70 lines
2.0 KiB
Modula-2
70 lines
2.0 KiB
Modula-2
// --+ options: stochastic,json=compute +--
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var foo z x y;
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varexo e_x e_y e_z;
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parameters a b c d e f beta ;
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a = .9;
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b = -.2;
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c = .3;
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f = .8;
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d = .5;
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e = .4;
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beta = 1/(1+.02);
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// Define a VAR model from a subset of equations in the model block.
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var_model(structural, model_name = toto, eqtags = [ 'X' 'Y' 'Z' ]);
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// Define a VAR_EXPECTATION_MODEL
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var_expectation_model(model_name = varexp, expression = diff(log(x)), auxiliary_model_name = toto, horizon = 1, discount = beta) ;
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model;
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[ name = 'X' ]
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diff(log(x)) = b*diff(z) + a*diff(log(x(-1))) + (1-a)*diff(log(x(-2))) + c*diff(z(-2)) + e_x;
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[ name = 'Z' ]
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diff(z) = f*(diff(z(-1))-diff(log(x)))+c*diff(z(-2)) + e_z;
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[ name = 'Y' ]
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log(y) = diff(log(x)) + d*log(y(-2)) + e*diff(z(-1)) + e_y;
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foo = var_expectation(varexp);
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end;
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// Initialize the VAR expectation model, will build the companion matrix of the VAR.
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var_expectation.initialize('varexp')
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// Update VAR_EXPECTATION reduced form parameters
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var_expectation.update('varexp');
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// Print expanded VAR_EXPECTATION expression in a file (to be included in substitution.mod).
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var_expectation.print('varexp');
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shocks;
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var e_x = .01;
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var e_y = .01;
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var e_z = .01;
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end;
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verbatim;
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initialconditions =zeros(3,4);
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initialconditions(3,1) = .1; % foo(-1)
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initialconditions(:,2) = .2; % y(-1)
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initialconditions(3,3) = .3; % z(-1)
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initialconditions(2,3) = .4; % z(-2)
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initialconditions(3,4) = .5; % x(-1)
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initialconditions(2,4) = .6; % x(-2)
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initialconditions(1,4) = .7; % x(-3)
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initialconditions = ...
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dseries(initialconditions, dates('2000Q1'), {'foo', 'y','z', 'x'});
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set_dynare_seed('default');
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ts = simul_backward_model(initialconditions, 15);
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foo = ts.foo.data;
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% Evaluate the (VAR) expectation term
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ts{'toto'} = example1.var_expectations.varexp.evaluate(ts);
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% Check that the evaluation is correct.
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range = dates('2000Q4'):dates('2004Q2');
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if max(abs(ts(range).foo.data-ts(range).toto.data))>1e-5
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error('Expectation term evaluations do not match!')
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end
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end;
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