60 lines
1.4 KiB
Modula-2
60 lines
1.4 KiB
Modula-2
//conducts posterior sampling with morris=1 and morris=2 and rmse
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var y l w pi r A dy pic;
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varexo eps_A eps_P;
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parameters PSI ETA BETA ALPHA KAPPA PI RHO;
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PSI = 1;
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ETA = 2;
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BETA = 0.95;
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KAPPA = 0.5;
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ALPHA = 0.99;
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PI = 1.005;
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RHO = 0.9;
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model;
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y = exp(A)*l; //production function
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w/y = PSI * l^ETA;//labor supply
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BETA*1/y(+1)*r/pi(+1)=1/y;//saving
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log(pi) = ALPHA*log(pi(-1))+(1-ALPHA) * log(pi(+1)) + KAPPA*log(w/exp(A))+eps_P;//inflation
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log(r*BETA/PI) = 1.5*log(pi/PI);
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A = RHO*A(-1)+eps_A;
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//observable variables
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dy = log(y)-log(y(-1));
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pic= log(pi)-log(PI);
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end;
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steady_state_model;
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A = eps_A;
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w = exp(A);
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pi = PI;
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l = (1/PSI)^(1/(1+ETA));
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y = exp(A)*l;
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r = PI/BETA;
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end;
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shocks;
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var eps_A;
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stderr 0.01;
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var eps_P;
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stderr 0.01;
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end;
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steady;
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check;
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varobs dy pic;
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estimated_params;
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ETA,3.7,0.00000001,10,GAMMA_PDF,5,1;
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KAPPA,0.3,0.00000001,0.99999999999,beta_PDF,0.5,0.1;
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stderr eps_A,0.02,0.000000000001,100,INV_GAMMA2_PDF,0.2,inf;
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stderr eps_P,0.03,0.000000000001,100,INV_GAMMA2_PDF,0.2,inf;
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end;
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estimation(order=1,prior_trunc=0,plot_priors =0,silent_optimizer, datafile=nk_est_data,conf_sig =.95,smoother,moments_varendo,filtered_vars,mode_check,mode_compute=4,mh_replic=5000,mh_jscale=1.5,mh_nblocks=1,bayesian_irf,tex) y pi l dy pic;
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sensitivity (datafile=nk_est_data,rmse=0, nsam = 500, lik_only = 0, morris = 2,var_rmse=(dy pic)) ;
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sensitivity (datafile=nk_est_data,rmse=0, nsam = 500, lik_only = 0, morris = 1,var_rmse=(dy pic)) ;
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