Fix growth neutrality correction in PAC equations.
- Force long run levels of the exogenous variables to be zero or g (the BGP growth rate of the LHS endogenous variable). - Fix the correction of the correction for the share λ (aak γ). - Provisions for the case where the long run level of the exogenous variable is different from 0 or g (see tmp1 and ll which should be added to the growth neutrality correction as a constant). (cherry picked from commit a4423d734e1df1d4ee09c7225d7fd610e0d94cd1)pac-components
parent
96c24b3052
commit
f778e3c1ce
GPG Key ID:
295C1FE89E17EB3C
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@ -76,11 +76,72 @@ for e=1:number_of_pac_eq
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[G, alpha, beta] = buildGmatrixWithAlphaAndBeta(params);
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M_.params(pac_equation.mce.alpha) = alpha;
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if isfield(pacmodel, 'growth_neutrality_param_index')
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if isfield(equations.(eqtag), 'non_optimizing_behaviour')
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gamma = M_.params(equations.(eqtag).share_of_optimizing_agents_index);
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else
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gamma = 1.0;
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end
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A = [alpha; 1];
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A_1 = polyval(A, 1.0);
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A_b = polyval(A, beta);
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m = length(alpha);
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d = A_1*A_b*(iota(m, m)'*inv((eye(m)-G)*(eye(m)-G))*iota(m, m));
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M_.params(pacmodel.growth_neutrality_param_index) = 1-sum(params(2:end-1))-d;
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cc = 1/gamma-(sum(params(2:end-1))+d);
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ll = 0;
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if isfield(equations.(eqtag), 'optim_additive')
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% Exogenous variables are present in the λ part (optimizing agents).
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tmp0 = 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)) && islogical(equations.(eqtag).optim_additive.bgp{i}) && equations.(eqtag).optim_additive.bgp{i}
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tmp0 = tmp0 + equations.(eqtag).optim_additive.scaling_factor(i);
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elseif ~isnan(equations.(eqtag).optim_additive.params(i)) && islogical(equations.(eqtag).optim_additive.bgp{i}) && equations.(eqtag).optim_additive.bgp{i}
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tmp0 = tmp0 + M_.params(equations.(eqtag).optim_additive.params(i))*equations.(eqtag).optim_additive.scaling_factor(i);
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elseif ~islogical(equations.(eqtag).optim_additive.bgp{i})
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error('It is not possible to provide a value for the mean of an exogenous variable appearing in the optimal part of the PAC equation.')
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end
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end
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cc = cc - tmp0;
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end
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if gamma<1
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equations.(eqtag).('non_optimizing_behaviour')
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if isfield(equations.(eqtag), 'non_optimizing_behaviour') && isfield(equations.(eqtag).non_optimizing_behaviour, 'params')
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% Exogenous variables are present in the 1-λ part (rule of thumb agents).
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tmp0 = 0;
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tmp1 = 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)) && islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && equations.(eqtag).non_optimizing_behaviour.bgp{i}
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tmp0 = tmp0 + equations.(eqtag).non_optimizing_behaviour.scaling_factor(i);
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elseif ~isnan(equations.(eqtag).non_optimizing_behaviour.params(i)) && islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && equations.(eqtag).non_optimizing_behaviour.bgp{i}
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tmp0 = tmp0 + M_.params(equations.(eqtag).non_optimizing_behaviour.params(i))*equations.(eqtag).non_optimizing_behaviour.scaling_factor(i);
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elseif ~islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && isnumeric(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && isnan(equations.(eqtag).non_optimizing_behaviour.params(i))
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tmp1 = tmp1 + equations.(eqtag).non_optimizing_behaviour.scaling_factor(i)*equations.(eqtag).non_optimizing_behaviour.bgp{i};
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elseif ~islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && isnumeric(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && ~isnan(equations.(eqtag).non_optimizing_behaviour.params(i))
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tmp1 = tmp1 + equations.(eqtag).non_optimizing_behaviour.scaling_factor(i)*equations.(eqtag).non_optimizing_behaviour.params(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-gamma)*tmp0/gamma;
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ll = -(1.0-gamma)*tmp1/gamma; % TODO: ll should be added as a constant in the PAC equation (under the λ part) when unrolling pac_expectation.
