,11 @@ The convergence criterion used in the logarithmic reduction algorithm. Its defau
The maximum number of iterations used in the logarithmic reduction algorithm. Its default value is 100.
@item loglinear
@xref{loglinear}.
@xref{loglinear}. Note that ALL variables are log-transformed by using the Jacobian transformation,
not only selected ones. Thus, you have to make sure that your variables have strictly positive
steady states. @code{stoch_simul} will display the moments, decision rules,
and impulse responses for the log-linearized variables. The decision rules saved
in @code{oo_.dr} and the simulated variables will also be the ones for the log-linear variables.
@end table

17
examples/fs2000.mod
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@ -65,23 +65,6 @@ gy_obs = dA*y/y(-1);
gp_obs = (P/P(-1))*m(-1)/dA;
end;
initval;
k = 6;
m = mst;
P = 2.25;
c = 0.45;
e = 1;
W = 4;
R = 1.02;
d = 0.85;
n = 0.19;
l = 0.86;
y = 0.6;
gy_obs = exp(gam);
gp_obs = exp(-gam);
dA = exp(gam);
end;
shocks;
var e_a; stderr 0.014;
var e_m; stderr 0.005;

2
matlab/DsgeSmoother.m
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@ -74,7 +74,7 @@ bayestopt_.mf = bayestopt_.smoother_mf;
if options_.noconstant
constant = zeros(nobs,1);
else
if options_.loglinear == 1
if options_.loglinear
constant = log(SteadyState(bayestopt_.mfys));
else
constant = SteadyState(bayestopt_.mfys);

6
matlab/PosteriorFilterSmootherAndForecast.m
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@ -170,19 +170,19 @@ for b=1:B
horizon+maxlag,1);
end
yf(:,IdObs) = yf(:,IdObs)+(gend+[1-maxlag:horizon]')*trend_coeff';
if options_.loglinear == 1
if options_.loglinear
yf = yf+repmat(log(SteadyState'),horizon+maxlag,1);
else
yf = yf+repmat(SteadyState',horizon+maxlag,1);
end
yf1 = forcst2(yyyy,horizon,dr,1);
if options_.prefilter == 1
if options_.prefilter
yf1(:,IdObs,:) = yf1(:,IdObs,:)+ ...
repmat(bayestopt_.mean_varobs',[horizon+maxlag,1,1]);
end
yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat((gend+[1-maxlag:horizon]')* ...
trend_coeff',[1,1,1]);
if options_.loglinear == 1
if options_.loglinear
yf1 = yf1 + repmat(log(SteadyState'),[horizon+maxlag,1,1]);
else
yf1 = yf1 + repmat(SteadyState',[horizon+maxlag,1,1]);

2
matlab/dr_block.m
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@ -628,7 +628,7 @@ for i = 1:Size;
if options_.loglinear == 1
if options_.loglinear
error('log linear option is for the moment not supported in first order approximation for a block decomposed mode');
% k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
% klag = dr.kstate(k,[1 2]);

7
matlab/dynare_estimation.m
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@ -63,7 +63,12 @@ if (isnumeric(options_.mode_compute) && options_.mode_compute && options_.analyt
analytic_derivation0=options_.analytic_derivation;
options_.analytic_derivation=1;
end
if options_.logged_steady_state
oo_.dr.ys=exp(oo_.dr.ys);
oo_.steady_state=exp(oo_.steady_state);
end
if nnobs > 1
for i=1:nnobs

2
matlab/dynare_sensitivity.m
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@ -388,7 +388,7 @@ if options_gsa.glue,
gend = options_.nobs;
rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
if options_.loglinear == 1
if options_.loglinear
rawdata = log(rawdata);
end
if options_.prefilter == 1

1
matlab/global_initialization.m
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@ -374,6 +374,7 @@ options_.lik_init = 1;
options_.load_mh_file = 0;
options_.logdata = 0;
options_.loglinear = 0;
options_.logged_steady_state = 0;
options_.mh_conf_sig = 0.90;
options_.prior_interval = 0.90;
options_.mh_drop = 0.5;

3
matlab/k_order_pert.m
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@ -97,11 +97,10 @@ else
dr.ghx = dr.g_1(:,1:nspred);
dr.ghu = dr.g_1(:,nspred+1:end);
if options.loglinear == 1
if options.loglinear
k = find(dr.kstate(:,2) <= M.maximum_endo_lag+1);
klag = dr.kstate(k,[1 2]);
k1 = dr.order_var;
dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* ...
repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;

6
matlab/prior_posterior_statistics_core.m
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@ -195,12 +195,12 @@ for b=fpar:B
if horizon
yyyy = alphahat(iendo,i_last_obs);
yf = forcst2a(yyyy,dr,zeros(horizon,exo_nbr));
if options_.prefilter == 1
if options_.prefilter
yf(:,IdObs) = yf(:,IdObs)+repmat(bayestopt_.mean_varobs', ...
horizon+maxlag,1);
end
yf(:,IdObs) = yf(:,IdObs)+(gend+[1-maxlag:horizon]')*trend_coeff';
if options_.loglinear == 1
if options_.loglinear
yf = yf+repmat(log(SteadyState'),horizon+maxlag,1);
else
yf = yf+repmat(SteadyState',horizon+maxlag,1);
@ -212,7 +212,7 @@ for b=fpar:B
end
yf1(:,IdObs,:) = yf1(:,IdObs,:)+repmat((gend+[1-maxlag:horizon]')* ...
trend_coeff',[1,1,1]);
if options_.loglinear == 1
if options_.loglinear
yf1 = yf1 + repmat(log(SteadyState'),[horizon+maxlag,1,1]);
else
yf1 = yf1 + repmat(SteadyState',[horizon+maxlag,1,1]);

