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Save conditional forecast output in oo_. Closes: Dynare/dynare#1672

time-shift
Dóra Kocsis 2019-11-29 11:13:21 +01:00
parent f2205ed4a1
commit b9af92eb8a
10 changed files with 43 additions and 41 deletions
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20
doc/manual/source/the-model-file.rst
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@ -7675,9 +7675,7 @@ the :comm:`bvar_forecast` command.
*Output*
The results are not stored in the ``oo_`` structure but in a
separate structure ``forecasts``, described below, saved to the
hard disk into a file called ``conditional_forecasts.mat.``
The results are stored in ``oo_.conditional_forecast``, which is described below.
*Example*
@ -7702,7 +7700,7 @@ the :comm:`bvar_forecast` command.
plot_conditional_forecast(periods = 10) a y;
.. matvar:: forecasts.cond
.. matvar:: oo_.conditional_forecast.cond
Variable set by the ``conditional_forecast`` command. It
stores the conditional forecasts. Fields are ``periods+1`` by
@ -7710,7 +7708,7 @@ the :comm:`bvar_forecast` command.
subsequent ``periods`` forecasts periods. Fields are of the
form::
forecasts.cond.FORECAST_MOMENT.VARIABLE_NAME
oo_.conditional_forecast.cond.FORECAST_MOMENT.VARIABLE_NAME
where FORECAST_MOMENT is one of the following:
@ -7724,12 +7722,12 @@ the :comm:`bvar_forecast` command.
distribution. The size corresponds to ``conf_sig``.
.. matvar:: forecasts.uncond
.. matvar:: oo_.conditional_forecast.uncond
Variable set by the ``conditional_forecast`` command. It stores
the unconditional forecasts. Fields are of the form::
forecasts.uncond.FORECAST_MOMENT.VARIABLE_NAME
oo_.conditional_forecast.uncond.FORECAST_MOMENT.VARIABLE_NAME
.. matvar:: forecasts.instruments
@ -7738,14 +7736,14 @@ the :comm:`bvar_forecast` command.
the names of the exogenous instruments.
.. matvar:: forecasts.controlled_variables
.. matvar:: oo_.conditional_forecast.controlled_variables
Variable set by the ``conditional_forecast`` command. Stores
the position of the constrained endogenous variables in
declaration order.
.. matvar:: forecasts.controlled_exo_variables
.. matvar:: oo_.conditional_forecast.controlled_exo_variables
Variable set by the ``conditional_forecast`` command. Stores
the values of the controlled exogenous variables underlying
@ -7753,9 +7751,9 @@ the :comm:`bvar_forecast` command.
endogenous variables. Fields are ``[number of constrained
periods]`` by ``1`` vectors and are of the form::
forecasts.controlled_exo_variables.FORECAST_MOMENT.SHOCK_NAME
oo_.conditional_forecast.controlled_exo_variables.FORECAST_MOMENT.SHOCK_NAME
.. matvar:: forecasts.graphs
.. matvar:: oo_.conditional_forecast.graphs
Variable set by the ``conditional_forecast`` command. Stores
the information for generating the conditional forecast plots.

7
matlab/imcforecast.m
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@ -25,10 +25,10 @@ function imcforecast(constrained_paths, constrained_vars, options_cond_fcst)
% This routine has to be called after an estimation statement or an estimated_params block.
%
% REMARKS
% [1] Results are stored in a structure which is saved in a mat file called conditional_forecasts.mat.
% [1] Results are stored in oo_.conditional_forecast.
% [2] Use the function plot_icforecast to plot the results.
% Copyright (C) 2006-2018 Dynare Team
% Copyright (C) 2006-2019 Dynare Team
%
% This file is part of Dynare.
%
@ -310,5 +310,4 @@ forecasts.graph.fname = M_.fname;
%reset qz_criterium
options_.qz_criterium=qz_criterium_old;
save('conditional_forecasts.mat','forecasts');
oo_.conditional_forecast = forecasts;

15
matlab/plot_icforecast.m
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@ -1,8 +1,11 @@
function plot_icforecast(Variables,periods,options_)
function plot_icforecast(Variables,periods,options_,oo_)
% Build plots for the conditional forecasts.
%
% INPUTS
% o Variables [cell] names of the endogenous variables to be plotted.
% o Variables [cell] Names of the endogenous variables to be plotted.
% o periods [int] Number of periods to be plotted.
% o options_ [structure] Options.
% o oo_ [structure] Storage of results.
%
% OUTPUTS
% None.
@ -10,7 +13,7 @@ function plot_icforecast(Variables,periods,options_)
% SPECIAL REQUIREMENTS
% This routine has to be called after imcforecast.m.
% Copyright (C) 2006-2018 Dynare Team
% Copyright (C) 2006-2019 Dynare Team
%
% This file is part of Dynare.
%
@ -27,7 +30,11 @@ function plot_icforecast(Variables,periods,options_)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
load conditional_forecasts;
if ~isfield(oo_, 'conditional_forecast')
error('Can''t find conditional forecasts');
else
forecasts = oo_.conditional_forecast;
end
forecast_periods = length(forecasts.cond.Mean.(Variables{1}));
if nargin==1 || isempty(periods) % Set default number of periods.