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end
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end
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if isfield(equations.(eqtag), 'additive')
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% Exogenous variables are present outside of the λ and (1-λ) parts (or we have exogenous variables in a "pure" PAC equation.
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tmp0 = 0;
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tmp1 = 0;
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for i=1:length(equations.(eqtag).additive.params)
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if isnan(equations.(eqtag).additive.params(i)) && islogical(equations.(eqtag).additive.bgp{i}) && equations.(eqtag).additive.bgp{i}
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tmp0 = tmp0 + equations.(eqtag).additive.scaling_factor(i);
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elseif ~isnan(equations.(eqtag).additive.params(i)) && islogical(equations.(eqtag).additive.bgp{i}) && equations.(eqtag).additive.bgp{i}
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tmp0 = tmp0 + M_.params(equations.(eqtag).additive.params(i))*equations.(eqtag).additive.scaling_factor(i);
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elseif ~islogical(equations.(eqtag).additive.bgp{i}) && isnumeric(equations.(eqtag).additive.bgp{i}) && isnan(equations.(eqtag).additive.params(i))
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tmp1 = tmp1 + equations.(eqtag).additive.scaling_factor(i)*equations.(eqtag).additive.bgp{i};
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elseif ~islogical(equations.(eqtag).additive.bgp{i}) && isnumeric(equations.(eqtag).additive.bgp{i}) && ~isnan(equations.(eqtag).additive.params(i))
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tmp1 = tmp1 + equations.(eqtag).additive.scaling_factor(i)*equations.(eqtag).additive.params(i)*equations.(eqtag).additive.bgp{i};
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end
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end
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cc
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cc = cc - tmp0/gamma;
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ll = ll - tmp1/gamma; % TODO: ll should be added as a constant in the PAC equation (under the λ part) when unrolling pac_expectation.
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end
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M_.params(pacmodel.growth_neutrality_param_index) = cc; % Multiplies the variable or expression provided though the growth option in command pac_model.
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end
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end
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@ -173,7 +173,7 @@ for e=1:number_of_pac_eq
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if ~isempty(equations.(eqtag).h1_param_indices)
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DynareModel.params(equations.(eqtag).h1_param_indices) = .0;
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end
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end
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end
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% Update the parameter related to the growth neutrality correction.
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if isfield(equations.(eqtag), 'non_optimizing_behaviour')
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gamma = DynareModel.params(equations.(eqtag).share_of_optimizing_agents_index);
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@ -185,46 +185,61 @@ for e=1:number_of_pac_eq
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% there is no exogenous variables in the model and in the
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% absence of non optimizing agents.
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gg = -(growthneutrality-1); % Finite sum of autoregressive parameters + infinite sum of the coefficients in the PAC expectation term.
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cc = 1.0-gamma*gg; % First adjustment of the growth neutrality correction (should also be divided by gamma, done below at the end of this section).
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% We may have to further change the correction if we have nonzero mean exogenous variables.
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cc = 1.0/gamma-gg; % First adjustment of the growth neutrality correction (should also be divided by gamma, done below at the end of this section).
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% We may have to further change the correction if we have nonzero mean exogenous variables.
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ll = 0.0;
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if isfield(equations.(eqtag), 'optim_additive')
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% Exogenous variables are present in the λ part (optimizing agents).
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tmp = 0;
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tmp0 = 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)*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)*equations.(eqtag).optim_additive.bgp(i);
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if isnan(equations.(eqtag).optim_additive.params(i)) && islogical(equations.(eqtag).optim_additive.bgp{i}) && equations.(eqtag).optim_additive.bgp{i}
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tmp0 = tmp0 + equations.(eqtag).optim_additive.scaling_factor(i);
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elseif ~isnan(equations.(eqtag).optim_additive.params(i)) && islogical(equations.(eqtag).optim_additive.bgp{i}) && equations.(eqtag).optim_additive.bgp{i}
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tmp0 = tmp0 + DynareModel.params(equations.(eqtag).optim_additive.params(i))*equations.(eqtag).optim_additive.scaling_factor(i);
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elseif ~islogical(equations.(eqtag).optim_additive.bgp{i})
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error('It is not possible to provide a value for the mean of an exogenous variable appearing in the optimal part of the PAC equation.')
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end
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end
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cc = cc - gamma*tmp;
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cc = cc - tmp0;
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end
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if gamma<1
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if isfield(equations.(eqtag), 'non_optimizing_behaviour.params')
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% Exogenous variables are present in the 1-λ part (rule of thumb agents).
