Added PAC with Model Consistent Expectations.

matlab/+pac/+mce/parameters.m

@@ -0,0 +1,76 @@
+function parameters(pacname)
+
+% Updates the parameters of a PAC Model Consistent Expectations.
+%
+% INPUTS
+% - pacname       [string]    Name of the pac equation.
+%
+% OUTPUTS
+% - none
+%
+% SPECIAL REQUIREMENTS
+%    none
+
+% Copyright (C) 2019 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/>.
+
+global M_
+
+% Check that the first input is a row character array.
+if ~isrow(pacname)==1 || ~ischar(pacname)
+    error('Input argument must be a row character array!')
+end
+
+% Check the name of the PAC model.
+if ~isfield(M_.pac, pacname)
+    error('PAC model %s is not defined in the model block!', pacname)
+end
+
+% Get PAC model description
+pacmodel = M_.pac.(pacname);
+
+% Check that we are dealing with PAC/MCE
+if ~pacmodel.model_consistent_expectations
+    error('This function is to be used only for PAC Model Consistent Expectations.')
+end
+
+% Show the equations where this PAC model is used.
+number_of_pac_eq = size(pacmodel.tag_map, 1);
+if number_of_pac_eq==1
+    fprintf('PAC model %s is used in equation %s.\n', pacname, pacmodel.tag_map{1,1});
+else
+    fprintf('PAC model %s is used in %u equation(s):\n', pacname, number_of_pac_eq);
+    skipline()
+    for i=1:number_of_pac_eq
+        fprintf('    -  %s\n', pacmodel.tag_map{i,1});
+    end
+end
+skipline()
+
+equations = pacmodel.equations;
+
+for e=1:number_of_pac_eq
+    % Get PAC equation tag
+    eqtag = pacmodel.tag_map{e,2};
+    % Get PAC equation
+    pac_equation = equations.(eqtag);
+    % Get Error correction and autoregressive parameters in PAC equation
+    a = NaN(1+pac_equation.max_lag, 1);
+    a(1) = M_.params(pac_equation.ec.params);
+    a(1+(1:pac_equation.max_lag)) = M_.params(pac_equation.ar.params);
+    M_.params(pac_equation.mce.alpha) = a2alpha(a);
+end

matlab/+pac/+update/parameters.m

@@ -47,20 +47,20 @@ end
 % Get PAC model description
 pacmodel = DynareModel.pac.(pacname);
 
-% Get the name of the associated VAR model and test its existence.
+if pacmodel.model_consistent_expectations
+    error('This function cannot be used with Model Consistent Expectation. Try pac.mce.parameters instead.')
+end
+
+% Get the name of the associated auxiliary model (VAR or TREND_COMPONENT) model and test its existence.
 if ~isfield(DynareModel.(pacmodel.auxiliary_model_type), pacmodel.auxiliary_model_name)
     error('Unknown auxiliary model (%s) in PAC model (%s)!', pacmodel.auxiliary_model_name, pacname)
 end
-
 varmodel = DynareModel.(pacmodel.auxiliary_model_type).(pacmodel.auxiliary_model_name);
-
-% Check that we have the values of the VAR matrices.
+% Check that we have the values of the VAR or TREND_COMPONENT matrices.
 if ~isfield(DynareOutput.(pacmodel.auxiliary_model_type), pacmodel.auxiliary_model_name)
     error('Auxiliary model %s has to be estimated first!', pacmodel.auxiliary_model_name)
 end
-
 varcalib = DynareOutput.(pacmodel.auxiliary_model_type).(pacmodel.auxiliary_model_name);
-
 if ~isfield(varcalib, 'CompanionMatrix') || any(isnan(varcalib.CompanionMatrix(:)))
     error('Auxiliary model %s has to be estimated first.', pacmodel.auxiliary_model_name)
 end

matlab/+pac/initialize.m

@@ -27,4 +27,9 @@ function initialize(pacmodel)
 
 global M_
 
-get_companion_matrix(M_.pac.(pacmodel).auxiliary_model_name, M_.pac.(pacmodel).auxiliary_model_type);
+if isempty(M_.pac.(pacmodel).auxiliary_model_name)
+    M_.pac.(pacmodel).model_consistent_expectations = true;
+else
+    M_.pac.(pacmodel).model_consistent_expectations = false;
+    get_companion_matrix(M_.pac.(pacmodel).auxiliary_model_name, M_.pac.(pacmodel).auxiliary_model_type);
+end

preprocessor

@@ -1 +1 @@
-Subproject commit 8ae7f81fd990d261058de21312ff0e0c290fde90
+Subproject commit abde60fbeaa36704054a23606057e4d35495b898

tests/Makefile.am

@@ -418,6 +418,8 @@ MODFILES = \
 	pac/trend-component-19/example1.mod \
 	pac/trend-component-19/example2.mod \
 	pac/trend-component-19/example3.mod \
+    pac/trend-component-1-mce/example_det.mod \
+    pac/trend-component-1-mce/example_sto.mod \
     ecb/backward-models/irf/solow_1.mod \
     ecb/backward-models/irf/solow_2.mod \
 	dynare-command-options/ramst.mod

tests/pac/trend-component-1-mce/clean

@@ -0,0 +1,13 @@
+#!/bin/sh
+
+rm *.eps
+
+rm -rf example_det
+rm -rf +example_det
+rm -f example_det*.mat
+rm -f example_det.log
+
+rm -rf example_sto
+rm -rf +example_sto
+rm -f example_sto*.mat
+rm -f example_sto.log

tests/pac/trend-component-1-mce/example_det.mod

@@ -0,0 +1,68 @@
+// --+ options: json=compute +--
+
+var x1 x2 x1bar x2bar z ;
+
+varexo ex1 ex2 ex1bar ex2bar ez;
+
+parameters a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 gamma beta ;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+gamma =  .7;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(discount=beta, model_name=pacman, steady_state_growth=.0);
+
+model;
+
+[name='eq:x1']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='eq:pac']
+diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+// Initialize the PAC model.
+pac.initialize('pacman');
+
+// Update the PAC/MCE parameters (α in M_.params).
+pac.mce.parameters('pacman');
+
+// Setup a scenario for the shocks.
+shocks;
+   var ex1;
+   periods 1;
+   values .2;
+end;
+
+perfect_foresight_setup(periods=50);
+perfect_foresight_solver;

tests/pac/trend-component-1-mce/example_sto.mod

@@ -0,0 +1,71 @@
+// --+ options: json=compute, stochastic +--
+
+var x1 x2 x1bar x2bar z ;
+
+varexo ex1 ex2 ex1bar ex2bar ez;
+
+parameters a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 gamma beta g;
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+
+gamma =  .7;
+
+g = .02;
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(discount=beta, model_name=pacman);
+
+model(linear);
+
+[name='eq:x1']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;     
+
+[name='eq:x2']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;     
+
+[name='eq:x1bar']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='eq:pac']
+diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+// Initialize the PAC model.
+pac.initialize('pacman');
+
+// Update the PAC/MCE parameters (α in M_.params).
+pac.mce.parameters('pacman');
+
+// Setup a scenario for the shocks.
+shocks;
+var ex1 = .01;
+var ex2 = .01;
+var ex1bar = .0;
+var ex2bar = .0;
+var ez = .02; 
+end;
+
+stoch_simul(periods=1000);