Allow arbitrary nonzero mean exogenous variables in PAC equations.
Previously any nonzero mean exogenous variable had to be centered on the growth rate (first difference) of the (PAC's LHS) endogenous variable.var-model-with-constant
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@ -1,6 +1,17 @@
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function dummy = set(pacmodel, paceq, parameter, isnonzeromean)
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function dummy = set(pacmodel, paceq, variable, nonzeromean)
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% Copyright (C) 2019 Dynare Team
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% Provide information about long run levels of exogenous variables in PAC equation.
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%
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% INPUTS
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% - pacmodel [char] 1×n array, name of the PAC model
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% - paceq [char] 1×m array, name of the PAC equation
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% - variable [char] 1×p array, name of the variable (exogenous variable in the PAC equation)
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% - nonzeromean [double] scalar, mean of the exogenous variable
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%
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% OUPUTS
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% - dummy [double] empty array.
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% Copyright © 2019-2021 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -22,39 +33,39 @@ global M_
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eqtag = M_.pac.(pacmodel).tag_map{strcmp(paceq, M_.pac.(pacmodel).tag_map(:,1)),2};
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dummy = [];
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idp = find(strcmp(parameter, M_.param_names));
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if ~isempty(idp)
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ide = find(strcmp(variable, M_.endo_names));
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if ~isempty(ide)
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if isfield(M_.pac.(pacmodel).equations.(eqtag), 'additive') && length(M_.pac.(pacmodel).equations.(eqtag).additive.vars)>1
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if ~isfield(M_.pac.(pacmodel).equations.(eqtag).additive, 'bgp')
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M_.pac.(pacmodel).equations.(eqtag).additive.bgp = false(1, length(M_.pac.(pacmodel).equations.(eqtag).additive.params));
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M_.pac.(pacmodel).equations.(eqtag).additive.bgp = zeros(1, length(M_.pac.(pacmodel).equations.(eqtag).additive.params));
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end
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[isparam, ip] = ismember(idp, M_.pac.(pacmodel).equations.(eqtag).additive.params);
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if isparam
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M_.pac.(pacmodel).equations.(eqtag).additive.bgp(ip) = isnonzeromean;
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[isvar, ie] = ismember(ide, M_.pac.(pacmodel).equations.(eqtag).additive.vars);
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if isvar
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M_.pac.(pacmodel).equations.(eqtag).additive.bgp(ie) = nonzeromean;
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return
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end
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end
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if isfield(M_.pac.(pacmodel).equations.(eqtag), 'optim_additive')
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if ~isfield(M_.pac.(pacmodel).equations.(eqtag).optim_additive, 'bgp')
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M_.pac.(pacmodel).equations.(eqtag).optim_additive.bgp = false(1, length(M_.pac.(pacmodel).equations.(eqtag).optim_additive.params));
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M_.pac.(pacmodel).equations.(eqtag).optim_additive.bgp = zeros(1, length(M_.pac.(pacmodel).equations.(eqtag).optim_additive.params));
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end
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[isparam, ip] = ismember(idp, M_.pac.(pacmodel).equations.(eqtag).optim_additive.params);
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if isparam
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M_.pac.(pacmodel).equations.(eqtag).optim_additive.bgp(ip) = isnonzeromean;
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[isvar, ie] = ismember(ide, M_.pac.(pacmodel).equations.(eqtag).optim_additive.vars);
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if isvar
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M_.pac.(pacmodel).equations.(eqtag).optim_additive.bgp(ie) = nonzeromean;
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return
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end
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end
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if isfield(M_.pac.(pacmodel).equations.(eqtag), 'non_optimizing_behaviour')
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if ~isfield(M_.pac.(pacmodel).equations.(eqtag).non_optimizing_behaviour, 'bgp')
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M_.pac.(pacmodel).equations.(eqtag).non_optimizing_behaviour.bgp = false(1, length(M_.pac.(pacmodel).equations.(eqtag).non_optimizing_behaviour.params));
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M_.pac.(pacmodel).equations.(eqtag).non_optimizing_behaviour.bgp = zeros(1, length(M_.pac.(pacmodel).equations.(eqtag).non_optimizing_behaviour.params));
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end
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[isparam, ip] = ismember(idp, M_.pac.(pacmodel).equations.(eqtag).non_optimizing_behaviour.params);
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if isparam
