diff --git a/tests/practicing/AssetPricingApproximation.mod b/tests/practicing/AssetPricingApproximation.mod deleted file mode 100644 index 57679f38c..000000000 --- a/tests/practicing/AssetPricingApproximation.mod +++ /dev/null @@ -1,47 +0,0 @@ -periods 500; -var dc, dd, v_c, v_d, x; -varexo e_c, e_x, e_d; - -parameters DELTA THETA PSI MU_C MU_D RHO_X LAMBDA_DX; - -DELTA=.99; -PSI=1.5; -THETA=(1-7.5)/(1-1/PSI); -MU_C=0.0015; -MU_D=0.0015; -RHO_X=.979; -LAMBDA_DX=3; - -model; -v_c = DELTA^THETA * exp((-THETA/PSI)*dc(+1) + (THETA-1)*log((1+v_c(+1))*exp(dc(+1))/v_c) ) * (1+v_c(+1))*exp(dc(+1)); -v_d = DELTA^THETA * exp((-THETA/PSI)*dc(+1) + (THETA-1)*log((1+v_c(+1))*exp(dc(+1))/v_c) ) * (1+v_d(+1))*exp(dd(+1)); -dc = MU_C + x(-1) + e_c; -dd = MU_D + LAMBDA_DX*x(-1) + e_d; -x = RHO_X * x(-1) + e_x; -end; - -initval; -v_c=15; -v_d=15; -dc=MU_C; -dd=MU_D; -x=0; -e_c=0; -e_x=0; -e_d=0; -end; - -shocks; -var e_c; -stderr .0078; -var e_x; -stderr .0078*.044; -var e_d; -stderr .0078*4.5; -end; - -steady(solve_algo=0); -check; - -stoch_simul(dr_algo=1, order=1, periods=1000, irf=30); -datasaver('simudata',[]); \ No newline at end of file diff --git a/tests/practicing/AssetPricingEstimate.mod b/tests/practicing/AssetPricingEstimate.mod deleted file mode 100644 index 482b54457..000000000 --- a/tests/practicing/AssetPricingEstimate.mod +++ /dev/null @@ -1,58 +0,0 @@ -var dc, dd, v_c, v_d, x; -varexo e_c, e_x, e_d; - -parameters DELTA THETA PSI MU_C MU_D RHO_X LAMBDA_DX; - -DELTA=.99; -PSI=1.5; -THETA=(1-7.5)/(1-1/PSI); -MU_C=0.0015; -MU_D=0.0015; -RHO_X=.979; -LAMBDA_DX=3; - - -model; -v_c = DELTA^THETA * exp((-THETA/PSI)*dc(+1) + (THETA-1)*log((1+v_c(+1))*exp(dc(+1))/v_c) ) * (1+v_c(+1))*exp(dc(+1)); -v_d = DELTA^THETA * exp((-THETA/PSI)*dc(+1) + (THETA-1)*log((1+v_c(+1))*exp(dc(+1))/v_c) ) * (1+v_d(+1))*exp(dd(+1)); -dc = MU_C + x(-1) + e_c; -dd = MU_D + LAMBDA_DX*x(-1) + e_d; -x = RHO_X * x(-1) + e_x; -end; - -initval; -v_c=15; -v_d=15; -dc=MU_C; -dd=MU_D; -x=0; -e_c=0; -e_x=0; -e_d=0; -end; - -shocks; -var e_d; stderr .001; -var e_c; stderr .001; -var e_x; stderr .001; -end; - -steady; - -estimated_params; -DELTA, beta_pdf, 0.98,.005; -THETA,normal_pdf,-19.5, 0.0025; -PSI,normal_pdf,1.6, 0.1; -MU_C,normal_pdf,0.001, 0.001; -MU_D,normal_pdf,0.001, 0.001; -RHO_X,normal_pdf,.98, 0.005; -LAMBDA_DX,normal_pdf,3, 0.05; -stderr e_d,inv_gamma_pdf,.0025, 30; -stderr e_x,inv_gamma_pdf,.0003, 30; -stderr e_c,inv_gamma_pdf,.01, 30; -end; - - -varobs v_d dd dc; - -estimation(datafile=simudata,mh_replic=1000,mh_jscale=.4,nodiagnostic); \ No newline at end of file diff --git a/tests/practicing/BansalYaronBayes.mod b/tests/practicing/BansalYaronBayes.mod deleted file mode 100644 index 4d8530610..000000000 --- a/tests/practicing/BansalYaronBayes.mod +++ /dev/null @@ -1,42 +0,0 @@ -var x y; -varexo e_x e_u; - -parameters rho sig_x sig_u mu_y; - -rho = .98; -mu_y=.015; -sig_x=0.00025; -sig_u=.0078; - -model(linear); -x=rho*x(-1) + sig_x*e_x; -y=mu_y + x(-1) + sig_u*e_u; -end; - -initval; -x=0; -y=mu_y; -end; - -steady; - -shocks; -var e_x; -stderr 1; -var e_u; -stderr 1; -end; - -estimated_params; - -rho, beta_pdf, .98, .01; -mu_y, uniform_pdf, .005, .0025; -sig_u, inv_gamma_pdf, .003, inf; -sig_x, inv_gamma_pdf, .003, inf; -// The syntax for to input the priors is the following: -// variable name, prior distribution, parameters of distribution. - -end; - -varobs y; -estimation(datafile=data_consRicardoypg,first_obs=1,nobs=227,mh_replic=5000,mh_nblocks=1,mh_jscale=1); \ No newline at end of file diff --git a/tests/practicing/BansalYaronML.mod b/tests/practicing/BansalYaronML.mod deleted file mode 100644 index 331e6b9be..000000000 --- a/tests/practicing/BansalYaronML.mod +++ /dev/null @@ -1,44 +0,0 @@ - -var x y; -varexo e_x e_u; - -parameters rho sig_x sig_u mu_y; - -rho = .98; -mu_y=.015; -sig_x=0.00025; -sig_u=.0078; - -model(linear); -x=rho*x(-1) + sig_x*e_x; -y=mu_y + x(-1) + sig_u*e_u; -end; - -initval; -x=0; -y=mu_y; -end; - -steady; - -shocks; -var e_x; -stderr 1; -var e_u; -stderr 1; -end; - -estimated_params; -// ML estimation setup -// parameter name, initial value, boundaries_low, ..._up; - rho, 0, -0.99, 0.999; // use this for unconstrained max likelihood -// rho, .98, .975, .999 ; // use this for long run risk model -// sig_x, .0004,.0001,.05 ; // use this for the long run risk model - sig_x, .0005, .00000000001, .01; // use this for unconstrained max likelihood -sig_u, .007,.001, .1; -mu_y, .014, .0001, .04; - -end; - -varobs y; -estimation(datafile=data_consRicardoypg,first_obs=1,nobs=227,mh_replic=0,mode_compute=4,mode_check); \ No newline at end of file diff --git a/tests/practicing/Fig1131.mod b/tests/practicing/Fig1131.mod deleted file mode 100644 index 41bf69a33..000000000 --- a/tests/practicing/Fig1131.mod +++ /dev/null @@ -1,79 +0,0 @@ -// This program replicates figure 11.3.1 from chapter 11 of RMT2 by Ljungqvist and Sargent - -var c k; -varexo taui tauc tauk g; -parameters bet gam del alpha A; -bet=.95; -gam=2; -del=.2; -alpha=.33; -A=1; - -model; -k=A*k(-1)^alpha+(1-del)*k(-1)-c-g; -c^(-gam)= bet*(c(+1)^(-gam))*((1+tauc(-1))/(1+tauc))*((1-taui)*(1-del)/(1-taui(-1))+ - ((1-tauk)/(1-taui(-1)))*alpha*A*k(-1)^(alpha-1)); -end; - -initval; -k=1.5; -c=0.6; -g = 0.2; -tauc = 0; -taui = 0; -tauk = 0; -end; -steady; - -endval; -k=1.5; -c=0.4; -g =.4; -tauc =0; -taui =0; -tauk =0; -end; -steady; - -shocks; -var g; -periods 1:9; -values 0.2; -end; - -simul(periods=100); - -co=ys0_(var_index('c')); -ko = ys0_(var_index('k')); -go = ex_(1,1); - -rbig0=1/bet; -rbig=y_(var_index('c'),2:101).