136 lines
4.3 KiB
Modula-2
136 lines
4.3 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;
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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*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*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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shocks;
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var e_a; stderr 0.014;
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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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estimated_params;
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alp, beta_pdf, 0.356, 0.02;
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bet, beta_pdf, 0.993, 0.002;
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gam, normal_pdf, 0.0085, 0.003;
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mst, normal_pdf, 1.0002, 0.007,1.0002-3*0.007,1.0002+3*0.007;
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rho, beta_pdf, 0.129, 0.05;
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psi, beta_pdf, 0.65, 0.05;
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del, beta_pdf, 0.01, 0.005;
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stderr e_a, inv_gamma_pdf, 0.035449, inf;
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stderr e_m, inv_gamma_pdf, 0.008862, inf;
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end;
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varobs gp_obs gy_obs;
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options_.solve_tolf = 1e-12;
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estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=3000,mh_nblocks=1,mh_jscale=0.8,moments_varendo,selected_variables_only,contemporaneous_correlation,smoother,forecast=8,
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geweke_interval = [0.19 0.49],
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taper_steps = [4 7 15],
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raftery_lewis_diagnostics,
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raftery_lewis_qrs=[0.025 0.01 0.95],
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bayesian_irf,posterior_nograph,
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additional_optimizer_steps=[4]
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) y m;
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if ~isequal(options_.convergence.geweke.taper_steps,[4 7 15]') || ~isequal(options_.convergence.geweke.geweke_interval,[0.19 0.49])
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error('Interface for Geweke diagnostics not working')
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end
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if ~isequal(options_.convergence.rafterylewis.qrs,[0.025 0.01 0.95]) || ~isequal(options_.convergence.rafterylewis.indicator,1)
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error('Interface for Raftery/Lewis diagnostics not working')
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end
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%test load_mh_file option
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options_.bayesian_irf=0;
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options_.smoother=0;
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options_.moments_varendo=0;
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options_.forecast=0;
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copyfile([M_.dname filesep 'metropolis' filesep M_.dname '_mh1_blck1.mat'],[M_.dname '_mh1_blck1.mat'])
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estimation(mode_compute=0,mode_file='fs2000/Output/fs2000_mode',order=1, datafile=fsdat_simul, nobs=192, loglinear, mh_replic=1500, mh_nblocks=1, mh_jscale=0.8);
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hh=eye(size(bayestopt_.name,1));
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save('fs2000/Output/fs2000_mode.mat','hh','-append')
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Laplace = oo_.MarginalDensity.LaplaceApproximation; %save Laplace approximation which will be overwritten with set hh otherwise
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estimation(mode_compute=0,mode_file='fs2000/Output/fs2000_mode',order=1, datafile=fsdat_simul, nobs=192, loglinear, mh_replic=1500, mh_nblocks=1, mh_jscale=10,load_mh_file);
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temp1=load([M_.dname '_mh1_blck1.mat']);
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temp2=load([M_.dname filesep 'metropolis' filesep M_.dname '_mh1_blck1.mat']);
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if ~isoctave
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if max(max(abs(temp1.x2-temp2.x2)))>1e-10
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error('Adding draws did not result in the same chain')
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end
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end
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if ~exist([M_.dname filesep 'Output'],'dir')
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mkdir(M_.dname,'Output');
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end
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save([M_.dname filesep 'Output' filesep 'fs2000_results.mat'], 'oo_');
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options_.load_results_after_load_mh=1;
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estimation(mode_compute=0,mode_file='fs2000/Output/fs2000_mode',order=1, datafile=fsdat_simul, nobs=192, loglinear, mh_replic=0, mh_nblocks=1, mh_jscale=10,load_mh_file,smoother) gy_obs gp_obs;
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oo_.MarginalDensity.LaplaceApproximation = Laplace; %reset correct Laplace
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%test prior sampling
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options_.prior_draws=100;
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options_.no_graph.posterior=0;
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prior_posterior_statistics('prior',dataset_,dataset_info); %get smoothed and filtered objects and forecasts
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