examples/Gali_2015.mod

+/*
+ * This file shows how to use "system prior"-type prior restrictions as in
+ * Michal Andrle/Miroslav Plašil (2018): "Econometrics with system priors",
+ * Economics Letters, 172, pp. 134-137 during estimation based on
+ * the baseline New Keynesian model of Jordi Galí (2015): Monetary Policy, Inflation,
+ * and the Business Cycle, Princeton University Press, Second Edition, Chapter 3
+ *
+ * THIS MOD-FILE REQUIRES DYNARE 4.5 OR HIGHER
+ *
+ * Notes:
+ *  - The estimation will automatically take the Gali_2015_prior_restrictions.m into
+ *      account, which has the required name and format
+ *  - Estimation is based on simulated data
+ *  - The file also shows how to use a prior/posterior-function
+ *
+ * This implementation was written by Johannes Pfeifer. In case you spot mistakes,
+ * email me at jpfeifer@gmx.de
+ *
+ * Please note that the following copyright notice only applies to this Dynare
+ */
+
+/*
+ * Copyright © 2021-2023 Dynare Team
+ *
+ * This file is part of Dynare.
+ *
+ * Dynare is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * Dynare is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+var pi          ${\pi}$                 (long_name='inflation')
+    y_gap       ${\tilde y}$            (long_name='output gap')
+    y_nat       ${y^{nat}}$             (long_name='natural output')      //(in contrast to the textbook defined in deviation from steady state)
+    y           ${y}$                   (long_name='output')
+    yhat        ${\hat y}$              (long_name='output deviation from steady state')
+    r_nat       ${r^{nat}}$             (long_name='natural interest rate')
+    i           ${i}$                   (long_name='nominal interrst rate')
+    n           ${n}$                   (long_name='hours worked')
+    nu          ${\nu}$                 (long_name='AR(1) monetary policy shock process')
+    a           ${a}$                   (long_name='AR(1) technology shock process')
+    z           ${z}$                   (long_name='AR(1) preference shock process')
+    p           ${p}$                   (long_name='price level')
+    w           ${w}$                   (long_name='nominal wage')
+    c           ${c}$                   (long_name='consumption')
+;
+
+varexo  eps_a       ${\varepsilon_a}$       (long_name='technology shock')
+        eps_nu  ${\varepsilon_\nu}$     (long_name='monetary policy shock')
+        eps_z       ${\varepsilon_z}$   (long_name='preference shock innovation')
+       ;
+
+parameters alppha       ${\alpha}$     (long_name='capital share')
+    betta               ${\beta}$       (long_name='discount factor')
+    rho_a               ${\rho_a}$      (long_name='autocorrelation technology shock')
+    rho_nu          ${\rho_{\nu}}$  (long_name='autocorrelation monetary policy shock')
+    rho_z               ${\rho_{z}}$    (long_name='autocorrelation monetary demand shock')
+    siggma              ${\sigma}$      (long_name='inverse EIS')
+    varphi              ${\varphi}$     (long_name='inverse Frisch elasticity')
+    phi_pi              ${\phi_{\pi}}$  (long_name='inflation feedback Taylor Rule')
+    phi_y               ${\phi_{y}}$    (long_name='output feedback Taylor Rule')
+    eta                 ${\eta}$        (long_name='semi-elasticity of money demand')
+    epsilon             ${\epsilon}$    (long_name='demand elasticity')
+    theta               ${\theta}$      (long_name='Calvo parameter')
+    ;
+%----------------------------------------------------------------
+% Parametrization, p. 67  and p. 113-115
+%----------------------------------------------------------------
+siggma = 1;