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tmp = 0;
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tmp0 = 0;
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tmp1 = 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)*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)*equations.(eqtag).non_optimizing_behaviour.bgp(i);
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if isnan(equations.(eqtag).non_optimizing_behaviour.params(i)) && islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && equations.(eqtag).non_optimizing_behaviour.bgp{i}
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tmp0 = tmp0 + equations.(eqtag).non_optimizing_behaviour.scaling_factor(i);
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elseif ~isnan(equations.(eqtag).non_optimizing_behaviour.params(i)) && islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && equations.(eqtag).non_optimizing_behaviour.bgp{i}
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tmp0 = tmp0 + DynareModel.params(equations.(eqtag).non_optimizing_behaviour.params(i))*equations.(eqtag).non_optimizing_behaviour.scaling_factor(i);
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elseif ~islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && isnumeric(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && isnan(equations.(eqtag).non_optimizing_behaviour.params(i))
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tmp1 = tmp1 + equations.(eqtag).non_optimizing_behaviour.scaling_factor(i)*equations.(eqtag).non_optimizing_behaviour.bgp{i};
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elseif ~islogical(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && isnumeric(equations.(eqtag).non_optimizing_behaviour.bgp{i}) && ~isnan(equations.(eqtag).non_optimizing_behaviour.params(i))
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tmp1 = tmp1 + equations.(eqtag).non_optimizing_behaviour.scaling_factor(i)*equations.(eqtag).non_optimizing_behaviour.params(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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cc = cc - (1.0-gamma)*tmp0/gamma;
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ll = -(1.0-gamma)*tmp1/gamma; % TODO: ll should be added as a constant in the PAC equation (under the λ part) when unrolling pac_expectation.
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end
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end
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if isfield(equations.(eqtag), 'additive')
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% Exogenous variables are present outside of the λ and (1-λ) parts (or we have exogenous variables in a "pure" PAC equation.
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tmp = 0;
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tmp0 = 0;
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tmp1 = 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)*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)*equations.(eqtag).additive.bgp(i);
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if isnan(equations.(eqtag).additive.params(i)) && islogical(equations.(eqtag).additive.bgp{i}) && equations.(eqtag).additive.bgp{i}
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tmp0 = tmp0 + equations.(eqtag).additive.scaling_factor(i);
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elseif ~isnan(equations.(eqtag).additive.params(i)) && islogical(equations.(eqtag).additive.bgp{i}) && equations.(eqtag).additive.bgp{i}
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tmp0 = tmp0 + DynareModel.params(equations.(eqtag).additive.params(i))*equations.(eqtag).additive.scaling_factor(i);
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elseif ~islogical(equations.(eqtag).additive.bgp{i}) && isnumeric(equations.(eqtag).additive.bgp{i}) && isnan(equations.(eqtag).additive.params(i))
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tmp1 = tmp1 + equations.(eqtag).additive.scaling_factor(i)*equations.(eqtag).additive.bgp{i};
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elseif ~islogical(equations.(eqtag).additive.bgp{i}) && isnumeric(equations.(eqtag).additive.bgp{i}) && ~isnan(equations.(eqtag).additive.params(i))
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tmp1 = tmp1 + equations.(eqtag).additive.scaling_factor(i)*equations.(eqtag).additive.params(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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cc = cc - tmp0/gamma;
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ll = ll - tmp1/gamma; % TODO: ll should be added as a constant in the PAC equation (under the λ part) when unrolling pac_expectation.
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end
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DynareModel.params(pacmodel.growth_neutrality_param_index) = cc/gamma;
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DynareModel.params(pacmodel.growth_neutrality_param_index) = cc; % Multiplies the variable or expression provided though the growth option in command pac_model.
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end
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end
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@ -8,7 +8,7 @@ function initialize(pacmodel)
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% OUTPUTS
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% None
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% Copyright (C) 2018-2019 Dynare Team
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% Copyright © 2018-2021 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -37,12 +37,12 @@ end
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% Append default bgp fields if needed.