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M_.pac.(pacmodel).equations.(eqtag).non_optimizing_behaviour.bgp(ip) = isnonzeromean;
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[isvar, ie] = ismember(ide, M_.pac.(pacmodel).equations.(eqtag).non_optimizing_behaviour.vars);
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if isvar
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M_.pac.(pacmodel).equations.(eqtag).non_optimizing_behaviour.bgp(ie) = nonzeromean;
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return
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end
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end
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warning('Parameter %s is not associated to an exogenous variable in equation %s.', parameter, paceq)
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warning('%s is not an exogenous variable in equation %s.', variable, paceq)
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else
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error('Parameter %s is unknown.', parameter)
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error('Endogenous variable %s is unknown.', variable)
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end
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@ -167,9 +167,9 @@ for e=1:number_of_pac_eq
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tmp = 0;
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for i=1:length(equations.(eqtag).optim_additive.params)
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if isnan(equations.(eqtag).optim_additive.params(i)) && equations.(eqtag).optim_additive.bgp(i)
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tmp = tmp + equations.(eqtag).optim_additive.scaling_factor(i);
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tmp = tmp + equations.(eqtag).optim_additive.scaling_factor(i)*equations.(eqtag).optim_additive.bgp(i);
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elseif ~isnan(equations.(eqtag).optim_additive.params(i)) && equations.(eqtag).optim_additive.bgp(i)
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tmp = tmp + DynareModel.params(equations.(eqtag).optim_additive.params(i))*equations.(eqtag).optim_additive.scaling_factor(i);
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tmp = tmp + DynareModel.params(equations.(eqtag).optim_additive.params(i))*equations.(eqtag).optim_additive.scaling_factor(i)*equations.(eqtag).optim_additive.bgp(i);
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end
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end
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cc = cc - gamma*tmp;
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@ -180,9 +180,9 @@ for e=1:number_of_pac_eq
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tmp = 0;
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for i=1:length(equations.(eqtag).non_optimizing_behaviour.params)
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if isnan(equations.(eqtag).non_optimizing_behaviour.params(i)) && equations.(eqtag).non_optimizing_behaviour.bgp(i)
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tmp = tmp + equations.(eqtag).non_optimizing_behaviour.scaling_factor(i);
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tmp = tmp + equations.(eqtag).non_optimizing_behaviour.scaling_factor(i)*equations.(eqtag).non_optimizing_behaviour.bgp(i);
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elseif ~isnan(equations.(eqtag).non_optimizing_behaviour.params(i)) && equations.(eqtag).non_optimizing_behaviour.bgp(i)
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tmp = tmp + DynareModel.params(equations.(eqtag).non_optimizing_behaviour.params(i))*equations.(eqtag).non_optimizing_behaviour.scaling_factor(i);
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tmp = tmp + DynareModel.params(equations.(eqtag).non_optimizing_behaviour.params(i))*equations.(eqtag).non_optimizing_behaviour.scaling_factor(i)*equations.(eqtag).non_optimizing_behaviour.bgp(i);
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end
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end
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cc = cc - (1.0-gamma)*tmp;
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@ -193,9 +193,9 @@ for e=1:number_of_pac_eq
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tmp = 0;
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for i=1:length(equations.(eqtag).additive.params)
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if isnan(equations.(eqtag).additive.params(i)) && equations.(eqtag).additive.bgp(i)
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tmp = tmp + equations.(eqtag).additive.scaling_factor(i);
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tmp = tmp + equations.(eqtag).additive.scaling_factor(i)*equations.(eqtag).additive.bgp(i);
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elseif ~isnan(equations.(eqtag).additive.params(i)) && equations.(eqtag).additive.bgp(i)
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tmp = tmp + DynareModel.params(equations.(eqtag).additive.params(i))*equations.(eqtag).additive.scaling_factor(i);
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tmp = tmp + DynareModel.params(equations.(eqtag).additive.params(i))*equations.(eqtag).additive.scaling_factor(i)*equations.(eqtag).additive.bgp(i);
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end
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end
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cc = cc - tmp;
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@ -1,6 +1,6 @@
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// --+ options: json=compute, stochastic +--
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var x1 x2 x1bar x2bar z y x u v s;
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var x1 x2 x1bar x2bar z y x u v s dx2 dv;
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varexo ex1 ex2 ex1bar ex2bar ez ey ex eu ev es;
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@ -45,7 +45,7 @@ lambda = 0.5; // Share of optimizing agents.