+varphi = 5;
+phi_pi = 1.5;
+phi_y  = 0.125;
+theta = 3/4;
+rho_nu =0.5;
+rho_z  = 0.5;
+rho_a  = 0.9;
+betta  = 0.99;
+eta  = 3.77; %footnote 11, p. 115
+alppha = 1/4;
+epsilon = 9;
+
+%----------------------------------------------------------------
+% First Order Conditions
+%----------------------------------------------------------------
+
+model(linear);
+//Composite parameters
+#Omega=(1-alppha)/(1-alppha+alppha*epsilon);        %defined on page 60
+#psi_n_ya=(1+varphi)/(siggma*(1-alppha)+varphi+alppha);   %defined on page 62
+#lambda=(1-theta)*(1-betta*theta)/theta*Omega;      %defined on page 61
+#kappa=lambda*(siggma+(varphi+alppha)/(1-alppha));     %defined on page 63
+[name='New Keynesian Phillips Curve eq. (22)']
+pi=betta*pi(+1)+kappa*y_gap;
+[name='Dynamic IS Curve eq. (23)']
+y_gap=-1/siggma*(i-pi(+1)-r_nat)+y_gap(+1);
+[name='Interest Rate Rule eq. (26)']
+i=phi_pi*pi+phi_y*yhat+nu;
+[name='Definition natural rate of interest eq. (24)']
+r_nat=-siggma*psi_n_ya*(1-rho_a)*a+(1-rho_z)*z;
+[name='Definition natural output, eq. (20)']
+y_nat=psi_n_ya*a;
+[name='Definition output gap']
+y_gap=y-y_nat;
+[name='Monetary policy shock']
+nu=rho_nu*nu(-1)+eps_nu;
+[name='TFP shock']
+a=rho_a*a(-1)+eps_a;
+[name='Production function (eq. 14)']
+y=a+(1-alppha)*n;
+[name='Preference shock, p. 54']
+z=rho_z*z(-1) - eps_z;
+[name='Output deviation from steady state']
+yhat=y-steady_state(y);
+[name='Definition price level']
+pi=p-p(-1);
+[name='resource constraint, eq. (12)']
+y=c;
+[name='FOC labor, eq. (2)']
+w-p=siggma*c+varphi*n;
+end;
+
+
+shocks;
+    var eps_nu = 0.0025^2; //1 standard deviation shock of 25 basis points, i.e. 1 percentage point annualized
+end;
+
+% simulate data
+stoch_simul(periods=100,drop=0,irf=0) yhat;
+% save data
+datatomfile('sim_data',{'yhat'});
+
+estimated_params;
+theta,0.75,beta_pdf,0.5,0.1;
+betta, beta_pdf, 0.993, 0.002;
+alppha, beta_pdf, 0.25, 0.02;
+end;
+
+varobs yhat;
+
+% Run prior function to get prior slope of the PC based on independent priors
+hh_fig=figure('Name','Slope of the Phillips Curve');
+prior_function(function='Gali_2015_PC_slope');
+PC_slope_vec=cell2mat(oo_.prior_function_results(:,1));
+
+optimal_bandwidth = mh_optimal_bandwidth(PC_slope_vec,length(PC_slope_vec),0,'gaussian');
+[density(:,1),density(:,2)] = kernel_density_estimate(PC_slope_vec,512,length(PC_slope_vec),optimal_bandwidth,'gaussian');
+figure(hh_fig)
+subplot(3,1,1)
+plot(density(:,1),density(:,2));
+title('Prior')
+
+% Run estimation with 1 observation to show effect of _prior_restriction .m
+% on independent prior
+estimation(datafile='sim_data',mode_compute=5,mh_replic=2001,mh_nblocks=1,diffuse_filter,nobs=1,mh_jscale=0.8);
+posterior_function(function='Gali_2015_PC_slope');
+PC_slope_vec=cell2mat(oo_.posterior_function_results(:,1));
+optimal_bandwidth = mh_optimal_bandwidth(PC_slope_vec,length(PC_slope_vec),0,'gaussian');
+[density(:,1),density(:,2)] = kernel_density_estimate(PC_slope_vec,512,length(PC_slope_vec),optimal_bandwidth,'gaussian');
+figure(hh_fig)
+subplot(3,1,2)
+plot(density(:,1),density(:,2));
+title('Updated Prior')
+
+
+% Run estimation with full observations
+estimation(datafile='sim_data',mode_compute=5,mh_replic=2001,mh_nblocks=1,diffuse_filter,nobs=100,mh_jscale=0.8);
+
+posterior_function(function='Gali_2015_PC_slope');
+PC_slope_vec=cell2mat(oo_.posterior_function_results(:,1));
+optimal_bandwidth = mh_optimal_bandwidth(PC_slope_vec,length(PC_slope_vec),0,'gaussian');
+[density(:,1),density(:,2)] = kernel_density_estimate(PC_slope_vec,512,length(PC_slope_vec),optimal_bandwidth,'gaussian');
+figure(hh_fig)
+subplot(3,1,3)
+plot(density(:,1),density(:,2));
+title('Posterior')