24
matlab/resol.m
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@ -107,6 +107,30 @@ if info(1)
return
end
if options.loglinear
% Find variables with non positive steady state.
idx = find(dr.ys<1e-9);
if length(idx)
variables_with_non_positive_steady_state = M.endo_names(idx,:);
skipline()
fprintf('You are attempting to simulate/estimate a loglinear approximation of a model, but\n')
fprintf('the steady state level of the following variables is not strictly positive:\n')
for i=1:length(idx)
fprintf(' - %s (%s)\n',deblank(variables_with_non_positive_steady_state(idx,:)), num2str(dr.ys(idx)))
end
if isestimation()
fprintf('You should check that the priors and/or bounds over the deep parameters are such')
frpintf('the steady state levels of all the variables are strictly positive, or consider')
fprintf('a linearization of the model instead of a log linearization.')
else
fprintf('You should check that the calibration of the deep parameters is such that the')
fprintf('steady state levels of all the variables are strictly positive, or consider')
fprintf('a linearization of the model instead of a log linearization.')
end
error('stoch_simul::resol: The loglinearization of the model cannot be performed because the steady state is not strictly positive!')
end
end
if options.block
[dr,info,M,options,oo] = dr_block(dr,check_flag,M,options,oo);
else

9
matlab/stoch_simul.m
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@ -77,9 +77,18 @@ elseif options_.discretionary_policy
end
[oo_.dr,ys,info] = discretionary_policy_1(oo_,options_.instruments);
else
if options_.logged_steady_state %if steady state was previously logged, undo this
oo_.dr.ys=exp(oo_.dr.ys);
oo_.steady_state=exp(oo_.steady_state);
end
[oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end
if options_.loglinear %log steady state for correct display of decision rules and simulations
oo_.dr.ys=log(oo_.dr.ys);
oo_.steady_state=log(oo_.steady_state);
options_old.logged_steady_state = 1;
end
if info(1)
options_ = options_old;
print_info(info, options_.noprint, options_);

6
matlab/stochastic_solvers.m
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@ -297,22 +297,20 @@ if M_.exo_det_nbr > 0
kron(hudi,Eud)+dr.ghxud{i-1}(kf,:)* ...
kron(hudj,Eud)+dr.ghxx(kf,:)*kron(hudj,hudi))-M1*R2;
end
end
end
end
if options_.loglinear == 1
if options_.loglinear
% this needs to be extended for order=2,3
k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
klag = dr.kstate(k,[1 2]);
k1 = dr.order_var;
dr.ghx = repmat(1./dr.ys(k1),1,size(dr.ghx,2)).*dr.ghx.* ...
repmat(dr.ys(k1(klag(:,1)))',size(dr.ghx,1),1);
dr.ghu = repmat(1./dr.ys(k1),1,size(dr.ghu,2)).*dr.ghu;
if options_.order>1
error('Loglinear options currently only works at order 1')
error('Loglinear options currently only works at order 1')
end
end

32
matlab/utilities/general/isestimation.m Normal file
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@ -0,0 +1,32 @@
function a = isestimation()
% Returns 1 if we are currently estimating a model, 0 otherwise.
% Copyright (C) 2014 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/>.
a = 0;
tmp = struct2cell(dbstack);
list_of_previously_called_routines = transpose(tmp(2,:));
if ~isempty(strmatch('dsge_likelihood', list_of_previously_called_routines, 'exact')) || ...
~isempty(strmatch('dsge_var_likelihood', list_of_previously_called_routines, 'exact')) || ...
~isempty(strmatch('non_linear_dsge_likelihood', list_of_previously_called_routines, 'exact')) || ...
~isempty(strmatch('simulated_moments_estimation', list_of_previously_called_routines, 'exact'))
a = 1;
end

6
tests/Makefile.am
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@ -167,7 +167,8 @@ MODFILES = \
gradient/fs2000_numgrad_3.mod \
gradient/fs2000_numgrad_5.mod \
filter_step_ahead/fs2000_filter_step_ahead_bayesian.mod \
filter_step_ahead/fs2000_filter_step_ahead_ML.mod
filter_step_ahead/fs2000_filter_step_ahead_ML.mod \
loglinear/example4_loglinear.mod
XFAIL_MODFILES = ramst_xfail.mod \
estim_param_in_shock_value.mod
@ -327,7 +328,8 @@ EXTRA_DIST = \
estimation/fs2000_MCMC_jumping_covariance_steadystate.m \
estimation/fs2000_initialize_from_calib_steadystate.m \
filter_step_ahead/fs2000_filter_step_ahead_bayesian_steadystate.m \
filter_step_ahead/fs2000_filter_step_ahead_ML_steadystate.m
filter_step_ahead/fs2000_filter_step_ahead_ML_steadystate.m \
loglinear/results_exp.mat
TARGETS =

