149 lines
3.8 KiB
Modula-2
149 lines
3.8 KiB
Modula-2
/* Tests perfect_foresight_with_expectation_errors_{setup,solver}
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using the shocks(learnt_in=…) and endval(learnt_in=…) syntax */
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var c k;
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varexo x;
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parameters alph gam delt bet aa;
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alph=0.5;
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gam=0.5;
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delt=0.02;
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bet=0.05;
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aa=0.5;
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model;
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c + k - aa*x*k(-1)^alph - (1-delt)*k(-1);
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c^(-gam) - (1+bet)^(-1)*(aa*alph*x(+1)*k^(alph-1) + 1 - delt)*c(+1)^(-gam);
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end;
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initval;
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x = 1;
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k = ((delt+bet)/(1.0*aa*alph))^(1/(alph-1));
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c = aa*k^alph-delt*k;
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end;
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steady;
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check;
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shocks(learnt_in = 1);
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var x;
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periods 1:2;
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values 1.2;
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end;
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shocks(learnt_in = 2);
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var x;
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periods 2;
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add 0.1;
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end;
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endval(learnt_in = 2);
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x = 1.1;
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end;
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shocks(learnt_in = 3);
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var x;
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periods 3 7;
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values 1.4 1.5;
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end;
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endval(learnt_in = 3);
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x += 0.1;
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end;
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// Dummy block, that will be overwritten by the next one
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shocks(learnt_in = 6);
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var x;
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periods 6:8;
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values 10;
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end;
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shocks(learnt_in = 6, overwrite);
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var x;
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periods 6:7;
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multiply 0.8;
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end;
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endval(learnt_in = 6);
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x *= 0.75;
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end;
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perfect_foresight_with_expectation_errors_setup(periods = 7);
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// First simulation with default options
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perfect_foresight_with_expectation_errors_solver;
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pfwee1 = oo_.endo_simul;
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// Second simulation with alternative guess values
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perfect_foresight_with_expectation_errors_solver(terminal_steady_state_as_guess_value);
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pfwee2 = oo_.endo_simul;
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// Now compute the solution by hand to verify the results
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perfect_foresight_setup;
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// Information arriving in period 1 (temp shock now and tomorrow)
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oo_.exo_simul(2:3,1) = 1.2;
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perfect_foresight_solver;
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// Information arriving in period 2 (temp shock now + permanent shock in future)
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oo_.exo_simul(3,1) = 1.3;
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oo_.exo_steady_state = 1.1;
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oo_.exo_simul(end, 1) = oo_.exo_steady_state;
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oo_.steady_state = evaluate_steady_state(oo_.steady_state, M_, options_, oo_, true);
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oo_.endo_simul(:, end) = oo_.steady_state;
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periods 6;
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saved_endo = oo_.endo_simul(:, 1);
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saved_exo = oo_.exo_simul(1, :);
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oo_.endo_simul = oo_.endo_simul(:, 2:end);
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oo_.exo_simul = oo_.exo_simul(2:end, :);
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perfect_foresight_solver;
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oo_.endo_simul = [ saved_endo oo_.endo_simul ];
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oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
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// Information arriving in period 3 (temp shocks + permanent shock in future)
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oo_.exo_simul(4,1) = 1.4;
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oo_.exo_simul(8,1) = 1.5;
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oo_.exo_steady_state = 1.1+0.1;
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oo_.exo_simul(end, 1) = oo_.exo_steady_state;
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oo_.steady_state = evaluate_steady_state(oo_.steady_state, M_, options_, oo_, true);
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oo_.endo_simul(:, end) = oo_.steady_state;
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periods 5;
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saved_endo = oo_.endo_simul(:, 1:2);
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saved_exo = oo_.exo_simul(1:2, :);
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oo_.endo_simul = oo_.endo_simul(:, 3:end);
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oo_.exo_simul = oo_.exo_simul(3:end, :);
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perfect_foresight_solver;
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oo_.endo_simul = [ saved_endo oo_.endo_simul ];
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oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
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// Information arriving in period 6 (temp shocks + permanent shock)
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oo_.exo_simul(7,1) = 1*0.8;
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oo_.exo_simul(8,1) = 1.5*0.8;
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oo_.exo_steady_state = (1.1+0.1)*0.75;
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oo_.exo_simul(end, 1) = oo_.exo_steady_state;
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oo_.steady_state = evaluate_steady_state(oo_.steady_state, M_, options_, oo_, true);
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oo_.endo_simul(:, end) = oo_.steady_state;
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periods 2;
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saved_endo = oo_.endo_simul(:, 1:5);
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saved_exo = oo_.exo_simul(1:5, :);
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oo_.endo_simul = oo_.endo_simul(:, 6:end);
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oo_.exo_simul = oo_.exo_simul(6:end, :);
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perfect_foresight_solver;
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oo_.endo_simul = [ saved_endo oo_.endo_simul ];
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oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
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% We should have strict equality with first pfwee simulation, because algorithm
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% and guess values are exactly the same.
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if any(any(pfwee1-oo_.endo_simul ~= 0))
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error('Error in perfect_foresight_with_expectation_errors')
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end
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% For the 2nd simulation, since the guess values are different, there are some
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% numerical differences
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if max(max(abs(pfwee2-oo_.endo_simul))) > 10*options_.simul.tolf
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error('Error in perfect_foresight_with_expectation_errors + terminal_steady_state_as_guess_value')
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end
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