examples/Gali_2015_PC_slope.m

+function output_cell =PC_slope(xparam1,M_,options_,oo_,estim_params_,bayestopt_,dataset_,dataset_info)
+% output_cell =PC_slope(xparam1,M_,options_,oo_,estim_params_,bayestopt_,dataset_,dataset_info);
+% This is an example file computing statistics on the prior/posterior draws. The
+% function allows read-only access to all Dynare structures. However, those
+% structures are local to this function.  Changing them will not affect
+% other Dynare functions and you cannot use them to pass results to other
+% Dynare functions.
+% The function takes one and only one output argument: an 1 by n cell.
+% Using functions like cell2mat, the contents of the cell can be easily
+% transformed back to matrices. See the fs2000_posterior_function.mod for
+% an example
+
+% INPUTS
+%   xparam1                      Current parameter draw
+%   M_           [structure]     Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
+%   options_     [structure]     Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
+%   oo_          [structure]     Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
+%   estim_params_[structure]     Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
+%   bayestopt_   [structure]     Matlab's structure describing the parameter options (initialized by dynare, see @ref{bayestopt_}).
+%   dataset_     [structure]     Matlab's structure storing the dataset
+%   dataset_info [structure]     Matlab's structure storing the information about the dataset
+
+% Output
+%   output_cell  [1 by n cell]   1 by n Matlab cell allowing to store any
+%                                desired computation or result (strings, matrices, structures, etc.)
+
+% Copyright © 2021 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+
+%% store the slope based on the parameter draw
+NumberOfParameters = M_.param_nbr;
+for ii = 1:NumberOfParameters
+  paramname = deblank(M_.param_names{ii,:});
+  eval([ paramname ' = M_.params(' int2str(ii) ');']);
+end
+
+Omega=(1-alppha)/(1-alppha+alppha*epsilon);
+lambda=(1-theta)*(1-betta*theta)/theta*Omega;      %defined on page 61
+output_cell{1,1}=lambda*(siggma+(varphi+alppha)/(1-alppha));     %defined on page 63
+
+end
\ No newline at end of file

examples/Gali_2015_prior_restrictions.m

+function log_prior_val=Gali_2015_prior_restrictions(M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_, dataset_, dataset_info);
+% function prior_val=Gali_2015_prior_restrictions(M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_, dataset_, dataset_info);
+% Example of a _prior_restrictions-file automatically called during
+% estimation
+% It imposes a prior of the slope of the New Keynesian Phillips Curve of
+% 0.03. As the slope is a composite of other parameters with independent
+% priors, a separate function is required to do this.
+
+% Copyright © 2021-2023 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
+
+
+% read out parameters to access them with their name
+NumberOfParameters = M_.param_nbr;
+for ii = 1:NumberOfParameters
+  paramname = M_.param_names{ii};
+  eval([ paramname ' = M_.params(' int2str(ii) ');']);
+end
+
+Omega=(1-alppha)/(1-alppha+alppha*epsilon);
+lambda=(1-theta)*(1-betta*theta)/theta*Omega;      %defined on page 61
+kappa=lambda*(siggma+(varphi+alppha)/(1-alppha));     %defined on page 63
+
+prior_mean=0.03;
+prior_std=0.02;
+log_prior_val=log(normpdf(kappa,prior_mean,prior_std));
\ No newline at end of file

examples/NK_baseline.mod

 /*
  * This file implements the Baseline New Keynesian DSGE model described in
- * much detail in Jesús Fernández-Villaverde and Juan F. Rubio-Ramírez (2006): "A Baseline DSGE 
- * Model", available at http://economics.sas.upenn.edu/~jesusfv/benchmark_DSGE.pdf
+ * much detail in Jesús Fernández-Villaverde and Juan F. Rubio-Ramírez (2006): “A Baseline DSGE
+ * Model”, available at http://economics.sas.upenn.edu/~jesusfv/benchmark_DSGE.pdf
  *
  * The parametrization is based on the estimated version of this model in
- * Jesús Fernández-Villaverde (2010): "The econometrics of DSGE models", 
+ 
  * SERIEs, Vol. 1, pp. 3-49, DOI 10.1007/s13209-009-0014-7
  *
  * This implementation was written by Benjamin Born and Johannes Pfeifer. In
@@ -17,23 +17,26 @@
  * and ii) solve a nonlinear equation using a numerical solver to find the steady
  * state of labor. It provides an example on how the steady state file can be used
  * to circumvent some of the limitation of Dynare mod-file by accessing an external
- * file that allows calling general Matlab routines. These capacities will mostly be 
+ * file that allows calling general MATLAB routines. These capacities will mostly be
  * interesting for power users. If one just wants to provide analytical steady state
  * values and update parameters, the steady_state_model-block allows an easy and convenient
  * alternative. It even allows calling numerical solvers like fsolve. For an example, see
  * example3.mod
  *
  * The model is written in the beginning of period stock notation. To make the model
- * conform with Dynare's end of period stock notation, we use the 
+ * conform with Dynare’s end of period stock notation, we use the
  * predetermined_variables-command.
  * 
+ * The model has been implemented in detrended form, i.e. the \mu_{i,t} are actually
+ * the the growth rates of the original \mu_{i,t}^{orig} in the paper, i.e. 
+ *  log(\mu_{i,t})=log(\mu_{i,t}^{orig}/\mu_{i,t-1}^{orig})
+ * 
  * Please note that the following copyright notice only applies to this Dynare
  * implementation of the model.
-
  */
 