^(-gam)./(bet*y_(var_index('c'),3:102).^(-gam)); -rq0=alpha*A*ko^(alpha-1); -rq=alpha*A*y_(var_index('k'),1:100).^(alpha-1); -wq0=A*ko^alpha-ko*alpha*A*ko^(alpha-1); -wq=A*y_(var_index('k'),1:100).^alpha-y_(var_index('k'),1:100).*alpha*A.*y_(var_index('k'),1:100).^(alpha-1); -sq0=(1-ex_(1,4))*A*alpha*ko^(alpha-1)+(1-del); -sq=(1-ex_(1:100,4)')*A*alpha.*y_(var_index('k'),1:100).^(alpha-1)+(1-del); - -figure -subplot(2,3,1) -plot([ko*ones(100,1) y_(var_index('k'),1:100)' ]) -title('k') -subplot(2,3,2) -plot([co*ones(100,1) y_(var_index('c'),2:101)' ]) -title('c') -subplot(2,3,3) -plot([rbig0*ones(100,1) rbig' ]) -title('R') -subplot(2,3,4) -plot([wq0*ones(100,1) wq' ]) -title('w/q') -subplot(2,3,5) -plot([sq0*ones(100,1) sq' ]) -title('s/q') -subplot(2,3,6) -plot([rq0*ones(100,1) rq' ]) -title('r/q') - -print -depsc fig1131.ps diff --git a/tests/practicing/Fig1131commented.mod b/tests/practicing/Fig1131commented.mod deleted file mode 100644 index 57311cd6e..000000000 --- a/tests/practicing/Fig1131commented.mod +++ /dev/null @@ -1,130 +0,0 @@ -// This program replicates figure 11.3.1 from chapter 11 of RMT2 by Ljungqvist and Sargent -// This is a commented version of the program given in the handout. - -// Note: y_ records the simulated endogenous variables in alphabetical order -// ys0_ records the initial steady state -// ys_ records the terminal steady state -// We check that these line up at the end points -// Note: y_ has ys0_ in first column, ys_ in last column, explaining why it is 102 long; -// The sample of size 100 is in between. - -// Warning: we align c, k, and the taxes to exploit the dynare syntax. See comments below. -// So k in the program corresponds to k_{t+1} and the same timing holds for the taxes. - -//Declares the endogenous variables; -var c k; -//declares the exogenous variables // investment tax credit, consumption tax, capital tax, government spending -varexo taui tauc tauk g; - -parameters bet gam del alpha A; - -bet=.95; // discount factor -gam=2; // CRRA parameter -del=.2; // depreciation rate -alpha=.33; // capital's share -A=1; // productivity - -// Alignment convention: -// g tauc taui tauk are now columns of ex_. Because of a bad design decision -// the date of ex_(1,:) doesn't necessarily match the date in y_. Whether they match depends -// on the number of lag periods in endogenous versus exogenous variables. -// In this example they match because tauc(-1) and taui(-1) enter the model. - -// These decisions and the timing conventions mean that -// y_(:,1) records the initial steady state, while y_(:,102) records the terminal steady state values. -// For j > 2, y_(:,j) records [c(j-1) .. k(j-1) .. G(j-1)] where k(j-1) means -// end of period capital in period j-1, which equals k(j) in chapter 11 notation. -// Note that the jump in G occurs in y_(;,11), which confirms this timing. -// the jump occurs now in ex_(11,1) - -model; -// equation 11.3.8.a -k=A*k(-1)^alpha+(1-del)*k(-1)-c-g; -// equation 11.3.8e + 11.3.8.g -c^(-gam)= bet*(c(+1)^(-gam))*((1+tauc(-1))/(1+tauc))*((1-taui)*(1-del)/(1-taui(-1))+ - ((1-tauk)/(1-taui(-1)))*alpha*A*k(-1)^(alpha-1)); -end; - -initval; -k=1.5; -c=0.6; -g = 0.2; -tauc = 0; -taui = 0; -tauk = 0; -end; -steady; // put this in if you want to start from the initial steady state, comment it out to start from the indicated values - -endval; // The following values determine the new steady state after the shocks. -k=1.5; -c=0.4; -g =.4; -tauc =0; -taui =0; -tauk =0; -end; - -steady; // We use steady again and the enval provided are initial guesses for dynare to compute the ss. - -// The following lines produce a g sequence with a once and for all jump in g -shocks; -// we use shocks to undo that for the first 9 periods and leave g at -// it's initial value of 0 - -var g; -periods 1:9; -values 0.2; -end; - - -// now solve the model -simul(periods=100); - -// Note: y_ records the simulated endogenous variables in alphabetical order -// ys0_ records the initial steady state -// ys_ records the terminal steady state -// check that these line up at the end points -y_(:,1) -ys0_(:) -y_(:,102) - ys_(:) - -// Compute the initial steady state for consumption to later do the plots. -co=ys0_(var_index('c')); -ko = ys0_(var_index('k')); -// g is in ex_(:,1) since it is stored in alphabetical order -go = ex_(1,1) - -// The following equation compute the other endogenous variables use in the plots below -// Since they are function of capital and consumption, so we can compute them from the solved -// model above. - -// These equations were taken from page 333 of RMT2 -rbig0=1/bet; -rbig=y_(var_index('c'),2:101).^(-gam)./(bet*y_(var_index('c'),3:102).^(-gam)); -rq0=alpha*A*ko^(alpha-1); -rq=alpha*A*y_(var_index('k'),1:100).^(alpha-1); -wq0=A*ko^alpha-ko*alpha*A*ko^(alpha-1); -wq=A*y_(var_index('k'),1:100).^alpha-y_(var_index('k'),1:100).*alpha*A.*y_(var_index('k'),1:100).^(alpha-1); -sq0=(1-ex_(1,4))*A*alpha*ko^(alpha-1)+(1-del); -sq=(1-ex_(1:100,4)')*A*alpha.*y_(var_index('k'),1:100).