218 lines
6.1 KiB
Modula-2
218 lines
6.1 KiB
Modula-2
// --+ options: json=compute, stochastic +--
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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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parameters
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rho_1 rho_2 rho_3 rho_4
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a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
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a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
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e_c_m c_z_1 c_z_2 c_z_dx2 c_z_u c_z_dv c_z_s cx cy beta
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lambda;
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rho_1 = .9;
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rho_2 = -.2;
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rho_3 = .4;
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rho_4 = -.3;
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a_x1_0 = -.9;
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a_x1_1 = .4;
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a_x1_2 = .3;
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a_x1_x2_1 = .1;
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a_x1_x2_2 = .2;
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a_x2_0 = -.9;
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a_x2_1 = .2;
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a_x2_2 = -.1;
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a_x2_x1_1 = -.1;
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a_x2_x1_2 = .2;
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beta = .2;
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e_c_m = .5;
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c_z_1 = .2;
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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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cx = 1.0;
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cy = 1.0;
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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, 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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[name='eq:s']
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s = .3*s(-1) - .1*s(-2) + es;
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[name='eq:diff(v)']
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diff(v) = .5*diff(v(-1)) + ev;
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[name='eq:u']
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u = .5*u(-1) - .2*u(-2) + eu;
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[name='eq:y']
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y = rho_1*y(-1) + rho_2*y(-2) + ey;
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[name='eq:x']
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x = rho_3*x(-1) + rho_4*x(-2) + ex;
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[name='eq:x1']
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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;
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[name='eq:x2']
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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;
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[name='eq:x1bar']
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x1bar = x1bar(-1) + ex1bar;
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[name='eq:x2bar']
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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*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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var ex1 = 1.0;
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var ex2 = 1.0;
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var ex1bar = 1.0;
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var ex2bar = 1.0;
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var ez = 1.0;
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var ey = 0.1;
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var ex = 0.1;
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var eu = 0.05;
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var ev = 0.05;
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var es = 0.07;
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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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// Exogenous variables with non zero mean:
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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.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.additive.vars);
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if ~isempty(id)
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error('Variable s should not be under M_.pac.pacman.additive.')
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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.non_optimizing_behaviour.vars);
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if ~isempty(id)
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error('Variable s should not be under M_.pac.pacman.non_optimizing_behaviour.')
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end
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id = find(strcmp('dv', M_.endo_names));
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id = find(id==M_.pac.pacman.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 dv is wrong.')
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end
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id = find(strcmp('dv', M_.endo_names));
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id = find(id==M_.pac.pacman.additive.vars);
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if ~isempty(id)
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error('Variable dv should not be under M_.pac.pacman.additive.')
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end
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id = find(strcmp('dv', M_.endo_names));
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id = find(id==M_.pac.pacman.non_optimizing_behaviour.vars);
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if ~isempty(id)
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error('Variable dv should not be under M_.pac.pacman.non_optimizing_behaviour.')
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end
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id = find(strcmp('x', M_.endo_names));
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id = find(id==M_.pac.pacman.non_optimizing_behaviour.vars);
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if ~(abs(pac.bgp.get('pacman', 'zpac', 'non_optimizing_behaviour', id))<1e-12)
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error('bgp field in non_optimizing_behaviour for variable x is wrong.')
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end
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id = find(strcmp('x', M_.endo_names));
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id = find(id==M_.pac.pacman.optim_additive.vars);
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if ~isempty(id)
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error('Variable x should not be under M_.pac.pacman.optim_additive.')
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end
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id = find(strcmp('x', M_.endo_names));
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id = find(id==M_.pac.pacman.additive.vars);
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if ~isempty(id)
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error('Variable x should not be under M_.pac.pacman.additive.')
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end
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id = find(strcmp('y', M_.endo_names));
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id = find(id==M_.pac.pacman.non_optimizing_behaviour.vars);
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if ~islogical(pac.bgp.get('pacman', 'zpac', 'non_optimizing_behaviour', id)) || pac.bgp.get('pacman', 'zpac', 'non_optimizing_behaviour', id)
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error('bgp field in non_optimizing_behaviour for variable y is wrong.')
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end
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id = find(strcmp('y', M_.endo_names));
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id = find(id==M_.pac.pacman.optim_additive.vars);
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if ~isempty(id)
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error('Variable y should not be under M_.pac.pacman.optim_additive.')
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end
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id = find(strcmp('y', M_.endo_names));
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id = find(id==M_.pac.pacman.additive.vars);
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if ~isempty(id)
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error('Variable y should not be under M_.pac.pacman.additive.')
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end
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id = find(strcmp('dx2', M_.endo_names));
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id = find(id==M_.pac.pacman.additive.vars);
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if ~(abs(pac.bgp.get('pacman', 'zpac', 'additive', id))<1e-12)
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error('bgp field in additive is wrong.')
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end
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id = find(strcmp('dx2', M_.endo_names));
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id = find(id==M_.pac.pacman.non_optimizing_behaviour.vars);
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if ~isempty(id)
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error('Variable y should not be under M_.pac.pacman.non_optimizing_behaviour.')
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end
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id = find(strcmp('dx2', M_.endo_names));
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id = find(id==M_.pac.pacman.optim_additive.vars);
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if ~isempty(id)
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error('Variable dx2 should not be under M_.pac.pacman.optim_additive.')
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end
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id = find(strcmp('u', M_.endo_names));
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id = find(id==M_.pac.pacman.additive.vars);
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if ~islogical(pac.bgp.get('pacman', 'zpac', 'additive', id)) || pac.bgp.get('pacman', 'zpac', 'additive', id)
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error('bgp field in additive for variable u is wrong.')
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end
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id = find(strcmp('u', M_.endo_names));
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id = find(id==M_.pac.pacman.non_optimizing_behaviour.vars);
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if ~isempty(id)
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error('Variable u should not be under M_.pac.pacman.non_optimizing_behaviour.')
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end
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id = find(strcmp('u', M_.endo_names));
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id = find(id==M_.pac.pacman.optim_additive.vars);
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if ~isempty(id)
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error('Variable u should not be under M_.pac.pacman.optim_additive.')
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end
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