 /*
- * Copyright (C) 2013-2016 Dynare Team
+ * Copyright © 2013-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -48,75 +51,81 @@
  * GNU General Public License for more details.
  *
  * You should have received a copy of the GNU General Public License
- * along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+ * along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
  */
 
-var d       //preference shock
-    c       //consumption
-    mu_z 	//trend growth rate of the economy (from neutral and investment specific technology)
-    mu_I    //growth rate of investment-specific technology growth
-    mu_A    //growth rate of neutral technology
-    lambda  //Lagrange multiplier
-    R       //Nominal Interest rate
-    PI      //Inflation
-    r       //rental rate of capital
-    x       //investment
-    u       //capacity utilization
-    q       //Tobin's marginal q
-    f       //variable for recursive formulation of wage setting
-    ld      //aggregate labor demand
-    w       //real wage
-    wstar   //optimal real wage
-    PIstarw //optimal wage inflation
-    PIstar  //optimal price inflation
-    g1      //variable 1 for recursive formulation of price setting
-    g2      //variable 2 for recursive formulation of price setting
-    yd      //aggregate output
-    mc      //marginal costs
-    k       //capital
-    vp      //price dispersion term
-    vw      //wage dispersion term
-    l       //aggregate labor bundle
-    phi     //labor disutility shock
-    F;      //firm profits
-    
-varexo epsd epsphi epsmu_I epsA epsm;
+var d       (long_name='preference shock')
+    c       (long_name='consumption')
+    mu_z    (long_name='trend growth rate of the economy (from neutral and investment specific technology)')
+    mu_I    (long_name='growth rate of investment-specific technology growth')
+    mu_A    (long_name='growth rate of neutral technology')
+    lambda  (long_name='Lagrange multiplier')
+    R       (long_name='Nominal Interest rate')
+    PI      (long_name='Inflation')
+    r       (long_name='rental rate of capital')
+    x       (long_name='investment')
+    u       (long_name='capacity utilization')
+    q       (long_name='Tobin marginal q')
+    f       (long_name='variable for recursive formulation of wage setting')
+    ld      (long_name='aggregate labor demand')
+    w       (long_name='real wage')
+    wstar   (long_name='optimal real wage')
+    PIstarw (long_name='optimal wage inflation')
+    PIstar  (long_name='optimal price inflation')
+    g1      (long_name='variable 1 for recursive formulation of price setting')
+    g2      (long_name='variable 2 for recursive formulation of price setting')
+    yd      (long_name='aggregate output')
+    mc      (long_name='marginal costs')
+    k       (long_name='capital')
+    vp      (long_name='price dispersion term')
+    vw      (long_name='wage dispersion term')
+    l       (long_name='aggregate labor bundle')
+    phi     (long_name='labor disutility shock')
+    F       (long_name='firm profits')
+    ;
 
-predetermined_variables k;
+varexo epsd (long_name='Innovation preference shock')
+    epsphi  (long_name='Innovation labor disutility shock')
+    epsmu_I (long_name='Innovation investment-specific technology')
+    epsA    (long_name='Innovation neutral technology')
+    epsm    (long_name='Innovation monetary policy shock')
+    ;
 