^(alpha-1)+(1-del); - -//Now we plot the responses of the endogenous variables to the shock. - -figure -subplot(2,3,1) -plot([ko*ones(100,1) y_(var_index('k'),1:100)' ]) // note the timing: we lag capital to correct for syntax -title('k') -subplot(2,3,2) -plot([co*ones(100,1) y_(var_index('c'),2:101)' ]) -title('c') -subplot(2,3,3) -plot([rbig0*ones(100,1) rbig' ]) -title('R') -subplot(2,3,4) -plot([wq0*ones(100,1) wq' ]) -title('w/q') -subplot(2,3,5) -plot([sq0*ones(100,1) sq' ]) -title('s/q') -subplot(2,3,6) -plot([rq0*ones(100,1) rq' ]) -title('r/q') diff --git a/tests/practicing/Fig1132.mod b/tests/practicing/Fig1132.mod deleted file mode 100644 index 646602f26..000000000 --- a/tests/practicing/Fig1132.mod +++ /dev/null @@ -1,79 +0,0 @@ -// This program replicates figure 11.3.1 from chapter 11 of RMT2 by Ljungqvist and Sargent - -var c k; -varexo taui tauc tauk g; -parameters bet gam del alpha A; -bet=.95; -gam=2; -del=.2; -alpha=.33; -A=1; - -model; -k=A*k(-1)^alpha+(1-del)*k(-1)-c-g; -c^(-gam)= bet*(c(+1)^(-gam))*((1+tauc(-1))/(1+tauc))*((1-taui)*(1-del)/(1-taui(-1))+ - ((1-tauk)/(1-taui(-1)))*alpha*A*k(-1)^(alpha-1)); -end; - -initval; -k=1.5; -c=0.6; -g = 0.2; -tauc = 0; -taui = 0; -tauk = 0; -end; -steady; - -endval; -k=1.5; -c=0.6; -g = 0.2; -tauc =0.2; -taui =0; -tauk =0; -end; -steady; - -shocks; -var tauc; -periods 1:9; -values 0; -end; - -simul(periods=100); - -co=ys0_(var_index('c')); -ko = ys0_(var_index('k')); -go = ex_(1,1); - -rbig0=1/bet; -rbig=y_(var_index('c'),2:101).^(-gam)./(bet*y_(var_index('c'),3:102).^(-gam)); -rq0=alpha*A*ko^(alpha-1); -rq=alpha*A*y_(var_index('k'),1:100).^(alpha-1); -wq0=A*ko^alpha-ko*alpha*A*ko^(alpha-1); -wq=A*y_(var_index('k'),1:100).^alpha-y_(var_index('k'),1:100).*alpha*A.*y_(var_index('k'),1:100).^(alpha-1); -sq0=(1-ex_(1,4))*A*alpha*ko^(alpha-1)+(1-del); -sq=(1-ex_(1:100,4)')*A*alpha.*y_(var_index('k'),1:100).^(alpha-1)+(1-del); - -figure -subplot(2,3,1) -plot([ko*ones(100,1) y_(var_index('k'),1:100)' ]) -title('k') -subplot(2,3,2) -plot([co*ones(100,1) y_(var_index('c'),2:101)' ]) -title('c') -subplot(2,3,3) -plot([rbig0*ones(100,1) rbig' ]) -title('R') -subplot(2,3,4) -plot([wq0*ones(100,1) wq' ]) -title('w/q') -subplot(2,3,5) -plot([sq0*ones(100,1) sq' ]) -title('s/q') -subplot(2,3,6) -plot([rq0*ones(100,1) rq' ]) -title('r/q') - -print -depsc fig1132.ps diff --git a/tests/practicing/Fig1151.mod b/tests/practicing/Fig1151.mod deleted file mode 100644 index 0922b1e46..000000000 --- a/tests/practicing/Fig1151.mod +++ /dev/null @@ -1,80 +0,0 @@ -// This program replicates figure 11.3.1 from chapter 11 of RMT2 by Ljungqvist and Sargent - -var c k; -varexo taui tauc tauk g; -parameters bet gam del alpha A; -bet=.95; -gam=2; -del=.2; -alpha=.33; -A=1; - -model; -k=A*k(-1)^alpha+(1-del)*k(-1)-c-g; -c^(-gam)= bet*(c(+1)^(-gam))*((1+tauc(-1))/(1+tauc))*((1-taui)*(1-del)/(1-taui(-1))+ - ((1-tauk)/(1-taui(-1)))*alpha*A*k(-1)^(alpha-1)); -end; - -initval; -k=1.5; -c=0.6; -g = 0.2; -tauc = 0; -taui = 0; -tauk = 0; -end; -steady; - -endval; -k=1.5; -c=0.6; -g =0.2; -tauc =0; -taui =0.20; -tauk =0; -end; -steady; - -shocks; -var taui; -periods 1:9; -values 0; -end; - -simul(periods=100); - -co=ys0_(var_index('c')); -ko = ys0_(var_index('k')); -go = ex_(1,1); - -rbig0=1/bet; -rbig=y_(var_index('c'),2:101).^(-gam)./(bet*y_(var_index('c'),3:102).^(-gam)); -rq0=alpha*A*ko^(alpha-1); -rq=alpha*A*y_(var_index('k'),1:100).^(alpha-1); -wq0=A*ko^alpha-ko*alpha*A*ko^(alpha-1); -wq=A*y_(var_index('k'),1:100).^alpha-y_(var_index('k'),1:100).*alpha*A.*y_(var_index('k'),1:100).^(alpha-1); -sq0=(1-ex_(1,4))*A*alpha*ko^(alpha-1)+(1-del); -sq=(1-ex_(1:100,4)')*A*alpha.*y_(var_index('k'),1:100).^(alpha-1)+(1-del); - -figure -subplot(2,3,1) -plot([ko*ones(100,1) y_(var_index('k'),1:100)' ]) -title('k') -subplot(2,3,2) -plot([co*ones(100,1) y_(var_index('c'),2:101)' ]) -title('c') -subplot(2,3,3) -plot([rbig0*ones(100,1) rbig' ]) -title('R') -subplot(2,3,4) -plot([wq0*ones(100,1) wq' ]) -title('w/q') -subplot(2,3,5) -plot([sq0*ones(100,1) sq' ]) -title('s/q') -subplot(2,3,6) -plot([rq0*ones(100,1) rq' ]) -title('r/q') - -print -depsc fig1151.ps - diff --git a/tests/practicing/Fig1152.mod b/tests/practicing/Fig1152.mod deleted file mode 100644 index 0296a090a..000000000 --- a/tests/practicing/Fig1152.mod +++ /dev/null @@ -1,80 +0,0 @@ -// This program replicates figure 11.3.1 from chapter 11 of RMT2 by Ljungqvist and Sargent - -var c k; -varexo taui tauc tauk g; -parameters bet gam del alpha A; -bet=.95; -gam=2; -del=.2; -alpha=.33; -A=1; - -model; -k=A*k(-1)^alpha+(1-del)*k(-1)-c-g; -c^(-gam)= bet*(c(+1)^(-gam))*((1+tauc(-1))/(1+tauc))*((1-taui)*(1-del)/(1-taui(-1))+ - ((1-tauk)/(1-taui(-1)))*alpha*A*k(-1)^(alpha-1)); -end; - -initval; -k=1.5; -c=0.6; -g = 0.2; -tauc = 0; -taui = 0; -tauk = 0; -end; -steady; - -endval; -k=1.5; -c=0.6; -g =0.2; -tauc =0; -taui =0; -tauk = 0.2; -end; -steady; - -shocks; -var tauk; -periods 1:9; -values 0; -end; - -simul(periods=100); - -co=ys0_(var_index('c')); -ko = ys0_(var_index('k')); -go = ex_(1,1); - -rbig0=1/bet; -rbig=y_(var_index('c'),2:101).^(-gam)./(bet*y_(var_index('c'),3:102).^(-gam)); -rq0=alpha*A*ko^(alpha-1); -rq=alpha*A*y_(var_index('k'),1:100).^(alpha-1); -wq0=A*ko^alpha-ko*alpha*A*ko^(alpha-1); -wq=A*y_(var_index('k'),1:100).^alpha-y_(var_index('k'),1:100).*alpha*A.*y_(var_index('k'),1:100).^(alpha-1); -sq0=(1-ex_(1,4))*A*alpha*ko^(alpha-1)+(1-del); -sq=(1-ex_(1:100,4)')*A*alpha.*y_(var_index('k'),1:100).