Test files for AIM that passed comparison of mjdgges and AIM dr.ghx and dr.ghu being equal down to near 0 differences on e-15
git-svn-id: https://www.dynare.org/svn/dynare/trunk@2519 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
george
parent
b953c5cf31
commit
e1c13c256a
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// This file replicates the estimation of the CIA model from
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// Frank Schorfheide (2000) "Loss function-based evaluation of DSGE models"
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// Journal of Applied Econometrics, 15, 645-670.
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// the data are the ones provided on Schorfheide's web site with the programs.
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// http://www.econ.upenn.edu/~schorf/programs/dsgesel.ZIP
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// You need to have fsdat.m in the same directory as this file.
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// This file replicates:
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// -the posterior mode as computed by Frank's Gauss programs
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// -the parameter mean posterior estimates reported in the paper
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// -the model probability (harmonic mean) reported in the paper
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// This file was tested with dyn_mat_test_0218.zip
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// the smooth shocks are probably stil buggy
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//
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// The equations are taken from J. Nason and T. Cogley (1994)
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// "Testing the implications of long-run neutrality for monetary business
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// cycle models" Journal of Applied Econometrics, 9, S37-S70.
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// Note that there is an initial minus sign missing in equation (A1), p. S63.
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//
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// Michel Juillard, February 2004
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options_.usePartInfo=0;
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var m P c e W R k d n l gy_obs gp_obs Y_obs P_obs y dA P2 c2;
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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)))))/(c2(+1)*P2(+1)*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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Y_obs/Y_obs(-1) = gy_obs;
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P_obs/P_obs(-1) = gp_obs;
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P2 = P(+1);
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c2 = c(+1);
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end;
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initval;
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k = 6;
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m = mst;
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P = 2.25;
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c = 0.45;
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e = 1;
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W = 4;
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R = 1.02;
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d = 0.85;
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n = 0.19;
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l = 0.86;
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y = 0.6;
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gy_obs = exp(gam);
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gp_obs = exp(-gam);
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dA = exp(gam);
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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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unit_root_vars P_obs Y_obs;
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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;
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rho, beta_pdf, 0.129, 0.223;
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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 P_obs Y_obs;
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observation_trends;
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P_obs (log(mst)-gam);
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Y_obs (gam);
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end;
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options_.useAIM = 1;
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estimation(datafile=fsdat,nobs=192,loglinear,mh_replic=2000,
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mode_compute=4,mh_nblocks=2,mh_drop=0.45,mh_jscale=0.65);
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// This file replicates the estimation of the CIA model from
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// Frank Schorfheide (2000) "Loss function-based evaluation of DSGE models"
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// Journal of Applied Econometrics, 15, 645-670.
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// the data are the ones provided on Schorfheide's web site with the programs.
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// http://www.econ.upenn.edu/~schorf/programs/dsgesel.ZIP
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// You need to have fsdat.m in the same directory as this file.
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// This file replicates:
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// -the posterior mode as computed by Frank's Gauss programs
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// -the parameter mean posterior estimates reported in the paper
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// -the model probability (harmonic mean) reported in the paper
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// This file was tested with dyn_mat_test_0218.zip
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// the smooth shocks are probably stil buggy
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//
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// The equations are taken from J. Nason and T. Cogley (1994)
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// "Testing the implications of long-run neutrality for monetary business
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// cycle models" Journal of Applied Econometrics, 9, S37-S70.
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// Note that there is an initial minus sign missing in equation (A1), p. S63.
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//
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// Michel Juillard, February 2004
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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(0.5*m(-1)+0.25*m(-2)+0.13*m(-3)+0.06*m(-4)+0.03*m(-5)+0.015*m(-6)+0.007*m(-7)+0.004*m(-8)+0.003*m(-9)+0.001*m(-10))+e_m;
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-P/(((1.3*c(+1)+c(+5)+0.7*c(+9))*(1.3*P(+1)+P(+5)+0.7*P(+9)))*m/9)+bet*((1.3*P(+1)+P(+5)+0.7*P(+9))/3)*(alp*exp(-alp*(gam+log((1.3*e(+1)+e(+5)+0.7*e(+9))/3)))*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* (1.3*c(+1)+c(+5)+0.7*c(+9))*(1.3*P(+1)+P(+5)+0.7*P(+9))/9) = 0;
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c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a)*4)*k(-4);
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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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initval;
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k = 6;
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m = mst;
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P = 2.25;
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c = 0.45;
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e = 1;
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W = 4;
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R = 1.02;
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d = 0.85;
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n = 0.19;
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l = 0.86;
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y = 0.6;
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gy_obs = exp(gam);
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gp_obs = exp(-gam);
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dA = exp(gam);
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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;
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rho, beta_pdf, 0.129, 0.223;
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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_.useAIM = 1;
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//stoch_simul m P c e W R k l y
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estimation(datafile=fsdat,nobs=192,loglinear,mh_replic=2000,mh_nblocks=5,mh_jscale=0.8);
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@ -0,0 +1,96 @@
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// This file replicates the estimation of the CIA model from
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// Frank Schorfheide (2000) "Loss function-based evaluation of DSGE models"
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// Journal of Applied Econometrics, 15, 645-670.