10
tests/conditional_forecasts/3/fs2000_conditional_forecast_initval.mod
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@ -85,8 +85,8 @@ plot_conditional_forecast(periods=100) gy_obs gp_obs k;
forecast(periods=100);
%compare unconditional forecasts
cond_forecast=load('conditional_forecasts.mat');
if max(abs(cond_forecast.forecasts.uncond.Mean.k(2:end)-oo_.forecast.Mean.k))>1e-8
cond_forecast=oo_.conditional_forecast;
if max(abs(cond_forecast.uncond.Mean.k(2:end)-oo_.forecast.Mean.k))>1e-8
error('Unconditional Forecasts do not match')
end
@ -94,11 +94,11 @@ end
%conditions need to be set with histval;
initial_condition_states = oo_.dr.ys;
shock_matrix = zeros(options_cond_fcst_.periods ,M_.exo_nbr); %create shock matrix with found controlled shocks
shock_matrix(1:5,strmatch('e_a',M_.exo_names,'exact')) = cond_forecast.forecasts.controlled_exo_variables.Mean.e_a; %set controlled shocks to their values
shock_matrix(1:5,strmatch('e_m',M_.exo_names,'exact')) = cond_forecast.forecasts.controlled_exo_variables.Mean.e_m; %set controlled shocks to their values
shock_matrix(1:5,strmatch('e_a',M_.exo_names,'exact')) = cond_forecast.controlled_exo_variables.Mean.e_a; %set controlled shocks to their values
shock_matrix(1:5,strmatch('e_m',M_.exo_names,'exact')) = cond_forecast.controlled_exo_variables.Mean.e_m; %set controlled shocks to their values
y_simult = simult_(M_,options_,initial_condition_states,oo_.dr,shock_matrix,1);
if max(abs(y_simult(strmatch('k',M_.endo_names,'exact'),:)'-cond_forecast.forecasts.cond.Mean.k))>1e-8
if max(abs(y_simult(strmatch('k',M_.endo_names,'exact'),:)'-cond_forecast.cond.Mean.k))>1e-8
error('Unconditional Forecasts do not match')
end

10
tests/conditional_forecasts/4/fs2000_conditional_forecast_histval.mod
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@ -86,8 +86,8 @@ plot_conditional_forecast(periods=100) gy_obs gp_obs k;
forecast(periods=100);
%compare unconditional forecasts
cond_forecast=load('conditional_forecasts.mat');
if max(abs(cond_forecast.forecasts.uncond.Mean.k(2:end)-oo_.forecast.Mean.k))>1e-8
cond_forecast=oo_.conditional_forecast;
if max(abs(cond_forecast.uncond.Mean.k(2:end)-oo_.forecast.Mean.k))>1e-8
error('Unconditional Forecasts do not match')
end
@ -95,11 +95,11 @@ end
initial_condition_states = oo_.dr.ys;
initial_condition_states(strmatch('k',M_.endo_names,'exact')) = 6;
shock_matrix = zeros(options_cond_fcst_.periods ,M_.exo_nbr); %create shock matrix with found controlled shocks
shock_matrix(1:5,strmatch('e_a',M_.exo_names,'exact')) = cond_forecast.forecasts.controlled_exo_variables.Mean.e_a; %set controlled shocks to their values
shock_matrix(1:5,strmatch('e_m',M_.exo_names,'exact')) = cond_forecast.forecasts.controlled_exo_variables.Mean.e_m; %set controlled shocks to their values
shock_matrix(1:5,strmatch('e_a',M_.exo_names,'exact')) = cond_forecast.controlled_exo_variables.Mean.e_a; %set controlled shocks to their values
shock_matrix(1:5,strmatch('e_m',M_.exo_names,'exact')) = cond_forecast.controlled_exo_variables.Mean.e_m; %set controlled shocks to their values
y_simult = simult_(M_,options_,initial_condition_states,oo_.dr,shock_matrix,1);
if max(abs(y_simult(strmatch('k',M_.endo_names,'exact'),:)'-cond_forecast.forecasts.cond.Mean.k))>1e-8
if max(abs(y_simult(strmatch('k',M_.endo_names,'exact'),:)'-cond_forecast.cond.Mean.k))>1e-8
error('Unconditional Forecasts do not match')
end

3
tests/loglinear/example4_exp.mod
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@ -77,6 +77,5 @@ end;
conditional_forecast(parameter_set=calibration, controlled_varexo=(u,e));
oo_exp=oo_;
load('conditional_forecasts.mat')
conditional_forecasts_exp=forecasts;
conditional_forecasts_exp=oo_.conditional_forecast;
save results_exp.mat oo_exp conditional_forecasts_exp