^(alpha-1)+(1-del); - -figure -subplot(2,3,1) -plot([ko*ones(100,1) y_(var_index('k'),1:100)' ]) -title('k') -subplot(2,3,2) -plot([co*ones(100,1) y_(var_index('c'),2:101)' ]) -title('c') -subplot(2,3,3) -plot([rbig0*ones(100,1) rbig' ]) -title('R') -subplot(2,3,4) -plot([wq0*ones(100,1) wq' ]) -title('w/q') -subplot(2,3,5) -plot([sq0*ones(100,1) sq' ]) -title('s/q') -subplot(2,3,6) -plot([rq0*ones(100,1) rq' ]) -title('r/q') - -print -depsc fig1152.ps - diff --git a/tests/practicing/Fig1171.mod b/tests/practicing/Fig1171.mod deleted file mode 100644 index 3870a8587..000000000 --- a/tests/practicing/Fig1171.mod +++ /dev/null @@ -1,80 +0,0 @@ -// This program replicates figure 11.3.1 from chapter 11 of RMT2 by Ljungqvist and Sargent - -var c k; -varexo taui tauc tauk g; -parameters bet gam del alpha A; -bet=.95; -gam=2; -del=.2; -alpha=.33; -A=1; - -model; -k=A*k(-1)^alpha+(1-del)*k(-1)-c-g; -c^(-gam)= bet*(c(+1)^(-gam))*((1+tauc(-1))/(1+tauc))*((1-taui)*(1-del)/(1-taui(-1))+ - ((1-tauk)/(1-taui(-1)))*alpha*A*k(-1)^(alpha-1)); -end; - -initval; -k=1.5; -c=0.6; -g = 0.2; -tauc = 0; -taui = 0; -tauk = 0; -end; -steady; - -endval; -k=1.5; -c=0.6; -g = 0.2; -tauc =0; -taui =0; -tauk =0; -end; -steady; - -shocks; -var g; -periods 10; -values 0.4; -end; - -simul(periods=100); - -co=ys0_(var_index('c')); -ko = ys0_(var_index('k')); -go = ex_(1,1); - -rbig0=1/bet; -rbig=y_(var_index('c'),2:101).^(-gam)./(bet*y_(var_index('c'),3:102).^(-gam)); -rq0=alpha*A*ko^(alpha-1); -rq=alpha*A*y_(var_index('k'),1:100).^(alpha-1); -wq0=A*ko^alpha-ko*alpha*A*ko^(alpha-1); -wq=A*y_(var_index('k'),1:100).^alpha-y_(var_index('k'),1:100).*alpha*A.*y_(var_index('k'),1:100).^(alpha-1); -sq0=(1-ex_(1,4))*A*alpha*ko^(alpha-1)+(1-del); -sq=(1-ex_(1:100,4)')*A*alpha.*y_(var_index('k'),1:100).^(alpha-1)+(1-del); - -figure -subplot(2,3,1) -plot([ko*ones(100,1) y_(var_index('k'),1:100)' ]) -title('k') -subplot(2,3,2) -plot([co*ones(100,1) y_(var_index('c'),2:101)' ]) -title('c') -subplot(2,3,3) -plot([rbig0*ones(100,1) rbig' ]) -title('R') -subplot(2,3,4) -plot([wq0*ones(100,1) wq' ]) -title('w/q') -subplot(2,3,5) -plot([sq0*ones(100,1) sq' ]) -title('s/q') -subplot(2,3,6) -plot([rq0*ones(100,1) rq' ]) -title('r/q') - -print -depsc fig1171.ps - diff --git a/tests/practicing/Fig1172.mod b/tests/practicing/Fig1172.mod deleted file mode 100644 index e1c74ee5c..000000000 --- a/tests/practicing/Fig1172.mod +++ /dev/null @@ -1,80 +0,0 @@ -// This program replicates figure 11.3.1 from chapter 11 of RMT2 by Ljungqvist and Sargent - -var c k; -varexo taui tauc tauk g; -parameters bet gam del alpha A; -bet=.95; -gam=2; -del=.2; -alpha=.33; -A=1; - -model; -k=A*k(-1)^alpha+(1-del)*k(-1)-c-g; -c^(-gam)= bet*(c(+1)^(-gam))*((1+tauc(-1))/(1+tauc))*((1-taui)*(1-del)/(1-taui(-1))+ - ((1-tauk)/(1-taui(-1)))*alpha*A*k(-1)^(alpha-1)); -end; - -initval; -k=1.5; -c=0.6; -g = 0.2; -tauc = 0; -taui = 0; -tauk = 0; -end; -steady; - -endval; -k=1.5; -c=0.6; -g =0.2; -tauc =0; -taui =0; -tauk =0; -end; -steady; - -shocks; -var taui; -periods 10; -values 0.2; -end; - -simul(periods=100); - -co=ys0_(var_index('c')); -ko = ys0_(var_index('k')); -go = ex_(1,1); - -rbig0=1/bet; -rbig=y_(var_index('c'),2:101).^(-gam)./(bet*y_(var_index('c'),3:102).^(-gam)); -rq0=alpha*A*ko^(alpha-1); -rq=alpha*A*y_(var_index('k'),1:100).^(alpha-1); -wq0=A*ko^alpha-ko*alpha*A*ko^(alpha-1); -wq=A*y_(var_index('k'),1:100).^alpha-y_(var_index('k'),1:100).*alpha*A.*y_(var_index('k'),1:100).^(alpha-1); -sq0=(1-ex_(1,4))*A*alpha*ko^(alpha-1)+(1-del); -sq=(1-ex_(1:100,4)')*A*alpha.*y_(var_index('k'),1:100).^(alpha-1)+(1-del); - -figure -subplot(2,3,1) -plot([ko*ones(100,1) y_(var_index('k'),1:100)' ]) -title('k') -subplot(2,3,2) -plot([co*ones(100,1) y_(var_index('c'),2:101)' ]) -title('c') -subplot(2,3,3) -plot([rbig0*ones(100,1) rbig' ]) -title('R') -subplot(2,3,4) -plot([wq0*ones(100,1) wq' ]) -title('w/q') -subplot(2,3,5) -plot([sq0*ones(100,1) sq' ]) -title('s/q') -subplot(2,3,6) -plot([rq0*ones(100,1) rq' ]) -title('r/q') - -print -depsc fig1172.ps - diff --git a/tests/practicing/GrowthApproximate.mod b/tests/practicing/GrowthApproximate.mod deleted file mode 100644 index 03b936a59..000000000 --- a/tests/practicing/GrowthApproximate.mod +++ /dev/null @@ -1,41 +0,0 @@ - - -periods 1000; - -var c k lab z; -varexo e; - -parameters bet the del alp tau rho s; - -bet = 0.987; -the = 0.357; -del = 0.012; -alp = 0.4; -tau = 2; -rho = 0.95; -s = 0.007; - -model; - (c^the*(1-lab)^(1-the))^(1-tau)/c=bet*((c(+1)^the*(1-lab(+1))^(1-the))^(1-tau)/c(+1))*(1+alp*exp(z(+1))*k^(alp-1)*lab(+1)^(1-alp)-del); - c=the/(1-the)*(1-alp)*exp(z)*k(-1)^alp*lab^(-alp)*(1-lab); - k=exp(z)*k(-1)^alp*lab^(1-alp)-c+(1-del)*k(-1); - z=rho*z(-1)+s*e; -end; - -initval; -k = 1; -c = 1; -lab = 0.3; -z = 0; -e = 0; -end; - -shocks; -var e; -stderr 1; -end; - -steady; - -stoch_simul(dr_algo=0,periods=1000,irf=40); -datasaver('simudata',[]); diff --git a/tests/practicing/GrowthEstimate.mod b/tests/practicing/GrowthEstimate.mod deleted file mode 100644 index 8c33ea92f..000000000 --- a/tests/practicing/GrowthEstimate.mod +++ /dev/null @@ -1,44 +0,0 @@ - -var c k lab z; -varexo e; - -parameters bet del alp rho the tau s; - -bet = 0.987; -the = 0.357; -del = 0.012; -alp = 0.4; -tau = 2; -rho = 0.95; -s = 