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// the data are the ones provided on Schorfheide's web site with the programs.
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// http://www.econ.upenn.edu/~schorf/programs/dsgesel.ZIP
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// You need to have fsdat.m in the same directory as this file.
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// This file replicates:
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// -the posterior mode as computed by Frank's Gauss programs
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// -the parameter mean posterior estimates reported in the paper
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// -the model probability (harmonic mean) reported in the paper
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// This file was tested with dyn_mat_test_0218.zip
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// the smooth shocks are probably stil buggy
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//
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// The equations are taken from J. Nason and T. Cogley (1994)
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// "Testing the implications of long-run neutrality for monetary business
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// cycle models" Journal of Applied Econometrics, 9, S37-S70.
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// Note that there is an initial minus sign missing in equation (A1), p. S63.
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//
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// Michel Juillard, February 2004
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//Model with up to 10 lags and leads up to 9 - but not all, with some missing
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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); % missing 0.06*m(-4) and +0.25*m(-2)
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log(m) = (1-rho)*log(mst) + rho*log(0.75*m(-1)+0.13*m(-3)+0.09*m(-5)+0.015*m(-6)+0.007*m(-7)+0.004*m(-8)+0.003*m(-9)+0.001*m(-10))+e_m;
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-P/(((1.3*c(+1)+c(+5)+0.7*c(+9))*(1.3*P(+1)+P(+5)+0.7*P(+9)))*m/9)+bet*((1.3*P(+1)+P(+5)+0.7*P(+9))/3)*(alp*exp(-alp*(gam+log((1.3*e(+1)+e(+5)+0.7*e(+9))/3)))*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* (1.3*c(+1)+c(+5)+0.7*c(+9))*(1.3*P(+1)+P(+5)+0.7*P(+9))/9) = 0;
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c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a)*4)*k(-4);
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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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initval;
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k = 6;
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m = mst;
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P = 2.25;
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c = 0.45;
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e = 1;
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W = 4;
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R = 1.02;
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d = 0.85;
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n = 0.19;
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l = 0.86;
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y = 0.6;
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gy_obs = exp(gam);
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gp_obs = exp(-gam);
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dA = exp(gam);
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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;
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rho, beta_pdf, 0.129, 0.223;
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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_.useAIM = 0;
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//stoch_simul m P c e W R k l y
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estimation(datafile=fsdat,nobs=192,loglinear,mh_replic=2000,mh_nblocks=5,mh_jscale=0.8);
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@ -0,0 +1,70 @@
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var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
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varexo e_R e_q e_ys e_pies e_A;
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parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
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psi1 = 1.54;
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psi2 = 0.25;
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psi3 = 0.25;
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rho_R = 0.5;
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alpha = 0.3;
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rr = 2.51;
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k = 0.5;
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tau = 0.5;
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rho_q = 0.4;
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rho_A = 0.2;
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rho_ys = 0.9;
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rho_pies = 0.7;
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// GP extended to see effect of 2 lags and 2 leads
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model(linear);
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//y = y(+1) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
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y = 0.3*y +0.3*y(-1) +0.3*y(-2)-(tau +alpha*(2-alpha)*(1-tau))*(R-pie)-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq + alpha*(2-alpha)*((1-tau)/tau)*(y_s(11)-y_s)-A;
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//y = 0.3*y(+2)+0.3*y(+1)+0.3*y(-2) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