-parameters h            //consumption habits
-           betta        //discount factor
-           gammma1      //capital utilization, linear term
-           gammma2      //capital utilization, quadratic term
-           delta        //depreciation rate
-           kappa        //capital adjustment costs parameter
-           eta          //elasticity of substitution between labor varieties
-           epsilon      //elasticity of substitution between goods varieties
-           varpsi       //labor disutility parameter
-           gammma       //inverse Frisch elasticity
-           chiw         //wage indexation parameter
-           chi          //price indexation
-           thetap       //Calvo parameter prices
-           thetaw       //Calvo parameter wages
-           alppha       //capital share
-           Rbar         //steady state interest rate
-           PIbar        //steady state inflation
-           gammmaR      //interest smoothing coefficient Taylor rule
-           gammmaPI     //feedback coefficient to inflation monetary policy rule
-           gammmay      //feedback coefficient to output growth deviation in monetary policy rule
-           Phi          //firms fixed costs
-           rhod         //autocorrelation preference shock
-           rhophi       //autocorrelation labor disutility shock
-           Lambdamu  	//steady state growth rate of investmentment-specific technology
-           LambdaA      //steady state neutral technology growth 
-           Lambdax      //steady state growth rate of investment
-           LambdaYd     //steady state growth rate of output
-           sigma_d      //standard deviation preference shock
-           sigma_phi    //standard deviation labor disutility shock
-           sigma_mu     //standard deviation investment-specific technology
-           sigma_A      //standard deviation neutral technology
-           sigma_m;      //standard deviation preference shock
+predetermined_variables k;
 
+parameters h            (long_name='consumption habits')
+           betta        (long_name='discount factor')
+           gammma1      (long_name='capital utilization, linear term')
+           gammma2      (long_name='capital utilization, quadratic term')
+           delta        (long_name='depreciation rate')
+           kappa        (long_name='capital adjustment costs parameter')
+           eta          (long_name='elasticity of substitution between labor varieties')
+           epsilon      (long_name='elasticity of substitution between goods varieties')
+           varpsi       (long_name='labor disutility parameter')
+           gammma       (long_name='inverse Frisch elasticity')
+           chiw         (long_name='wage indexation parameter')
+           chi          (long_name='price indexation')
+           thetap       (long_name='Calvo parameter prices')
+           thetaw       (long_name='Calvo parameter wages')
+           alppha       (long_name='capital share')
+           Rbar         (long_name='steady state interest rate')
+           PIbar        (long_name='steady state inflation')
+           gammmaR      (long_name='interest smoothing coefficient Taylor rule')
+           gammmaPI     (long_name='feedback coefficient to inflation monetary policy rule')
+           gammmay      (long_name='feedback coefficient to output growth deviation in monetary policy rule')
+           Phi          (long_name='firms fixed costs')
+           rhod         (long_name='autocorrelation preference shock')
+           rhophi       (long_name='autocorrelation labor disutility shock')
+           Lambdamu  	(long_name='steady state growth rate of investmentment-specific technology')
+           LambdaA      (long_name='steady state neutral technology growth')
+           Lambdax      (long_name='steady state growth rate of investment')
+           LambdaYd     (long_name='steady state growth rate of output')
+           sigma_d      (long_name='standard deviation preference shock')
+           sigma_phi    (long_name='standard deviation labor disutility shock')
+           sigma_mu     (long_name='standard deviation investment-specific technology')
+           sigma_A      (long_name='standard deviation neutral technology')
+           sigma_m      (long_name='standard deviation monetary policy shock')
+            ;
 
 //Note that the parameter naming in FV(2010) differs from FV(2006)
 //Fixed parameters, taken from FV(2010), Table 2, p. 37
@@ -146,12 +155,12 @@ gammmaPI =1.29;
 PIbar = 1.01;
 rhod  = 0.12;
 rhophi = 0.93;
-sigma_A = -3.97;
-sigma_d = -1.51;
+sigma_A =  exp(-3.97);
+sigma_d = exp(-1.51);
 
-sigma_phi =-2.36;
-sigma_mu =-5.43;
-sigma_m  =-5.85;
+sigma_phi =exp(-2.36);
+sigma_mu =exp(-5.43);
+sigma_m  =exp(-5.85);
 Lambdamu=3.4e-3;
 LambdaA = 2.8e-3;
 