0.007; - -model; - (c^the*(1-lab)^(1-the))^(1-tau)/c=bet*((c(+1)^the*(1-lab(+1))^(1-the))^(1-tau)/c(+1))*(1+alp*exp(z(+1))*k^(alp-1)*lab(+1)^(1-alp)-del); - c=the/(1-the)*(1-alp)*exp(z)*k(-1)^alp*lab^(-alp)*(1-lab); - k=exp(z)*k(-1)^alp*lab^(1-alp)-c+(1-del)*k(-1); - z=rho*z(-1)+s*e; -end; - -initval; -k = 1; -c = 1; -lab = 0.3; -z = 0; -e = 0; -end; - -shocks; -var e; -stderr 1; -end; - -estimated_params; -stderr e, inv_gamma_pdf, 0.95,30; -rho, beta_pdf,0.93,0.02; -the, normal_pdf,0.3,0.05; -tau, normal_pdf,2.1,0.3; -end; - -varobs c; - -estimation(datafile=simudata,mh_replic=1000,mh_jscale=0.9,nodiagnostic); diff --git a/tests/practicing/HSTBayes.mod b/tests/practicing/HSTBayes.mod deleted file mode 100644 index ef5db051c..000000000 --- a/tests/practicing/HSTBayes.mod +++ /dev/null @@ -1,62 +0,0 @@ -// Estimates the Hansen Sargent and Tallarini model by maximum likelihood. - -var s c h k i d dhat dbar mus muc muh gamma R; -varexo e_dhat e_dbar; - -parameters lambda deltah deltak mud b bet phi1 phi2 cdbar alpha1 alpha2 cdhat; -bet=0.9971; -deltah=0.682; -lambda=2.443; -alpha1=0.813; -alpha2=0.189; -phi1=0.998; -phi2=0.704; -mud=13.710; -cdhat=0.155; -cdbar=0.108; -b=32; -deltak=0.975; - -model(linear); -R=deltak+gamma; -R*bet=1; -s=(1+lambda)*c-lambda*h(-1); -h=deltah*h(-1)+(1-deltah)*c; -k=deltak*k(-1)+i; -c+i=gamma*k(-1)+d; -mus=b-s; -muc=(1+lambda)*mus+(1-deltah)*muh; -muh=bet*(deltah*muh(+1)-lambda*mus(+1)); -muc=bet*R*muc(+1); -d=mud+dbar+dhat; -dbar=(phi1+phi2)*dbar(-1) - phi1*phi2*dbar(-2) + cdbar*e_dbar; -dhat=(alpha1+alpha2)*dhat(-1) - alpha1*alpha2*dhat(-2) + cdhat*e_dhat; -end; - -shocks; -var e_dhat; -stderr 1; -var e_dbar; -stderr 1; -end; - -stoch_simul(irf=0, periods=500); -// save dataHST c i; - -estimated_params; -bet,uniform_pdf, .9499999999, 0.0288675134306; -deltah,uniform_pdf, 0.45, 0.202072594216; -lambda,uniform_pdf, 25.05, 14.4048892163; -alpha1,uniform_pdf, 0.8, 0.115470053809; -alpha2,uniform_pdf, 0.25, 0.144337567297; -phi1,uniform_pdf, 0.8, 0.115470053809; -phi2,uniform_pdf, 0.5, 0.288675134595; -mud,uniform_pdf, 24.5, 14.1450815951; -cdhat,uniform_pdf, 0.175, 0.0721687836487; -cdbar,uniform_pdf, 0.175, 0.0721687836487; - -end; - -varobs c i; -// estimation(datafile=dataHST,first_obs=1,nobs=500,mode_compute=4,MH_jscale=2); -estimation(datafile=dataHST,first_obs=1,nobs=500,mode_compute=4,mode_check,mh_replic=5000,mh_nblocks=1,mh_jscale=0.3); diff --git a/tests/practicing/HSTML.mod b/tests/practicing/HSTML.mod deleted file mode 100644 index 6e94f0f30..000000000 --- a/tests/practicing/HSTML.mod +++ /dev/null @@ -1,62 +0,0 @@ -// Estimates the Hansen Sargent and Tallarini model by maximum likelihood. - -var s c h k i d dhat dbar mus muc muh gamma R; -varexo e_dhat e_dbar; - -parameters lambda deltah deltak mud b bet phi1 phi2 cdbar alpha1 alpha2 cdhat; -bet=0.9971; -deltah=0.682; -lambda=2.443; -alpha1=0.813; -alpha2=0.189; -phi1=0.998; -phi2=0.704; -mud=13.710; -cdhat=0.155; -cdbar=0.108; -b=32; -deltak=0.975; - -model(linear); -R=deltak+gamma; -R*bet=1; -s=(1+lambda)*c-lambda*h(-1); -h=deltah*h(-1)+(1-deltah)*c; -k=deltak*k(-1)+i; -c+i=gamma*k(-1)+d; -mus=b-s; -muc=(1+lambda)*mus+(1-deltah)*muh; -muh=bet*(deltah*muh(+1)-lambda*mus(+1)); -muc=bet*R*muc(+1); -d=mud+dbar+dhat; -dbar=(phi1+phi2)*dbar(-1) - phi1*phi2*dbar(-2) + cdbar*e_dbar; -dhat=(alpha1+alpha2)*dhat(-1) - alpha1*alpha2*dhat(-2) + cdhat*e_dhat; -end; - -shocks; -var e_dhat; -stderr 1; -var e_dbar; -stderr 1; -end; - -// stoch_simul(irf=0, periods=500); -// save dataHST c i; - -estimated_params; -bet, .91, .9, .99999; -deltah, 0.4, 0.1, 0.8; -lambda, 2, 0.1, 50; -alpha1, 0.8, 0.6, 0.99999; -alpha2, 0.2, 0.01, 0.5; -phi1, 0.8, 0.6, 0.99999; -phi2, 0.5, 0.3, 0.9; -mud, 10, 1, 50; -cdhat, 0.1, 0.05, 0.2; -cdbar, 0.1, 0.05, 0.2; - -end; - -varobs c i; -estimation(datafile=dataHST,first_obs=1,nobs=500,mode_compute=4,mode_check); - diff --git a/tests/practicing/TwocountryApprox.mod b/tests/practicing/TwocountryApprox.mod deleted file mode 100644 index 02b2ced97..000000000 --- a/tests/practicing/TwocountryApprox.mod +++ /dev/null @@ -1,45 +0,0 @@ -periods 200; -var c1 c2 k1 k2 a1 a2 y1 y2; -varexo e1 e2; - -parameters gamma delta alpha beta rho; - -gamma=2; -delta=.05; -alpha=.4; -beta=.98; -rho=.85; - -model; -c1=c2; -exp(c1)^(-gamma) = beta*exp(c1(+1))^(-gamma)*(alpha*exp(a1(+1))*exp(k1)^(alpha-1)+1-delta); -exp(c2)^(-gamma) = beta*exp(c2(+1))^(-gamma)*(alpha*exp(a2(+1))*exp(k2)^(alpha-1)+1-delta); -exp(c1)+exp(c2)+exp(k1)-exp(k1(-1))*(1-delta)+exp(k2)-exp(k2(-1))*(1-delta) = exp(a1)*exp(k1(-1))^alpha+exp(a2)*exp(k2(-1))^alpha; -a1=rho*a1(-1)+e1; -a2=rho*a2(-1)+e2; -exp(y1)=exp(a1)*exp(k1(-1))^alpha; -exp(y2)=exp(a2)*exp(k2(-1))^alpha; -end; - -initval; -y1=1.1; -y2=1.1; -k1=2.8; -k2=2.8; -c1=.8; -c2=.8; -a1=0; -a2=0; -e1=0; -e2=0; -end; - -shocks; -var e1; stderr .08; -var e2; stderr .08; -end; - -steady; - -stoch_simul(dr_algo=0,periods=200); -datatomfile('simu2',[]); \ No newline at end of file diff --git a/tests/practicing/TwocountryEst.mod b/tests/practicing/TwocountryEst.mod deleted file mode 100644 index 1594a2a1a..000000000 --- a/tests/practicing/TwocountryEst.mod +++ /dev/null @@ -1,51 +0,0 @@ -periods 200; -var c1 c2 k1 k2 a1 a2 y1 y2; -varexo e1 e2; - -parameters gamma delta alpha beta