68
tests/loglinear/example4_exp.mod Normal file
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@ -0,0 +1,68 @@
/*
* Example 1 from F. Collard (2001): "Stochastic simulations with DYNARE:
* A practical guide" (see "guide.pdf" in the documentation directory).
*/
/*
* Copyright (C) 2001-2010 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/>.
*/
var y, c, k, a, h, b;
varexo e, u;
parameters beta, rho, alpha, delta, theta, psi, tau;
alpha = 0.36;
rho = 0.95;
tau = 0.025;
beta = 0.99;
delta = 0.025;
psi = 0;
theta = 2.95;
phi = 0.1;
model;
exp(c)*theta*exp(h)^(1+psi)=(1-alpha)*exp(y);
exp(k) = beta*(((exp(b)*exp(c))/(exp(b(+1))*exp(c(+1))))
*(exp(b(+1))*alpha*exp(y(+1))+(1-delta)*exp(k)));
exp(y) = exp(a)*(exp(k(-1))^alpha)*(exp(h)^(1-alpha));
exp(k) = exp(b)*(exp(y)-exp(c))+(1-delta)*exp(k(-1));
a = rho*a(-1)+tau*b(-1) + e;
b = tau*a(-1)+rho*b(-1) + u;
end;
initval;
y = log(1.08068253095672);
c = log(0.80359242014163);
h = log(0.29175631001732);
k = log(11.08360443260358);
a = 0;
b = 0;
end;
resid(1);
shocks;
var e; stderr 0.009;
var u; stderr 0.009;
var e, u = phi*0.009*0.009;
end;
stoch_simul(order=1);
oo_exp=oo_;
save results_exp oo_exp

105
tests/loglinear/example4_loglinear.mod Normal file
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@ -0,0 +1,105 @@
/*
* Example 1 from F. Collard (2001): "Stochastic simulations with DYNARE:
* A practical guide" (see "guide.pdf" in the documentation directory).
*/
/*
* Copyright (C) 2001-2010 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/>.
*/
var y, c, k, a, h, b;
varexo e, u;
parameters beta, rho, alpha, delta, theta, psi, tau;
alpha = 0.36;
rho = 0.95;
tau = 0.025;
beta = 0.99;
delta = 0.025;
psi = 0;
theta = 2.95;
phi = 0.1;
model;
c*theta*h^(1+psi)=(1-alpha)*y;
k = beta*(((b*c)/(b(+1)*c(+1)))
*(b(+1)*alpha*y(+1)+(1-delta)*k));
y = a*(k(-1)^alpha)*(h^(1-alpha));
k = b*(y-c)+(1-delta)*k(-1);
log(a) = rho*log(a(-1))+tau*log(b(-1)) + e;
log(b) = tau*log(a(-1))+rho*log(b(-1)) + u;
end;
initval;
y = 1.08068253095672;
c = 0.80359242014163;
h = 0.29175631001732;
k = 11.08360443260358;
a = 1;
b = 1;
end;
resid(1);
shocks;
var e; stderr 0.009;
var u; stderr 0.009;
var e, u = phi*0.009*0.009;
end;
stoch_simul(loglinear,order=1);
load results_exp;
if max(max(abs(oo_.dr.ghx-oo_exp.dr.ghx)))>1e-10
error('Option loglinear wrong, ghx not equal')
end
if max(max(abs(oo_.dr.ghu-oo_exp.dr.ghu)))>1e-10
error('Option loglinear wrong, ghu not equal')
end
if max(max(abs(oo_.irfs.y_e-oo_exp.irfs.y_e)))>1e-10
error('Option loglinear wrong, IRFs not equal')
end
if max(max(abs(oo_.irfs.y_u-oo_exp.irfs.y_u)))>1e-10
error('Option loglinear wrong, ghu not equal')
end
if max(max(abs(oo_.mean-oo_exp.mean)))>1e-10
error('Option loglinear wrong, mean not equal')
end
if max(max(abs(oo_.dr.ys-oo_exp.dr.ys)))>1e-10
error('Option loglinear wrong, ys not equal')
end
if max(max(abs(oo_.steady_state-oo_exp.steady_state)))>1e-10
error('Option loglinear wrong, steady_state not equal')
end
for ii=1:length(oo_.gamma_y)
if max(max(abs(oo_.gamma_y{ii,1}-oo_exp.gamma_y{ii,1})))>1e-10
error('Option loglinear wrong, moments not equal')
end
end
for ii=1:length(oo_.autocorr)
if max(max(abs(oo_.autocorr{1,ii}-oo_exp.autocorr{1,ii})))>1e-10
error('Option loglinear wrong, moments not equal')
end
end
stoch_simul(loglinear,order=1,periods=100000);
if abs(mean(y)-0.0776)>0.02
error('Simulations are wrong')
end

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tests/loglinear/results_exp.mat Normal file
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