From 187f543a055e33c1608033caab1da4933732703d Mon Sep 17 00:00:00 2001
From: michel <michel@ac1d8469-bf42-47a9-8791-bf33cf982152>
Date: Tue, 4 Dec 2007 15:46:49 +0000
Subject: [PATCH] adding examples from Practicing Dynare

git-svn-id: https://www.dynare.org/svn/dynare/dynare_v4@1471 ac1d8469-bf42-47a9-8791-bf33cf982152
---
 .../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 0 -> 801 bytes
 tests/practicing/dataHST.mat                  | Bin 0 -> 7771 bytes
 tests/practicing/data_consRicardoypg.mat      | Bin 0 -> 1959 bytes
 tests/practicing/datasaver.m                  |  44 ++++++
 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, 1628 insertions(+)
 create mode 100644 tests/practicing/AssetPricingApproximation.mod
 create mode 100644 tests/practicing/AssetPricingEstimate.mod
 create mode 100644 tests/practicing/BansalYaronBayes.mod
 create mode 100644 tests/practicing/BansalYaronML.mod
 create mode 100644 tests/practicing/Fig1131.mod
 create mode 100644 tests/practicing/Fig1131commented.mod
 create mode 100644 tests/practicing/Fig1132.mod
 create mode 100644 tests/practicing/Fig1151.mod
 create mode 100644 tests/practicing/Fig1152.mod
 create mode 100644 tests/practicing/Fig1171.mod
 create mode 100644 tests/practicing/Fig1172.mod
 create mode 100644 tests/practicing/GrowthApproximate.mod
 create mode 100644 tests/practicing/GrowthEstimate.mod
 create mode 100644 tests/practicing/HSTBayes.mod
 create mode 100644 tests/practicing/HSTML.mod
 create mode 100644 tests/practicing/TwocountryApprox.mod
 create mode 100644 tests/practicing/TwocountryEst.mod
 create mode 100644 tests/practicing/cagan_data.mat
 create mode 100644 tests/practicing/dataHST.mat
 create mode 100644 tests/practicing/data_consRicardoypg.mat
 create mode 100644 tests/practicing/datasaver.m
 create mode 100644 tests/practicing/hall1.mod
 create mode 100644 tests/practicing/hall1estimateBayes.mod
 create mode 100644 tests/practicing/hall1estimateML.mod
 create mode 100644 tests/practicing/rosen.mod
 create mode 100644 tests/practicing/rosenestimateBayes.mod
 create mode 100644 tests/practicing/rosenestimateML.mod
 create mode 100644 tests/practicing/sargent77.mod
 create mode 100644 tests/practicing/sargent77Bayes.mod
 create mode 100644 tests/practicing/sargent77ML.mod

diff --git a/tests/practicing/AssetPricingApproximation.mod b/tests/practicing/AssetPricingApproximation.mod
new file mode 100644
index 000000000..ae762083b
--- /dev/null
+++ b/tests/practicing/AssetPricingApproximation.mod
@@ -0,0 +1,47 @@
+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
new file mode 100644
index 000000000..99db53dc8
--- /dev/null
+++ b/tests/practicing/AssetPricingEstimate.mod
@@ -0,0 +1,58 @@
+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
new file mode 100644
index 000000000..2c55da483
--- /dev/null
+++ b/tests/practicing/BansalYaronBayes.mod
@@ -0,0 +1,42 @@
+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
new file mode 100644
index 000000000..3e49ab1ce
--- /dev/null
+++ b/tests/practicing/BansalYaronML.mod
@@ -0,0 +1,44 @@
+
+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
new file mode 100644
index 000000000..161a1a69f
--- /dev/null
+++ b/tests/practicing/Fig1131.mod
@@ -0,0 +1,79 @@
+// 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
new file mode 100644
index 000000000..dafb29f57
--- /dev/null
+++ b/tests/practicing/Fig1131commented.mod
@@ -0,0 +1,130 @@
+// 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
new file mode 100644
index 000000000..c291403b5
--- /dev/null
+++ b/tests/practicing/Fig1132.mod
@@ -0,0 +1,79 @@
+// 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
new file mode 100644
index 000000000..bfcc6fd49
--- /dev/null
+++ b/tests/practicing/Fig1151.mod
@@ -0,0 +1,80 @@
+// 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
new file mode 100644
index 000000000..da14b0779
--- /dev/null
+++ b/tests/practicing/Fig1152.mod
@@ -0,0 +1,80 @@
+// 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
new file mode 100644
