196 lines
7.5 KiB
Modula-2
196 lines
7.5 KiB
Modula-2
// See fs2000.mod in the examples/ directory for details on the model
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var m P c e W R k d n l gy_obs gp_obs y dA;
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varexo e_a e_m e_b;
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parameters alp bet gam mst rho psi del;
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alp = 0.33;
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bet = 0.99;
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gam = 0.003;
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mst = 1.011;
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rho = 0.7;
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psi = 0.787;
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del = 0.02;
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model;
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dA = exp(gam+e_a);
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log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
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-P/(c(+1)*P(+1)*m)+bet*exp(e_b)*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
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W = l/n;
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-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
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R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
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1/(c*P)-bet*exp(e_b)*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
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c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
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P*c = m;
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m-1+d = l;
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e = exp(e_a);
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y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
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gy_obs = dA*y/y(-1);
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gp_obs = (P/P(-1))*m(-1)/dA;
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end;
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steady_state_model;
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dA = exp(gam);
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gst = 1/dA;
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m = mst;
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khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
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xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
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nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
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n = xist/(nust+xist);
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P = xist + nust;
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k = khst*n;
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l = psi*mst*n/( (1-psi)*(1-n) );
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c = mst/P;
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d = l - mst + 1;
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y = k^alp*n^(1-alp)*gst^alp;
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R = mst/bet;
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W = l/n;
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ist = y-c;
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q = 1 - d;
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e = 1;
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gp_obs = m/dA;
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gy_obs = dA;
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end;
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write_latex_dynamic_model;
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shocks;
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var e_a; stderr 0.014;
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var e_b; stderr 0.1;
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var e_m; stderr 0.005;
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end;
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steady;
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check;
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varobs gp_obs gy_obs;
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calib_smoother(datafile=fsdat_simul, filtered_vars, filter_step_ahead = [3:4],filter_covariance,smoothed_state_uncertainty) m P c e W R k d n l y dA;
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oo0=oo_;
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calib_smoother(datafile=fsdat_simul, filtered_vars, filter_step_ahead = [3:4],filter_covariance,smoothed_state_uncertainty,smoother_redux) m P c e W R k d n l y dA;
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oo1=oo_;
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calib_smoother(datafile=fsdat_simul, filtered_vars, filter_step_ahead = [3:4],filter_covariance,smoothed_state_uncertainty,kalman_algo=2,smoother_redux) m P c e W R k d n l y dA;
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oo2=oo_;
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for k=1:M_.exo_nbr
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mserr(k)=max(abs(oo0.SmoothedShocks.(M_.exo_names{k})-oo1.SmoothedShocks.(M_.exo_names{k})));
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end
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if max(mserr)>1.e-12
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error('smoother_redux with kalman_algo=1 does not replicate original smoother for shocks!')
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end
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vlist = M_.endo_names(oo_.dr.order_var(oo_.dr.restrict_var_list));
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for k=1:length(vlist)
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merr(k)=max(abs(oo0.SmoothedVariables.(vlist{k})-oo1.SmoothedVariables.(vlist{k})));
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merrU(k)=max(abs(oo0.UpdatedVariables.(vlist{k})-oo1.UpdatedVariables.(vlist{k})));
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merrF(k)=max(abs(oo0.FilteredVariables.(vlist{k})-oo1.FilteredVariables.(vlist{k})));
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end
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if max(merr)>1.e-12
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error('smoother_redux with kalman_algo=1 does not replicate original smoothed restricted var list!')
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end
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if max(merrU)>1.e-12
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error('smoother_redux with kalman_algo=1 does not replicate original updated restricted var list!')
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end
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if max(merrF)>1.e-12
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error('smoother_redux with kalman_algo=1 does not replicate original filtered restricted var list!')
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end
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vlist1 = M_.endo_names(~ismember(M_.endo_names,vlist));
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for k=1:length(vlist1)
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merr1(k)=max(abs(oo0.SmoothedVariables.(vlist1{k})-oo1.SmoothedVariables.(vlist1{k})));
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merr1U(k)=max(abs(oo0.UpdatedVariables.(vlist1{k})-oo1.UpdatedVariables.(vlist1{k})));
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merr1F(k)=max(abs(oo0.FilteredVariables.(vlist1{k})-oo1.FilteredVariables.(vlist1{k})));
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end
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if max(merr1)>1.e-12
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error('smoother_redux with kalman_algo=1 does not replicate original smoothed static variables!')
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end
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if max(merr1U)>1.e-12
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error('smoother_redux with kalman_algo=1 does not replicate original updated static variables!')
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end
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if max(merr1F)>1.e-12
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error('smoother_redux with kalman_algo=1 does not replicate original filtered static variables!')