rho; - -gamma=2; -delta=.05; -alpha=.4; -beta=.98; -rho=.85; - -model; -c1=c2; -exp(c1)^(-gamma) = beta*exp(c1(+1))^(-gamma)*(alpha*exp(a1(+1))*exp(k1)^(alpha-1)+1-delta); -exp(c2)^(-gamma) = beta*exp(c2(+1))^(-gamma)*(alpha*exp(a2(+1))*exp(k2)^(alpha-1)+1-delta); -exp(c1)+exp(c2)+exp(k1)-exp(k1(-1))*(1-delta)+exp(k2)-exp(k2(-1))*(1-delta) = exp(a1)*exp(k1(-1))^alpha+exp(a2)*exp(k2(-1))^alpha; -a1=rho*a1(-1)+e1; -a2=rho*a2(-1)+e2; -exp(y1)=exp(a1)*exp(k1(-1))^alpha; -exp(y2)=exp(a2)*exp(k2(-1))^alpha; -end; - -initval; -y1=1.1; -y2=1.1; -k1=2.8; -k2=2.8; -c1=.8; -c2=.8; -a1=0; -a2=0; -e1=0; -e2=0; -end; - -shocks; -var e1; stderr .08; -var e2; stderr .08; -end; - -steady; -estimated_params; -alpha, normal_pdf, .35, .05; -rho, normal_pdf, .8, .05; -stderr e1, inv_gamma_pdf, .09, 10; -stderr e2, inv_gamma_pdf, .09, 10; -end; - -varobs y1 y2; -estimation(datafile=simu2,mh_replic=1200,mh_jscale=.7,nodiagnostic); \ No newline at end of file diff --git a/tests/practicing/cagan_data.mat b/tests/practicing/cagan_data.mat deleted file mode 100644 index 0d27da163..000000000 Binary files a/tests/practicing/cagan_data.mat and /dev/null differ diff --git a/tests/practicing/dataHST.mat b/tests/practicing/dataHST.mat deleted file mode 100644 index 107ce6fbc..000000000 Binary files a/tests/practicing/dataHST.mat and /dev/null differ diff --git a/tests/practicing/data_consRicardoypg.mat b/tests/practicing/data_consRicardoypg.mat deleted file mode 100644 index d8a94dc7d..000000000 Binary files a/tests/practicing/data_consRicardoypg.mat and /dev/null differ diff --git a/tests/practicing/datasaver.m b/tests/practicing/datasaver.m deleted file mode 100644 index 0ac532a4a..000000000 --- a/tests/practicing/datasaver.m +++ /dev/null @@ -1,58 +0,0 @@ -function datasaver (s,var_list) -% datasaver saves variables simulated by Dynare -% INPUT -% s: a string containing the name of the destination *.m file -% var_list: a character matrix containting the name of the variables -% to be saved (optional, default: all endogenous variables) -% OUTPUT -% none -% This is part of the examples included in F. Barillas, R. Colacito, -% S. Kitao, C. Matthes, T. Sargent and Y. Shin (2007) "Practicing -% Dynare". - -% Modified by M. Juillard to make it also compatible with Dynare -% version 4 (12/4/07) - - -global lgy_ lgx_ y_ endo_nbr M_ oo_ - -% test and adapt for Dynare version 4 -if isempty(lgy_) - lgy_ = M_.endo_names; - lgx_ + M_.exo_names; - y_ = oo_.endo_simul; - endo_nbr = M_.endo_nbr; -end - -sm=[s,'.m']; -fid=fopen(sm,'w') ; - -n = size(var_list,1); -if n == 0 - n = endo_nbr; - ivar = [1:n]'; - var_list = lgy_; -else - ivar=zeros(n,1); - for i=1:n - i_tmp = strmatch(var_list(i,:),lgy_,'exact'); - if isempty(i_tmp) - error (['One of the specified variables does not exist']) ; - else - ivar(i) = i_tmp; - end - end -end - - -for i = 1:n - fprintf(fid,[lgy_(ivar(i),:), '=['],'\n') ; - fprintf(fid,'\n') ; - fprintf(fid,'%15.8g\n',y_(ivar(i),:)') ; - fprintf(fid,'\n') ; - fprintf(fid,'];\n') ; - fprintf(fid,'\n') ; -end -fclose(fid) ; - -return ; diff --git a/tests/practicing/hall1.mod b/tests/practicing/hall1.mod deleted file mode 100644 index 8383715f5..000000000 --- a/tests/practicing/hall1.mod +++ /dev/null @@ -1,46 +0,0 @@ -periods 5000; - -var c k mu_c b d in; -varexo e_d e_b; - -parameters R rho rho_b mu_b mu_d; -R=1.05; -//rho=0.9; -rho = 0; -mu_b=30; -mu_d=5; -rho_b = 0; - -model(linear); - - c+k = R*k(-1) + d; - mu_c = b - c; - mu_c=mu_c(+1); - d= rho*d(-1)+ mu_d*(1-rho) + e_d; - b=(1-rho_b)*mu_b+rho_b*b(-1)+e_b; - in = k - k(-1); - end; - -//With a unit root, there exists no steady state. Use the following trick. -//Supply ONE solution corresponding to the initial k that you named. - -initval; -d=mu_d; -k=100; -c = (R-1)*k +d; -mu_c=mu_b-c; -b=mu_b; -end; - -shocks; -var e_d; -stderr 1; -var e_b; -stderr 1; -end; - -steady; -check; - -stoch_simul(dr_algo=1, order=1, periods=500, irf=10); -save data_hall.mat c in; diff --git a/tests/practicing/hall1estimateBayes.mod b/tests/practicing/hall1estimateBayes.mod deleted file mode 100644 index e78ec113b..000000000 --- a/tests/practicing/hall1estimateBayes.mod +++ /dev/null @@ -1,54 +0,0 @@ -// Estimates the hall model using Bayesian method. -// hall1_estimate.mod estimates by maximum likelihood - -periods 5000; - -var c k mu_c b d in; -varexo e_d e_b; - -parameters R rho rho_b mu_b mu_d; -R=1.05; -rho=0.9; -mu_b=30; -mu_d=5; -rho_b = 0.5; - -model(linear); - - c+k = R*k(-1) + d; - mu_c = b - c; - mu_c=mu_c(+1); - d= rho*d(-1)+ mu_d*(1-rho) + e_d; - b=(1-rho_b)*mu_b+rho_b*b(-1)+e_b; -in = k - k(-1); - end; -// Michel says that in a stationary linear model, this junk is irrelevant. -// But with a unit root, there exists no steady state. Use the following trick. -// Supply ONE solution corresponding to the initial k that you named. (Michel is a gneius!! Or so he thinks -- let's see -// if this works.) - -initval; -d=mu_d; -k=100; -c = (R-1)*k +d; -mu_c=mu_b-c; -b=mu_b; -end; - -shocks; -var e_d; -stderr 0.05; -var e_b; -stderr 0.05; -end; - -estimated_params; -rho, beta_pdf, .1, 0.2; -R, normal_pdf, 1.02, 0.05; -end; - -varobs c in; -// declare the unit root variables for diffuse filter -unit_root_vars k; -//estimation(datafile=data_hall,first_obs=101,nobs=200,mh_replic=1000,mh_nblocks=2,mh_jscale=2,mode_compute=0,mode_file=hall1_estimate2_mode); -estimation(datafile=data_hall,first_obs=101,nobs=200,mh_replic=1000,mh_nblocks=2,mh_jscale=2); diff --git a/tests/practicing/hall1estimateML.mod b/tests/practicing/hall1estimateML.mod deleted file