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for i=1:rows(M_.pac.(pacmodel).tag_map)
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if isfield(M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}), 'additive')
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M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).additive.bgp = false(1, length(M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).additive.params));
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M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).additive.bgp = repmat({false}, 1, length(M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).additive.params));
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end
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if isfield(M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}), 'optim_additive')
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M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).optim_additive.bgp = false(1, length(M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).optim_additive.params));
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M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).optim_additive.bgp = repmat({false}, 1, length(M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).optim_additive.params));
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end
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if isfield(M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}), 'non_optimizing_behaviour')
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M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).non_optimizing_behaviour.bgp = false(1, length(M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).non_optimizing_behaviour.params));
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M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).non_optimizing_behaviour.bgp = repmat({false}, 1, length(M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{i,2}).non_optimizing_behaviour.params));
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end
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end
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@ -1,6 +1,6 @@
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function dummy = get(pacmodel, paceq, kind, id)
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% Copyright (C) 2019 Dynare Team
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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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@ -19,4 +19,4 @@ function dummy = get(pacmodel, paceq, kind, id)
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global M_
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dummy = M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{strcmp(paceq, M_.pac.(pacmodel).tag_map(:,1)),2}).(kind).bgp(id);
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dummy = M_.pac.(pacmodel).equations.(M_.pac.(pacmodel).tag_map{strcmp(paceq, M_.pac.(pacmodel).tag_map(:,1)),2}).(kind).bgp{id};
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@ -59,7 +59,7 @@ function [M_, done] = writebgpfield(type, pacmodel, eqtag, ide, xflag, nonzerome
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done = false;
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if isfield(M_.pac.(pacmodel).equations.(eqtag), type)
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if ~isfield(M_.pac.(pacmodel).equations.(eqtag).additive, 'bgp')
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M_.pac.(pacmodel).equations.(eqtag).(type).bgp = zeros(1, length(M_.pac.(pacmodel).equations.(eqtag).(type).params));
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M_.pac.(pacmodel).equations.(eqtag).(type).bgp = repmat({false}, 1, length(M_.pac.(pacmodel).equations.(eqtag).(type).params));
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end
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[isvar, ie] = ismember(ide, M_.pac.(pacmodel).equations.(eqtag).(type).vars);
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if isvar
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@ -68,7 +68,7 @@ if isfield(M_.pac.(pacmodel).equations.(eqtag), type)
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else
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assert(M_.pac.(pacmodel).equations.(eqtag).(type).isendo(ie), 'Variable type issue.')
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end
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M_.pac.(pacmodel).equations.(eqtag).(type).bgp(ie) = nonzeromean;
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M_.pac.(pacmodel).equations.(eqtag).(type).bgp{ie} = nonzeromean;
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done = true;
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end
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end
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@ -36,7 +36,7 @@ c_z_2 = -.1;
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c_z_dx2 = .3;
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c_z_u = .3;
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c_z_dv = .4;
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c_z_s = -.2;
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c_z_s = -.2;
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cx = 1.0;
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cy = 1.0;
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@ -101,45 +101,117 @@ end;
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pac.initialize('pacman');
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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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pac.bgp.set('pacman', 'zpac', 's', true);
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pac.bgp.set('pacman', 'zpac', 'x', .0);
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pac.bgp.set('pacman', 'zpac', 'dx2', .0);
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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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id = find(strcmp('s', 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 for variable s is wrong.')
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end
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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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if ~isempty(id)
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error('Variable s should not be under M_.pac.pacman.equations.eq0.additive.')
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end
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|
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id = find(strcmp('s', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.non_optimizing_behaviour.vars);
|
||||
if ~isempty(id)
|
||||
error('Variable s should not be under M_.pac.pacman.equations.eq0.non_optimizing_behaviour.')
|
||||
end
|
||||
|
||||
id = find(strcmp('dv', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.optim_additive.vars);
|
||||
if pac.bgp.get('pacman', 'zpac', 'optim_additive', id)
|
||||
error('bgp field in optim_additive for variable dv is wrong.')
|
||||
end
|
||||
|
||||
id = find(strcmp('dv', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.additive.vars);
|
||||
if pac.bgp.get('pacman', 'zpac', 'additive', id)
|
||||
error('bgp field in additive is wrong.')
|
||||
if ~isempty(id)
|
||||
error('Variable dv should not be under M_.pac.pacman.equations.eq0.additive.')
