From 85d00ad267fb09641f7c78a7fa14c447b01dc184 Mon Sep 17 00:00:00 2001
From: Houtan Bastani <houtan@dynare.org>
Date: Tue, 29 Aug 2017 17:15:06 +0200
Subject: [PATCH] test suite: remove practicing directory. closes #1500

---
 .../practicing/AssetPricingApproximation.mod  |  47 -------
 tests/practicing/AssetPricingEstimate.mod     |  58 --------
 tests/practicing/BansalYaronBayes.mod         |  42 ------
 tests/practicing/BansalYaronML.mod            |  44 ------
 tests/practicing/Fig1131.mod                  |  79 -----------
 tests/practicing/Fig1131commented.mod         | 130 ------------------
 tests/practicing/Fig1132.mod                  |  79 -----------
 tests/practicing/Fig1151.mod                  |  80 -----------
 tests/practicing/Fig1152.mod                  |  80 -----------
 tests/practicing/Fig1171.mod                  |  80 -----------
 tests/practicing/Fig1172.mod                  |  80 -----------
 tests/practicing/GrowthApproximate.mod        |  41 ------
 tests/practicing/GrowthEstimate.mod           |  44 ------
 tests/practicing/HSTBayes.mod                 |  62 ---------
 tests/practicing/HSTML.mod                    |  62 ---------
 tests/practicing/TwocountryApprox.mod         |  45 ------
 tests/practicing/TwocountryEst.mod            |  51 -------
 tests/practicing/cagan_data.mat               | Bin 801 -> 0 bytes
 tests/practicing/dataHST.mat                  | Bin 7771 -> 0 bytes
 tests/practicing/data_consRicardoypg.mat      | Bin 1959 -> 0 bytes
 tests/practicing/datasaver.m                  |  58 --------
 tests/practicing/hall1.mod                    |  46 -------
 tests/practicing/hall1estimateBayes.mod       |  54 --------
 tests/practicing/hall1estimateML.mod          |  56 --------
 tests/practicing/rosen.mod                    |  55 --------
 tests/practicing/rosenestimateBayes.mod       |  63 ---------
 tests/practicing/rosenestimateML.mod          |  65 ---------
 tests/practicing/sargent77.mod                |  41 ------
 tests/practicing/sargent77Bayes.mod           |  48 -------
 tests/practicing/sargent77ML.mod              |  52 -------
 30 files changed, 1642 deletions(-)
 delete mode 100644 tests/practicing/AssetPricingApproximation.mod
 delete mode 100644 tests/practicing/AssetPricingEstimate.mod
 delete mode 100644 tests/practicing/BansalYaronBayes.mod
 delete mode 100644 tests/practicing/BansalYaronML.mod
 delete mode 100644 tests/practicing/Fig1131.mod
 delete mode 100644 tests/practicing/Fig1131commented.mod
 delete mode 100644 tests/practicing/Fig1132.mod
 delete mode 100644 tests/practicing/Fig1151.mod
 delete mode 100644 tests/practicing/Fig1152.mod
 delete mode 100644 tests/practicing/Fig1171.mod
 delete mode 100644 tests/practicing/Fig1172.mod
 delete mode 100644 tests/practicing/GrowthApproximate.mod
 delete mode 100644 tests/practicing/GrowthEstimate.mod
 delete mode 100644 tests/practicing/HSTBayes.mod
 delete mode 100644 tests/practicing/HSTML.mod
 delete mode 100644 tests/practicing/TwocountryApprox.mod
 delete mode 100644 tests/practicing/TwocountryEst.mod
 delete mode 100644 tests/practicing/cagan_data.mat
 delete mode 100644 tests/practicing/dataHST.mat
 delete mode 100644 tests/practicing/data_consRicardoypg.mat
 delete mode 100644 tests/practicing/datasaver.m
 delete mode 100644 tests/practicing/hall1.mod
 delete mode 100644 tests/practicing/hall1estimateBayes.mod
 delete mode 100644 tests/practicing/hall1estimateML.mod
 delete mode 100644 tests/practicing/rosen.mod
 delete mode 100644 tests/practicing/rosenestimateBayes.mod
 delete mode 100644 tests/practicing/rosenestimateML.mod
 delete mode 100644 tests/practicing/sargent77.mod
 delete mode 100644 tests/practicing/sargent77Bayes.mod
 delete mode 100644 tests/practicing/sargent77ML.mod

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
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GIT binary patch
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diff --git a/tests/practicing/dataHST.mat b/tests/practicing/dataHST.mat
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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