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//pie = exp(-rr/400)*pie(+1)+alpha*exp(-rr/400)*dq(+1)-alpha*dq+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
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pie = exp(-rr/400)*pie(-1)+alpha*exp(-rr/400)*dq-alpha*dq(-1)+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
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pie = de+(1-alpha)*dq+pie_s;
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R = rho_R*R(-1)+(1-rho_R)*(psi1*pie+psi2*(y+alpha*(2-alpha)*((1-tau)/tau)*y_s)+psi3*de)+e_R;
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dq = rho_q*dq(-1)+e_q;
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y_s = rho_ys*y_s(-1)+e_ys;
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//pie_s = rho_pies*pie_s(-1)+e_pies;
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pie_s = rho_pies*pie_s(-1)+(1-rho_pies)*pie_s(-2)+e_pies;
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A = rho_A*A(-1)+e_A;
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y_obs = y-y(-1)+A;
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pie_obs = 4*pie;
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R_obs = 4*R;
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end;
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shocks;
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var e_R = 1.25^2;
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var e_q = 2.5^2;
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var e_A = 1.89;
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var e_ys = 1.89;
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var e_pies = 1.89;
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end;
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varobs y_obs R_obs pie_obs dq de;
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check;
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estimated_params;
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psi1 , gamma_pdf,1.5,0.5;
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psi2 , gamma_pdf,0.25,0.125;
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psi3 , gamma_pdf,0.25,0.125;
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rho_R ,beta_pdf,0.5,0.2;
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alpha ,beta_pdf,0.3,0.1;
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rr ,gamma_pdf,2.5,1;
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k , gamma_pdf,0.5,0.25;
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tau ,gamma_pdf,0.5,0.2;
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rho_q ,beta_pdf,0.4,0.2;
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rho_A ,beta_pdf,0.5,0.2;
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rho_ys ,beta_pdf,0.8,0.1;
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rho_pies,beta_pdf,0.7,0.15;
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stderr e_R,inv_gamma_pdf,1.2533,0.6551;
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stderr e_q,inv_gamma_pdf,2.5066,1.3103;
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stderr e_A,inv_gamma_pdf,1.2533,0.6551;
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stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
|
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stderr e_pies,inv_gamma_pdf,1.88,0.9827;
|
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end;
|
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|
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options_.useAIM = 1;
|
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estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=10,prefilter=1,mh_jscale=0.5,mh_replic=0);
|
||||
|
||||
|
|
@ -0,0 +1,68 @@
|
|||
var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
|
||||
varexo e_R e_q e_ys e_pies e_A;
|
||||
|
||||
parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
|
||||
|
||||
psi1 = 1.54;
|
||||
psi2 = 0.25;
|
||||
psi3 = 0.25;
|
||||
rho_R = 0.5;
|
||||
alpha = 0.3;
|
||||
rr = 2.51;
|
||||
k = 0.5;
|
||||
tau = 0.5;
|
||||
rho_q = 0.4;
|
||||
rho_A = 0.2;
|
||||
rho_ys = 0.9;
|
||||
rho_pies = 0.7;
|
||||
|
||||
// GP extended to see effect of 2 lags and 2 leads
|
||||
model(linear);
|
||||
//y = y(+1) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
|
||||
y = 0.3*y(+2)+0.3*y(+1)+0.3*y(-2) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
|
||||
pie = exp(-rr/400)*pie(+1)+alpha*exp(-rr/400)*dq(+1)-alpha*dq+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
|
||||
pie = de+(1-alpha)*dq+pie_s;
|
||||
R = rho_R*R(-1)+(1-rho_R)*(psi1*pie+psi2*(y+alpha*(2-alpha)*((1-tau)/tau)*y_s)+psi3*de)+e_R;
|
||||
dq = rho_q*dq(-1)+e_q;
|
||||
y_s = rho_ys*y_s(-1)+e_ys;
|
||||
//pie_s = rho_pies*pie_s(-1)+e_pies;
|
||||
pie_s = rho_pies*pie_s(-1)+(1-rho_pies)*pie_s(-2)+e_pies;
|
||||
A = rho_A*A(-1)+e_A;
|
||||
y_obs = y-y(-1)+A;
|
||||
pie_obs = 4*pie;
|
||||
R_obs = 4*R;
|
||||
end;
|
||||
|
||||
shocks;
|
||||
var e_R = 1.25^2;
|
||||
var e_q = 2.5^2;
|
||||
var e_A = 1.89;
|
||||
var e_ys = 1.89;
|
||||
var e_pies = 1.89;
|
||||
end;
|
||||
|
||||
varobs y_obs R_obs pie_obs dq de;
|
||||
|
||||
estimated_params;
|
||||
psi1 , gamma_pdf,1.5,0.5;
|
||||
psi2 , gamma_pdf,0.25,0.125;
|
||||
psi3 , gamma_pdf,0.25,0.125;
|
||||
rho_R ,beta_pdf,0.5,0.2;
|
||||
alpha ,beta_pdf,0.3,0.1;
|
||||
rr ,gamma_pdf,2.5,1;
|
||||
k , gamma_pdf,0.5,0.25;
|
||||
tau ,gamma_pdf,0.5,0.2;
|
||||
rho_q ,beta_pdf,0.4,0.2;
|
||||
rho_A ,beta_pdf,0.5,0.2;
|
||||
rho_ys ,beta_pdf,0.8,0.1;
|
||||
rho_pies,beta_pdf,0.7,0.15;
|
||||
stderr e_R,inv_gamma_pdf,1.2533,0.6551;
|
||||
stderr e_q,inv_gamma_pdf,2.5066,1.3103;
|
||||
stderr e_A,inv_gamma_pdf,1.2533,0.6551;
|
||||
stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
|
||||
stderr e_pies,inv_gamma_pdf,1.88,0.9827;
|
||||
end;
|
||||
|
||||
options_.useAIM = 1;
|
||||
estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=10,prefilter=1,mh_jscale=0.5,mh_replic=0);
|
||||
|
||||
Loading…
Reference in New Issue