|
||||
end
|
||||
|
||||
id = find(strcmp('dv', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.non_optimizing_behaviour.vars);
|
||||
if ~isempty(id)
|
||||
error('Variable dv should not be under M_.pac.pacman.equations.eq0.non_optimizing_behaviour.')
|
||||
end
|
||||
|
||||
id = find(strcmp('x', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.non_optimizing_behaviour.vars);
|
||||
if ~pac.bgp.get('pacman', 'zpac', 'non_optimizing_behaviour', id)
|
||||
error('bgp field in non_optimizing_behaviour is wrong.')
|
||||
if ~(abs(pac.bgp.get('pacman', 'zpac', 'non_optimizing_behaviour', id))<1e-12)
|
||||
error('bgp field in non_optimizing_behaviour for variable x is wrong.')
|
||||
end
|
||||
|
||||
id = find(strcmp('x', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.optim_additive.vars);
|
||||
if ~isempty(id)
|
||||
error('Variable x should not be under M_.pac.pacman.equations.eq0.optim_additive.')
|
||||
end
|
||||
|
||||
id = find(strcmp('x', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.additive.vars);
|
||||
if ~isempty(id)
|
||||
error('Variable x should not be under M_.pac.pacman.equations.eq0.additive.')
|
||||
end
|
||||
|
||||
id = find(strcmp('y', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.non_optimizing_behaviour.vars);
|
||||
if pac.bgp.get('pacman', 'zpac', 'non_optimizing_behaviour', id)
|
||||
error('bgp field in non_optimizing_behaviour is wrong.')
|
||||
if ~islogical(pac.bgp.get('pacman', 'zpac', 'non_optimizing_behaviour', id)) || pac.bgp.get('pacman', 'zpac', 'non_optimizing_behaviour', id)
|
||||
error('bgp field in non_optimizing_behaviour for variable y is wrong.')
|
||||
end
|
||||
|
||||
id = find(strcmp('y', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.optim_additive.vars);
|
||||
if ~isempty(id)
|
||||
error('Variable y should not be under M_.pac.pacman.equations.eq0.optim_additive.')
|
||||
end
|
||||
|
||||
id = find(strcmp('y', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.additive.vars);
|
||||
if ~isempty(id)
|
||||
error('Variable y should not be under M_.pac.pacman.equations.eq0.additive.')
|
||||
end
|
||||
|
||||
id = find(strcmp('dx2', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.additive.vars);
|
||||
if ~(abs(pac.bgp.get('pacman', 'zpac', 'additive', id))<1e-12)
|
||||
error('bgp field in additive is wrong.')
|
||||
end
|
||||
|
||||
id = find(strcmp('dx2', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.non_optimizing_behaviour.vars);
|
||||
if ~isempty(id)
|
||||
error('Variable y should not be under M_.pac.pacman.equations.eq0.non_optimizing_behaviour.')
|
||||
end
|
||||
|
||||
id = find(strcmp('dx2', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.optim_additive.vars);
|
||||
if ~pac.bgp.get('pacman', 'zpac', 'optim_additive', id)
|
||||
error('bgp field in optim_additive is wrong.')
|
||||
if ~isempty(id)
|
||||
error('Variable dx2 should not be under M_.pac.pacman.equations.eq0.optim_additive.')
|
||||
end
|
||||
|
||||
id = find(strcmp('u', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.additive.vars);
|
||||
if pac.bgp.get('pacman', 'zpac', 'optim_additive', id)
|
||||
error('bgp field in optim_additive is wrong.')
|
||||
if ~islogical(pac.bgp.get('pacman', 'zpac', 'additive', id)) || pac.bgp.get('pacman', 'zpac', 'additive', id)
|
||||
error('bgp field in additive for variable u is wrong.')
|
||||
end
|
||||
|
||||
id = find(strcmp('u', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.non_optimizing_behaviour.vars);
|
||||
if ~isempty(id)
|
||||
error('Variable u should not be under M_.pac.pacman.equations.eq0.non_optimizing_behaviour.')
|
||||
end
|
||||
|
||||
id = find(strcmp('u', M_.endo_names));
|
||||
id = find(id==M_.pac.pacman.equations.eq0.optim_additive.vars);
|
||||
if ~isempty(id)
|
||||
error('Variable u should not be under M_.pac.pacman.equations.eq0.optim_additive.')
|
||||
end
|
||||
|
|
|
|||
Loading…
Reference in New Issue