@@ -164,7 +173,7 @@ algorithm, while the parameters initialized here are only set once for the initi
 values of the parameters they depend on:
 
 gammma1=mu_z*mu_I/betta-(1-delta);
-R=1+(PIbar*mu_z/betta-1);
+Rbar=1+(PIbar*mu_z/betta-1);
 Lambdax=exp(LambdaYd);
 LambdaYd= (LambdaA+alppha*Lambdamu)/(1-alppha);
 */
@@ -177,70 +186,77 @@ FV(2006), p. 20, section 3.2.
 */
 
 model;
-//1. FOC consumption
+[name='FOC consumption']
 d*(c-h*c(-1)*mu_z^(-1))^(-1)-h*betta*d(+1)*(c(+1)*mu_z(+1)-h*c)^(-1)=lambda;
-//2. Euler equation
+[name='Euler equation']
 lambda=betta*lambda(+1)*mu_z(+1)^(-1)/PI(+1)*R;
-//3. FOC capital utilization
+[name='FOC capital utilization']
 r=gammma1+gammma2*(u-1);
-//4. FOC capital
+[name='FOC capital']
 q=betta*lambda(+1)/lambda*mu_z(+1)^(-1)*mu_I(+1)^(-1)*((1-delta)*q(+1)+r(+1)*u(+1)-(gammma1*(u(+1)-1)+gammma2/2*(u(+1)-1)^2));
-//5. FOC investment
+[name='FOC investment']
 1=q*(1-(kappa/2*(x/x(-1)*mu_z-Lambdax)^2)-(kappa*(x/x(-1)*mu_z-Lambdax)*x/x(-1)*mu_z))
   +betta*q(+1)*lambda(+1)/lambda*mu_z(+1)^(-1)*kappa*(x(+1)/x*mu_z(+1)-Lambdax)*(x(+1)/x*mu_z(+1))^2;
-//6-7. Wage setting
+[name='Wage setting 1']
 f=(eta-1)/eta*wstar^(1-eta)*lambda*w^eta*ld+betta*thetaw*(PI^chiw/PI(+1))^(1-eta)*(wstar(+1)/wstar*mu_z(+1))^(eta-1)*f(+1);
+[name='Wage setting 2']
 f=varpsi*d*phi*PIstarw^(-eta*(1+gammma))*ld^(1+gammma)+betta*thetaw*(PI^chiw/PI(+1))^(-eta*(1+gammma))*(wstar(+1)/wstar*mu_z(+1))^(eta*(1+gammma))*f(+1);
 
-//8-10. firm's price setting
+[name='Firm price setting 1']
 g1=lambda*mc*yd+betta*thetap*(PI^chi/PI(+1))^(-epsilon)*g1(+1);
+[name='Firm price setting 2']
 g2=lambda*PIstar*yd+betta*thetap*(PI^chi/PI(+1))^(1-epsilon)*PIstar/PIstar(+1)*g2(+1);
+[name='Firm price setting 3']
 epsilon*g1=(epsilon-1)*g2;
-//11-12. optimal inputs
+[name='Optimal capital labor ratio']
 u*k/ld=alppha/(1-alppha)*w/r*mu_z*mu_I;
+[name='Marginal costs']
 mc=(1/(1-alppha))^(1-alppha)*(1/alppha)^alppha*w^(1-alppha)*r^alppha;
-//13. law of motion wages
+[name='law of motion wages']
 1=thetaw*(PI(-1)^chiw/PI)^(1-eta)*(w(-1)/w*mu_z^(-1))^(1-eta)+(1-thetaw)*PIstarw^(1-eta);
-//14. law of motion prices
+[name='law of motion prices']
 1=thetap*(PI(-1)^chi/PI)^(1-epsilon)+(1-thetap)*PIstar^(1-epsilon);
 
-//15. Taylor Rule
-R/Rbar=(R(-1)/Rbar)^gammmaR*((PI/PIbar)^gammmaPI*((yd/yd(-1)*mu_z)/exp(LambdaYd))^gammmay)^(1-gammmaR)*exp(epsm);
+[name='Taylor Rule']
+R/Rbar=(R(-1)/Rbar)^gammmaR*((PI/PIbar)^gammmaPI*((yd/yd(-1)*mu_z)/exp(LambdaYd))^gammmay)^(1-gammmaR)*exp(sigma_m*epsm);
 