mode 100644 index 82eee5646..000000000 --- a/tests/practicing/hall1estimateML.mod +++ /dev/null @@ -1,56 +0,0 @@ -// Estimates the hall model using maximum likelihood. See hall1_estimateBayes.mod for Bayesian method - -periods 5000; - -var c k mu_c b d in; -varexo e_d e_b; - -parameters R rho rho_b mu_b mu_d; -R=1.05; -rho=0.9; -mu_b=30; -mu_d=5; -rho_b = 0.5; - -model(linear); - - c+k = R*k(-1) + d; - mu_c = b - c; - mu_c=mu_c(+1); - d= rho*d(-1)+ mu_d*(1-rho) + e_d; - b=(1-rho_b)*mu_b+rho_b*b(-1)+e_b; -in = k - k(-1); - end; -// Michel says that in a stationary linear model, this junk is irrelevant. -// But with a unit root, there exists no steady state. Use the following trick. -// Supply ONE solution corresponding to the initial k that you named. (Michel is a gneius!! Or so he thinks -- let's see -// if this works.) - -initval; -d=mu_d; -k=100; -c = (R-1)*k +d; -mu_c=mu_b-c; -b=mu_b; -end; - -shocks; -var e_d; -stderr 0.05; -var e_b; -stderr 0.05; -end; - -estimated_params; -// ML estimation setup -// parameter name, initial value, boundaries_low, ..._up; -// now we use the optimum results from csminwel for starting up Marco's -rho, -0.0159, -0.9, 0.9; -R, 1.0074, 0, 1.5; -end; - -varobs c in; -// declare the unit root variables for diffuse filter -unit_root_vars k; -estimation(datafile=data_hall,first_obs=101,nobs=200,mh_replic=0,mode_compute=4,mode_check); -// Note: there is a problem when you try to use method 5. Tom, Jan 13, 2006 \ No newline at end of file diff --git a/tests/practicing/rosen.mod b/tests/practicing/rosen.mod deleted file mode 100644 index 1f3798059..000000000 --- a/tests/practicing/rosen.mod +++ /dev/null @@ -1,55 +0,0 @@ -// Rosen schooling model -// -// The model is the one Sherwin Rosen showed Sargent in Sargent's Chicago office. -// The equations are -// -// s_t = a0 + a1*P_t + e_st ; flow supply of new engineers -// -// N_t = (1-delta)*N_{t-1} + s_{t-k} ; time to school engineers -// -// N_t = d0 - d1*W_t +e_dt ; demand for engineers -// -// P_t = (1-delta)*bet P_(t+1) + beta^k*W_(t+k); present value of wages of an engineer - - -periods 500; -var s N P W; -varexo e_s e_d; - - -parameters a0 a1 delta d0 d1 bet k; -a0=10; -a1=1; -d0=1000; -d1=1; -bet=.99; -delta=.02; - -model(linear); -s=a0+a1*P+e_s; // flow supply of new entrants -N=(1-delta)*N(-1) + s(-4); // evolution of the stock -N=d0-d1*W+e_d; // stock demand equation -P=bet*(1-delta)*P(+1) + bet^4*(1-delta)^4*W(+4); // present value of wages -end; - -initval; -s=0; -N=0; -P=0; -W=0; -end; - -shocks; -var e_d; -stderr 1; -var e_s; -stderr 1; -end; - -steady; -check; - -stoch_simul(dr_algo=1, order=1, periods=500, irf=10); -//datasaver('simudata',[]); -save data_rosen.mat s N P W; - diff --git a/tests/practicing/rosenestimateBayes.mod b/tests/practicing/rosenestimateBayes.mod deleted file mode 100644 index 64892b753..000000000 --- a/tests/practicing/rosenestimateBayes.mod +++ /dev/null @@ -1,63 +0,0 @@ -// Estimates the Rosen schooling model by maximum likelihood - -// Rosen schooling model -// -// The model is the one Sherwin Rosen showed Sargent in Sargent's Chicago office. -// The equations are -// -// s_t = a0 + a1*P_t + e_st ; flow supply of new engineers -// -// N_t = (1-delta)*N_{t-1} + s_{t-k} ; time to school engineers -// -// N_t = d0 - d1*W_t +e_dt ; demand for engineers -// -// P_t = (1-delta)*bet P_(t+1) + W_(t+k); present value of wages of an engineer - - -periods 500; -var s N P W; -varexo e_s e_d; - - - -parameters a0 a1 delta d0 d1 bet ; -a0=10; -a1=1; -d0=1000; -d1=1; -bet=.99; -delta=.02; - -model(linear); - -s=a0+a1*P+e_s; // flow supply of new entrants -N=(1-delta)*N(-1) + s(-4); // evolution of the stock -N=d0-d1*W+e_d; // stock demand equation -P=bet*(1-delta)*P(+1) + bet^4*(1-delta)^4*W(+4); // present value of wages -end; - -initval; -s=0; -N=0; -P=0; -W=0; -end; - -shocks; -var e_d; -stderr 1; -var e_s; -stderr 1; -end; - -steady; - -estimated_params; -a1, gamma_pdf, .5, .5; -d1, gamma_pdf, 2, .5; -end; - -varobs W N; -estimation(datafile=data_rosen,first_obs=101,nobs=200,mh_replic=5000,mh_nblocks=2,mh_jscale=2,mode_compute=0,mode_file=rosen_estimateML_mode); - - diff --git a/tests/practicing/rosenestimateML.mod b/tests/practicing/rosenestimateML.mod deleted file mode 100644 index 8321b5546..000000000 --- a/tests/practicing/rosenestimateML.mod +++ /dev/null @@ -1,65 +0,0 @@ -// Estimates the Rosen schooling model by maximum likelihood - -// Rosen schooling model -// -// The model is the one Sherwin Rosen showed Sargent in Sargent's Chicago office. -// The equations are -// -// s_t = a0 + a1*P_t + e_st ; flow supply of new engineers -// -// N_t = (1-delta)*N_{t-1} + s_{t-k} ; time to school engineers -// -// N_t = d0 - d1*W_t +e_dt ; demand for engineers -// -// P_t = (1-delta)*bet P_(t+1) + W_(t+k); present value of wages of an engineer - - -periods 500; -var s N P W; -varexo e_s e_d; - - - -parameters a0 a1 delta d0 d1 bet ; -a0=10; -a1=1; -d0=1000; -d1=1; -bet=.99; -delta=.02; - -model(linear); - -s=a0+a1*P+e_s; // flow supply of new entrants -N=(1-delta)*N(-1) + s(-4); // evolution of the stock -N=d0-d1*W+e_d; // stock demand equation -P=bet*(1-delta)*P(+1) + bet^4*(1-delta)^4*W(+4); // present value of wages -end; - -initval; -s=0; -N=0; -P=0; -W=0; -end; - -shocks; -var e_d; -stderr 1; -var e_s; -stderr 1; -end; - -steady; - -estimated_params; -a1, .5, -10, 