index 000000000..353ddcee0
--- /dev/null
+++ b/tests/practicing/Fig1171.mod
@@ -0,0 +1,80 @@
+// 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
new file mode 100644
index 000000000..969b71f74
--- /dev/null
+++ b/tests/practicing/Fig1172.mod
@@ -0,0 +1,80 @@
+// 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
new file mode 100644
index 000000000..a33f62bcd
--- /dev/null
+++ b/tests/practicing/GrowthApproximate.mod
@@ -0,0 +1,41 @@
+
+
+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
new file mode 100644
index 000000000..9ef1f4a3b
--- /dev/null
+++ b/tests/practicing/GrowthEstimate.mod
@@ -0,0 +1,44 @@
+
+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
new file mode 100644
index 000000000..0efb6d742
--- /dev/null
+++ b/tests/practicing/HSTBayes.mod
@@ -0,0 +1,62 @@
+// 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
new file mode 100644
index 000000000..52f7febfb
--- /dev/null
+++ b/tests/practicing/HSTML.mod
@@ -0,0 +1,62 @@
+// 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
new file mode 100644
index 000000000..024cd4d79
--- /dev/null
+++ b/tests/practicing/TwocountryApprox.mod
@@ -0,0 +1,45 @@
+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
new file mode 100644
index 000000000..bc5a41f4c
--- /dev/null
+++ b/tests/practicing/TwocountryEst.mod
@@ -0,0 +1,51 @@
+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
new file mode 100644
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diff --git a/tests/practicing/datasaver.m b/tests/practicing/datasaver.m
new file mode 100644
index 000000000..dcee78ca4
--- /dev/null
+++ b/tests/practicing/datasaver.m
@@ -0,0 +1,44 @@
+% Copyright (C) 2001 Michel Juillard
+%
+function dynatype (s,var_list)
+% DYNATYPE :	DYNATYPE ( [ 'filename' ] )
+%		This optional command saves the simulation
+%		results in a text file. The name of each 
+%		variable preceeds the corresponding results.
+%		This command must follow SIMUL.
+
+global lgy_ lgx_ y_ endo_nbr
+
+%fid=fopen([s,'.m'],'w') ;
+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
new file mode 100644
index 000000000..974fbca7e
--- /dev/null
+++ b/tests/practicing/hall1.mod
@@ -0,0 +1,46 @@
+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 c in;
\ No newline at end of file
diff --git a/tests/practicing/hall1estimateBayes.mod b/tests/practicing/hall1estimateBayes.mod
new file mode 100644
index 000000000..0fe590dab
--- /dev/null
+++ b/tests/practicing/hall1estimateBayes.mod
@@ -0,0 +1,54 @@
+//  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
new file mode 100644
index 000000000..7eb498871
--- /dev/null
+++ b/tests/practicing/hall1estimateML.mod
@@ -0,0 +1,56 @@
+// 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
new file mode 100644
index 000000000..ed71d8940
--- /dev/null
+++ b/tests/practicing/rosen.mod
@@ -0,0 +1,55 @@
+// 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 s N P W;
+
diff --git a/tests/practicing/rosenestimateBayes.mod b/tests/practicing/rosenestimateBayes.mod
new file mode 100644
index 000000000..e93ab59da
--- /dev/null
+++ b/tests/practicing/rosenestimateBayes.mod
@@ -0,0 +1,63 @@
+// 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
new file mode 100644
index 000000000..f9768f20e
--- /dev/null
+++ b/tests/practicing/rosenestimateML.mod
@@ -0,0 +1,65 @@
+// 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
new file mode 100644
index 000000000..f2983fdf4
--- /dev/null
+++ b/tests/practicing/sargent77.mod
@@ -0,0 +1,41 @@
+// 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 x mu;
\ No newline at end of file
diff --git a/tests/practicing/sargent77Bayes.mod b/tests/practicing/sargent77Bayes.mod
new file mode 100644
index 000000000..39492e09a
--- /dev/null
+++ b/tests/practicing/sargent77Bayes.mod
@@ -0,0 +1,48 @@
+// 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
new file mode 100644
index 000000000..2a5ad6d36
--- /dev/null
+++ b/tests/practicing/sargent77ML.mod
@@ -0,0 +1,52 @@
+// 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