-//16-17. Market clearing
+[name='Resource constraint']
 yd=c+x+mu_z^(-1)*mu_I^(-1)*(gammma1*(u-1)+gammma2/2*(u-1)^2)*k;
+[name='Aggregate production']
 yd=(mu_A*mu_z^(-1)*(u*k)^alppha*ld^(1-alppha)-Phi)/vp;
-//18-20. Price and wage dispersion terms
+[name='Aggregate labor market']
 l=vw*ld;
+[name='LOM Price dispersion term']
 vp=thetap*(PI(-1)^chi/PI)^(-epsilon)*vp(-1)+(1-thetap)*PIstar^(-epsilon);
+[name='LOM Wage dispersion term']
 vw=thetaw*(w(-1)/w*mu_z^(-1)*PI(-1)^chiw/PI)^(-eta)*vw(-1)+(1-thetaw)*(PIstarw)^(-eta);
-//21. Law of motion for capital
+[name='Law of motion for capital']
 k(+1)*mu_z*mu_I-(1-delta)*k-mu_z*mu_I*(1-kappa/2*(x/x(-1)*mu_z-Lambdax)^2)*x=0;
-//22. Profits
+[name='Profits']
 F=yd-1/(1-alppha)*w*ld;
-//23. definition optimal wage inflation
+[name='definition optimal wage inflation']
 PIstarw=wstar/w;
 
 //exogenous processes
-//24. Preference Shock
-log(d)=rhod*log(d(-1))+epsd;
-//25. Labor disutility Shock
-log(phi)=rhophi*log(phi(-1))+epsphi;
-//26. Investment specific technology
-log(mu_I)=Lambdamu+epsmu_I;
-//27. Neutral technology
-log(mu_A)=LambdaA+epsA; 
-//28. Defininition composite technology
+[name='Preference Shock']
+log(d)=rhod*log(d(-1))+sigma_d*epsd;
+[name='Labor disutility Shock']
+log(phi)=rhophi*log(phi(-1))+sigma_phi*epsphi;
+[name='Investment specific technology']
+log(mu_I)=Lambdamu+sigma_mu*epsmu_I;
+[name='Neutral technology']
+log(mu_A)=LambdaA+sigma_A*epsA;
+[name='Defininition composite technology']
 mu_z=mu_A^(1/(1-alppha))*mu_I^(alppha/(1-alppha));
 
 end;
 
 shocks;
-var epsd; stderr exp(sigma_d);
-var epsphi; stderr exp(sigma_phi);
-var epsmu_I; stderr exp(sigma_mu);
-var epsA; stderr exp(sigma_A);
-var epsm; stderr exp(sigma_m);
+var epsd; stderr 1;
+var epsphi; stderr 1;
+var epsmu_I; stderr 1;
+var epsA; stderr 1;
+var epsm; stderr 1;
 end;
 
 steady;

examples/NK_baseline_steadystate.m

-function [ys,check] = NK_baseline_steadystate(ys,exo)
-% function [ys,check] = NK_baseline_steadystate(ys,exo)
+function [ys,params,check] = NK_baseline_steadystate(ys,exo,M_,options_)
+% function [ys,params,check] = NK_baseline_steadystate(ys,exo,M_,options_)
 % computes the steady state for the NK_baseline.mod and uses a numerical
 % solver to do so
 % Inputs:
 %   - ys        [vector] vector of initial values for the steady state of
 %                   the endogenous variables
 %   - exo       [vector] vector of values for the exogenous variables
+%   - M_        [structure] Dynare model structure
+%   - options   [structure] Dynare options structure
 %
 % Output:
-%   - ys        [vector] vector of steady state values fpr the the endogenous variables
+%   - ys        [vector] vector of steady state values for the the endogenous variables
+%   - params    [vector] vector of parameter values
 %   - check     [scalar] set to 0 if steady state computation worked and to
-%                    1 of not (allows to impos restriction on parameters)
+%                    1 of not (allows to impose restrictions on parameters)
 
-global M_ 
+% Copyright © 2013-2024 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
+
+% make parameter explicitly known to Matlab to avoid naming conflicts; actual value is read out
+% in next eval-loop 
+delta=0;
 
 % read out parameters to access them with their name
 NumberOfParameters = M_.param_nbr;
@@ -28,14 +50,12 @@ check = 0;
 
