69 lines
1.4 KiB
Modula-2
69 lines
1.4 KiB
Modula-2
// --+ options: json=compute, stochastic +--
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var y x z v;
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varexo ex ey ez ;
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parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 d_y; // VAR parameters
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parameters beta e_c_m c_z_1 c_z_2; // PAC equation parameters
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a_y_1 = .2;
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a_y_2 = .3;
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b_y_1 = .1;
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b_y_2 = .4;
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b_x_1 = -.1;
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b_x_2 = -.2;
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d_y = .5;
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beta = .9;
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e_c_m = .1;
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c_z_1 = .7;
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c_z_2 = -.3;
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var_model(model_name=toto, structural, eqtags=['eq:x', 'eq:y']);
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pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
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pac_target_info(pacman);
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target v;
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auxname_target_nonstationary vns;
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component y;
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auxname pv_y_;
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kind ll;
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component log(x);
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growth diff(log(x(-2)));
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auxname pv_dx_;
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kind dd;
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end;
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model;
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[name='eq:y']
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y = a_y_1*y(-1) + a_y_2*diff(log(x(-1))) + b_y_1*y(-2) + b_y_2*diff(log(x(-2))) + ey ;
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[name='eq:x']
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diff(log(x)) = b_x_1*y(-2) + b_x_2*diff(log(x(-1))) + ex ;
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[name='eq:v']
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v = log(x) + d_y*y ;
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[name='eq:pac']
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diff(z) = e_c_m*(pac_target_nonstationary(pacman)-z(-1)) + c_z_1*diff(z(-1)) + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
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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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// Update the parameters of the PAC expectation model (h0 and h1 vectors).
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pac.update.expectation('pacman');
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// Print expanded PAC_EXPECTATION term.
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pac.print('pacman', 'eq:pac');
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