10; -d1, .5, -20, 40; // these are the ranges for the parameters -end; - -varobs W N; - estimation(datafile=data_rosen,first_obs=101,nobs=200,mh_replic=0,mode_compute=4,mode_check); - - - - diff --git a/tests/practicing/sargent77.mod b/tests/practicing/sargent77.mod deleted file mode 100644 index eb7828a28..000000000 --- a/tests/practicing/sargent77.mod +++ /dev/null @@ -1,41 +0,0 @@ -// this program solves and simulates the model in -// "The Demand for Money during Hyperinflations under Rational Expectations: I" by T. Sargent, IER 1977 -// this program mainly serves as the data generating process for the estimation of the model in sargent77ML.mod and sargent77Bayes.mod -// variables are defined as follows: -// x=p_t-p_{t-1}, p being the log of the price level -// mu=m_t-m_{t-1}, m being the log of money supply -// note that in contrast to the paper eta and epsilon have variance 1 (they are multiplied by the standard deviations) - - - -var x mu a1 a2; -varexo epsilon eta; -parameters alpha lambda sig_eta sig_epsilon; -lambda=.5921; -alpha=-2.344; -sig_eta= .001; -sig_epsilon= .001; - - -// the model equations are taken from equation (27) on page 69 of the paper - -model; -x=x(-1)-lambda*a1(-1)+(1/(lambda+alpha*(1-lambda)))*sig_epsilon*epsilon-(1/(lambda+alpha*(1-lambda)))*sig_eta*eta; -mu=(1-lambda)*x(-1)+lambda*mu(-1)-lambda*a2(-1)+(1+alpha*(1-lambda))/(lambda+alpha*(1-lambda))*sig_epsilon*epsilon-(1-lambda)/(lambda+alpha*(1-lambda))*sig_eta*eta; -a1=(1/(lambda+alpha*(1-lambda)))*sig_epsilon*epsilon-(1/(lambda+alpha*(1-lambda)))*sig_eta*eta; -a2=(1+alpha*(1-lambda))/(lambda+alpha*(1-lambda))*sig_epsilon*epsilon-(1-lambda)/(lambda+alpha*(1-lambda))*sig_eta*eta; -end; - -steady; - -shocks; - -var eta; -stderr 1; -var epsilon; -stderr 1; -end; - -stoch_simul(dr_algo=1,drop=0, order=1, periods=33, irf=0); - -save data_hyperinfl.mat x mu; diff --git a/tests/practicing/sargent77Bayes.mod b/tests/practicing/sargent77Bayes.mod deleted file mode 100644 index 8895296e2..000000000 --- a/tests/practicing/sargent77Bayes.mod +++ /dev/null @@ -1,48 +0,0 @@ -// this program estimates the model in -// "The Demand for Money during Hyperinflations under Rational Expectations: I" by T. Sargent, IER 1977 using Bayesian techniques -// variables are defined as follows: -// x=p_t-p_{t-1}, p being the log of the price level -// mu=m_t-m_{t-1}, m being the log of money supply -// note that in contrast to the paper eta and epsilon have variance 1 (they are multiplied by the standard deviations) - - - -var x mu a1 a2; -varexo epsilon eta; -parameters alpha lambda sig_eta sig_epsilon; -lambda=.5921; -alpha=-2.344; -sig_eta=.001; -sig_epsilon=.001; - -model; -x=x(-1)-lambda*a1(-1)+(1/(lambda+alpha*(1-lambda)))*sig_epsilon*epsilon-(1/(lambda+alpha*(1-lambda)))*sig_eta*eta; -mu=(1-lambda)*x(-1)+lambda*mu(-1)-lambda*a2(-1)+(1+alpha*(1-lambda))/(lambda+alpha*(1-lambda))*sig_epsilon*epsilon-(1-lambda)/(lambda+alpha*(1-lambda))*sig_eta*eta; -a1=(1/(lambda+alpha*(1-lambda)))*sig_epsilon*epsilon-(1/(lambda+alpha*(1-lambda)))*sig_eta*eta; -a2=(1+alpha*(1-lambda))/(lambda+alpha*(1-lambda))*sig_epsilon*epsilon-(1-lambda)/(lambda+alpha*(1-lambda))*sig_eta*eta; -end; - -steady; - -shocks; - -var eta; -stderr 1; -var epsilon; -stderr 1; -end; - - - -estimated_params; -// Bayesian setup -lambda, uniform_pdf, 0.68, .5; -alpha, uniform_pdf, -5, 2; -sig_eta, uniform_pdf, .5, 0.25; -sig_epsilon, uniform_pdf, .5, 0.25; -end; - - -varobs mu x; -unit_root_vars x; -estimation(datafile=cagan_data,first_obs=1,nobs=34,mh_replic=25000,mh_nblocks=1,mh_jscale=1,mode_compute=4); diff --git a/tests/practicing/sargent77ML.mod b/tests/practicing/sargent77ML.mod deleted file mode 100644 index ced989947..000000000 --- a/tests/practicing/sargent77ML.mod +++ /dev/null @@ -1,52 +0,0 @@ -// this program estimates the model in -// "The Demand for Money during Hyperinflations under Rational Expectations: I" by T. Sargent, IER 1977 using maximum likelihood -// variables are defined as follows: -// x=p_t-p_{t-1}, p being the log of the price level -// mu=m_t-m_{t-1}, m being the log of money supply -// note that in contrast to the paper eta and epsilon have variance 1 (they are multiplied by the standard deviations) - - - -var x mu a1 a2; -varexo epsilon eta; -parameters alpha lambda sig_eta sig_epsilon; -lambda=.5921; -alpha=-2.344; -sig_eta=.001; -sig_epsilon=.001; - -model; -x=x(-1)-lambda*a1(-1)+(1/(lambda+alpha*(1-lambda)))*sig_epsilon*epsilon-(1/(lambda+alpha*(1-lambda)))*sig_eta*eta; -mu=(1-lambda)*x(-1)+lambda*mu(-1)-lambda*a2(-1)+(1+alpha*(1-lambda))/(lambda+alpha*(1-lambda))*sig_epsilon*epsilon-(1-lambda)/(lambda+alpha*(1-lambda))*sig_eta*eta; -a1=(1/(lambda+alpha*(1-lambda)))*sig_epsilon*epsilon-(1/(lambda+alpha*(1-lambda)))*sig_eta*eta; -a2=(1+alpha*(1-lambda))/(lambda+alpha*(1-lambda))*sig_epsilon*epsilon-(1-lambda)/(lambda+alpha*(1-lambda))*sig_eta*eta; -end; - -steady; - -shocks; - -var eta; -stderr 1; -var epsilon; -stderr 1; -end; - - - - - - - -estimated_params; -// ML estimation setup -// parameter name, initial value, boundaries_low, ..._up; -lambda, .5, 0.25, 0.75; -alpha, -2, -8, -0.1; -sig_eta, .0001, 0.0001, 0.3; -sig_epsilon, .0001, 0.0001, 0.3; -end; - -varobs mu x; -unit_root_vars x; -estimation(datafile=cagan_data,first_obs=1,nobs=34,mh_replic=0,mode_compute=4,mode_check); \ No newline at end of file