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trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
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pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
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pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman, growth=.25*diff(x1(-1))+.25*diff(x1(-2))+.25*diff(x1(-3))+.25*diff(x1(-4)));
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model;
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@ -77,8 +77,11 @@ x1bar = x1bar(-1) + ex1bar;
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x2bar = x2bar(-1) + ex2bar;
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[name='zpac']
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diff(z) = lambda*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman) + c_z_s*s + c_z_dv*diff(v) ) + (1-lambda)*( cy*y + cx*x) + c_z_u*u + c_z_dx2*diff(x2) + ez;
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diff(z) = lambda*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman) + c_z_s*s + c_z_dv*dv ) + (1-lambda)*( cy*y + cx*x) + c_z_u*u + c_z_dx2*dx2 + ez;
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dx2 = diff(x2);
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dv = diff(v);
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end;
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shocks;
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@ -97,46 +100,46 @@ end;
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// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
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pac.initialize('pacman');
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// Update the parameters of the PAC expectation model (h0 and h1 vectors).
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// Exogenous variables with non zero mean:
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pac.bgp.set('pacman', 'zpac', 's', .2);
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pac.bgp.set('pacman', 'zpac', 'x', .2);
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pac.bgp.set('pacman', 'zpac', 'dx2', .2);
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// Update the parameters of the PAC expectation model (h0 and h1 vectors, growth neutrality correction).
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pac.update.expectation('pacman');
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// Exogenous variables with non zero mean:
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pac.bgp.set('pacman', 'zpac', 'c_z_s', true);
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pac.bgp.set('pacman', 'zpac', 'cx', true);
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pac.bgp.set('pacman', 'zpac', 'c_z_dx2', true);
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id = find(strcmp('c_z_s', M_.param_names));
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id = find(id==M_.pac.pacman.equations.eq0.additive.params);
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id = find(strcmp('s', M_.endo_names));
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id = find(id==M_.pac.pacman.equations.eq0.additive.vars);
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if ~pac.bgp.get('pacman', 'zpac', 'additive', id)
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error('bgp field in additive is wrong.')
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end
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id = find(strcmp('c_z_dv', M_.param_names));
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id = find(id==M_.pac.pacman.equations.eq0.additive.params);
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id = find(strcmp('dv', M_.endo_names));
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id = find(id==M_.pac.pacman.equations.eq0.additive.vars);
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if pac.bgp.get('pacman', 'zpac', 'additive', id)
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error('bgp field in additive is wrong.')
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end
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id = find(strcmp('cx', M_.param_names));
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id = find(id==M_.pac.pacman.equations.eq0.non_optimizing_behaviour.params);
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id = find(strcmp('x', M_.endo_names));
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id = find(id==M_.pac.pacman.equations.eq0.non_optimizing_behaviour.vars);
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if ~pac.bgp.get('pacman', 'zpac', 'non_optimizing_behaviour', id)
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error('bgp field in non_optimizing_behaviour is wrong.')
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end
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id = find(strcmp('cy', M_.param_names));
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id = find(id==M_.pac.pacman.equations.eq0.non_optimizing_behaviour.params);
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id = find(strcmp('y', M_.endo_names));
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id = find(id==M_.pac.pacman.equations.eq0.non_optimizing_behaviour.vars);
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if pac.bgp.get('pacman', 'zpac', 'non_optimizing_behaviour', id)
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error('bgp field in non_optimizing_behaviour is wrong.')
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end
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id = find(strcmp('c_z_dx2', M_.param_names));
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id = find(id==M_.pac.pacman.equations.eq0.optim_additive.params);
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id = find(strcmp('dx2', M_.endo_names));
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id = find(id==M_.pac.pacman.equations.eq0.optim_additive.vars);
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if ~pac.bgp.get('pacman', 'zpac', 'optim_additive', id)
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error('bgp field in optim_additive is wrong.')
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end
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id = find(strcmp('c_z_u', M_.param_names));
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id = find(id==M_.pac.pacman.equations.eq0.additive.params);
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id = find(strcmp('u', M_.endo_names));
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id = find(id==M_.pac.pacman.equations.eq0.additive.vars);
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if pac.bgp.get('pacman', 'zpac', 'optim_additive', id)
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error('bgp field in optim_additive is wrong.')
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end
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end
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