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end
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merrK = max(max(max(abs(oo0.FilteredVariablesKStepAhead-oo1.FilteredVariablesKStepAhead))));
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if max(merrK)>1.e-12
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error('smoother_redux with kalman_algo=1 does not replicate original k-step ahead forecasts!')
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end
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verrK = max(max(max(max(abs(oo0.FilteredVariablesKStepAheadVariances(:,[1:14 16],[1:14 16],:)-oo1.FilteredVariablesKStepAheadVariances(:,[1:14 16],[1:14 16],:))))));
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if verrK>1.e-12
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error('smoother_redux with kalman_algo=1 does not replicate original k-step ahead forecast variances!')
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end
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verr=max(max(max(abs(oo0.Smoother.Variance([1:14 16],[1:14 16],:)-oo1.Smoother.Variance([1:14 16],[1:14 16],:)))));
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if verr>1.e-12
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error('smoother_redux with kalman_algo=1 does not replicate original filter covariance!')
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end
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verrS=max(max(max(abs(oo0.Smoother.State_uncertainty([1:14 16],[1:14 16],:)-oo1.Smoother.State_uncertainty([1:14 16],[1:14 16],:)))));
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if verrS>1.e-12
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error('smoother_redux with kalman_algo=1 does not replicate original state covariance!')
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end
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// now I check kalman_algo=2
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for k=1:M_.exo_nbr
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mserr(k)=max(abs(oo0.SmoothedShocks.(M_.exo_names{k})-oo2.SmoothedShocks.(M_.exo_names{k})));
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end
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if max(mserr)>1.e-12
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error('smoother_redux with kalman_algo=2 does not replicate original smoother for shocks!')
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end
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vlist = M_.endo_names(oo_.dr.order_var(oo_.dr.restrict_var_list));
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for k=1:length(vlist)
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merr(k)=max(abs(oo0.SmoothedVariables.(vlist{k})-oo2.SmoothedVariables.(vlist{k})));
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merrU(k)=max(abs(oo0.UpdatedVariables.(vlist{k})-oo2.UpdatedVariables.(vlist{k})));
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merrF(k)=max(abs(oo0.FilteredVariables.(vlist{k})-oo2.FilteredVariables.(vlist{k})));
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end
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if max(merr)>1.e-12
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error('smoother_redux with kalman_algo=2 does not replicate original smoothed restricted var list!')
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end
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if max(merrU)>1.e-12
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error('smoother_redux with kalman_algo=2 does not replicate original updated restricted var list!')
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end
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if max(merrF)>1.e-12
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error('smoother_redux with kalman_algo=2 does not replicate original filtered restricted var list!')
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end
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vlist1 = M_.endo_names(~ismember(M_.endo_names,vlist));
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for k=1:length(vlist1)
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merr1(k)=max(abs(oo0.SmoothedVariables.(vlist1{k})-oo2.SmoothedVariables.(vlist1{k})));
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merr1U(k)=max(abs(oo0.UpdatedVariables.(vlist1{k})-oo2.UpdatedVariables.(vlist1{k})));
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merr1F(k)=max(abs(oo0.FilteredVariables.(vlist1{k})-oo2.FilteredVariables.(vlist1{k})));
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end
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if max(merr1)>1.e-12
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error('smoother_redux with kalman_algo=2 does not replicate original smoothed static variables!')
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end
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if max(merr1U)>1.e-12
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error('smoother_redux with kalman_algo=2 does not replicate original updated static variables!')
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end
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if max(merr1F)>1.e-12
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error('smoother_redux with kalman_algo=2 does not replicate original filtered static variables!')
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end
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merrK = max(max(max(abs(oo0.FilteredVariablesKStepAhead-oo2.FilteredVariablesKStepAhead))));
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if max(merrK)>1.e-12
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error('smoother_redux with kalman_algo=2 does not replicate original k-step ahead forecasts!')
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end
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verrK = max(max(max(max(abs(oo0.FilteredVariablesKStepAheadVariances(:,[1:14 16],[1:14 16],:)-oo2.FilteredVariablesKStepAheadVariances(:,[1:14 16],[1:14 16],:))))));
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if verrK>1.e-12
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error('smoother_redux with kalman_algo=2 does not replicate original k-step ahead forecast variances!')
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end
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verr=max(max(max(abs(oo0.Smoother.Variance([1:14 16],[1:14 16],:)-oo2.Smoother.Variance([1:14 16],[1:14 16],:)))));
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if verr>1.e-12
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error('smoother_redux with kalman_algo=2 does not replicate original filter covariance!')
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end
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verrS=max(max(max(abs(oo0.Smoother.State_uncertainty([1:14 16],[1:14 16],:)-oo2.Smoother.State_uncertainty([1:14 16],[1:14 16],:)))));
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if verrS>1.e-12
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error('smoother_redux with kalman_algo=2 does not replicate original state covariance!')
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end
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