3
tests/loglinear/example4_exp_histval.mod
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@ -85,8 +85,7 @@ end;
conditional_forecast(parameter_set=calibration, controlled_varexo=(u,e));
oo_exp=oo_;
load('conditional_forecasts.mat')
conditional_forecasts_exp=forecasts;
conditional_forecasts_exp=oo_.conditional_forecast;
oo_exp=oo_;
save results_exp_histval.mat oo_exp conditional_forecasts_exp

2
tests/loglinear/example4_loglinear.mod
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@ -118,7 +118,7 @@ if max(max(abs(struct2array(oo_.forecast.Mean)-struct2array(oo_exp.forecast.Mean
error('Option loglinear wrong, forecast not equal')
end
load('conditional_forecasts.mat')
forecasts=oo_.conditional_forecast;
if max(max(abs(struct2array(forecasts.cond.Mean)-struct2array(conditional_forecasts_exp.cond.Mean))))>1e-10 || ...
max(max(abs(struct2array(forecasts.cond.ci)-struct2array(conditional_forecasts_exp.cond.ci))))>1e-10 || ...

2
tests/loglinear/example4_loglinear_histval.mod
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@ -114,7 +114,7 @@ end;
conditional_forecast(parameter_set=calibration, controlled_varexo=(u,e));
load('conditional_forecasts.mat')
forecasts=oo_.conditional_forecast;
if max(max(abs(struct2array(forecasts.cond.Mean)-struct2array(conditional_forecasts_exp.cond.Mean))))>1e-10 || ...
max(max(abs(struct2array(forecasts.cond.ci)-struct2array(conditional_forecasts_exp.cond.ci))))>1e-10 || ...

12
tests/observation_trends_and_prefiltering/Trend_no_prefilter_conditional_forecast.inc
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@ -12,8 +12,8 @@ conditional_forecast(periods=100,parameter_set=posterior_mode,replic=1000, contr
plot_conditional_forecast(periods=100) Y_obs P_obs;
%compare unconditional forecasts
cond_forecast=load('conditional_forecasts.mat');
if max(abs(cond_forecast.forecasts.uncond.Mean.P_obs(2:end)-oo_.forecast.Mean.P_obs))>1e-8
cond_forecast=oo_.conditional_forecast;
if max(abs(cond_forecast.uncond.Mean.P_obs(2:end)-oo_.forecast.Mean.P_obs))>1e-8
error('Unconditional Forecasts do not match')
end
@ -24,16 +24,16 @@ initial_condition_states(strmatch('P_obs',M_.endo_names,'exact')) = oo_.Smoothed
initial_condition_states(strmatch('junk1',M_.endo_names,'exact')) = oo_.SmoothedVariables.junk1(end);
initial_condition_states(strmatch('junk2',M_.endo_names,'exact')) = oo_.SmoothedVariables.junk2(end)-oo_.Smoother.Trend.junk2(end);
shock_matrix = zeros(options_cond_fcst_.periods ,M_.exo_nbr); %create shock matrix with found controlled shocks
shock_matrix(1:5,strmatch('e_y',M_.exo_names,'exact')) = cond_forecast.forecasts.controlled_exo_variables.Mean.e_y; %set controlled shocks to their values
shock_matrix(1:5,strmatch('e_p',M_.exo_names,'exact')) = cond_forecast.forecasts.controlled_exo_variables.Mean.e_p; %set controlled shocks to their values
shock_matrix(1:5,strmatch('e_y',M_.exo_names,'exact')) = cond_forecast.controlled_exo_variables.Mean.e_y; %set controlled shocks to their values
shock_matrix(1:5,strmatch('e_p',M_.exo_names,'exact')) = cond_forecast.controlled_exo_variables.Mean.e_p; %set controlled shocks to their values
y_simult = simult_(M_,options_,initial_condition_states,oo_.dr,shock_matrix,1);
if max(abs(y_simult(strmatch('Y_obs',M_.endo_names,'exact'),:)'+(options_.first_obs-1+options_.nobs:options_.first_obs-1+options_.nobs+options_.forecast)'*g_y-cond_forecast.forecasts.cond.Mean.Y_obs))>1e-8
if max(abs(y_simult(strmatch('Y_obs',M_.endo_names,'exact'),:)'+(options_.first_obs-1+options_.nobs:options_.first_obs-1+options_.nobs+options_.forecast)'*g_y-cond_forecast.cond.Mean.Y_obs))>1e-8
error('Conditional Forecasts do not match')
end
if max(abs(y_simult(strmatch('P_obs',M_.endo_names,'exact'),:)'+(options_.first_obs-1+options_.nobs:options_.first_obs-1+options_.nobs+options_.forecast)'*g_p-cond_forecast.forecasts.cond.Mean.P_obs))>1e-8
if max(abs(y_simult(strmatch('P_obs',M_.endo_names,'exact'),:)'+(options_.first_obs-1+options_.nobs:options_.first_obs-1+options_.nobs+options_.forecast)'*g_p-cond_forecast.cond.Mean.P_obs))>1e-8
error('Conditional Forecasts do not match')
end