 options=optimset(); % set options for numerical solver
 
-% the steady state computation follows FV (2006), section 4.1
+% the steady state computation follows FVRR (2006), section 4.1
 PI=PIbar;
 u=1;
 q=1;
 d=1;
 phi=1;
-m=0;
-zeta=1;
 LambdaYd= (LambdaA+alppha*Lambdamu)/(1-alppha);
 mu_z=exp(LambdaYd);
 mu_I=exp(Lambdamu);
@@ -47,6 +67,7 @@ Lambdax=mu_z;
 %set the parameter gammma1
 gammma1=mu_z*mu_I/betta-(1-delta);
 if gammma1<0 % parameter violates restriction; Preventing this cannot be implemented via prior restriction as it is a composite of different parameters and the valid prior region has unknown form
+    params=M_.params;
     check=1; %set failure indicator
     return; %return without updating steady states
 end
@@ -68,13 +89,20 @@ vp=(1-thetap)/(1-thetap*PI^((1-chi)*epsilon))*PIstar^(-epsilon);
 vw=(1-thetaw)/(1-thetaw*PI^((1-chiw)*eta)*mu_z^eta)*PIstarw^(-eta);
 tempvaromega=alppha/(1-alppha)*w/r*mu_z*mu_I;
 
+try
+    %proper error handling for cases for infeasible initial value, which would result in error instead of valid exitflag
     [ld,fval,exitflag]=fzero(@(ld)(1-betta*thetaw*mu_z^(eta-1)*PI^(-(1-chiw)*(1-eta)))/(1-betta*thetaw*mu_z^(eta*(1+gammma))*PI^(eta*(1-chiw)*(1+gammma)))...
         -(eta-1)/eta*wstar/(varpsi*PIstarw^(-eta*gammma)*ld^gammma)*((1-h*mu_z^(-1))^(-1)-betta*h*(mu_z-h)^(-1))*...
         ((mu_A*mu_z^(-1)*vp^(-1)*tempvaromega^alppha-tempvaromega*(1-(1-delta)*(mu_z*mu_I)^(-1)))*ld-vp^(-1)*Phi)^(-1),0.25,options);
+catch
+    exitflag = 0;
+end
+
 if exitflag <1
     %indicate the SS computation was not sucessful; this would also be detected by Dynare
     %setting the indicator here shows how to use this functionality to
     %filter out parameter draws
+    params=M_.params;
     check=1; %set failure indicator
     return; %return without updating steady states
 end
@@ -95,8 +123,9 @@ g2=epsilon/(epsilon-1)*g1;
 
 %% end own model equations
 
+params=NaN(NumberOfParameters,1);
 for iter = 1:length(M_.params) %update parameters set in the file
-  eval([ 'M_.params(' num2str(iter) ') = ' M_.param_names{iter} ';' ])
+    eval([ 'params(' num2str(iter) ') = ' M_.param_names{iter} ';' ])
 end
 
 NumberOfEndogenousVariables = M_.orig_endo_nbr; %auxiliary variables are set automatically

examples/Occbin_example.mod

+/*
+ * This file shows how to solve an RBC model with two occasionally binding constraints:
+ *  1. The INEG constraint implements quadratic capital adjustment costs if investment
+ *      falls below its steady state. If investment is above steady state, there are no
+ *      adjustment costs
+ *  2. The IRR constraint implements irreversible investment. Investment cannot be lower
+ *      than a factor phi of its steady state.
+ *
+ * Notes:
+ *  - This mod-file is based on an example originally provided by Luca Guerrieri
+ *      and Matteo Iacoviello provided at https://www.matteoiacoviello.com/research_files/occbin_20140630.zip
+ *  - The INEG constraint should theoretically be log_Invest-log(steady_state(Invest))<0, but this will lead
+ *      to numerical issues. Instead we allow for a small negative value of <-0.000001
+ *
+ * Please note that the following copyright notice only applies to this Dynare
+ * implementation of the model.
+ */
+
+/*
+ * Copyright © 2021 Dynare Team
+ *
+ * This is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * It is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * For A copy of the GNU General Public License,
+ * see <https://www.gnu.org/licenses/>.
+ */
+
+
+var A           $A$         (long_name='TFP')
+    C           $C$         (long_name='consumption')
+    Invest      $I$         (long_name='investment')
+    K           $K$         (long_name='capital')
+    Lambda      $\lambda$   (long_name='Lagrange multiplier')
+    log_K       ${\hat K}$  (long_name='log capital')
+    log_Invest  ${\hat I}$  (long_name='log investment')
+    log_C       ${\hat C}$  (long_name='log consumption')
+    ;
+
+varexo epsilon $\varepsilon$        (long_name='TFP shock');
+
+parameters alpha    $\alpha$        (long_name='capital share')
+        delta       $\delta$        (long_name='depreciation')
+        beta        $\beta$         (long_name='discount factor')
+        sigma       $\sigma$        (long_name='risk aversion')
+        rho         $\rho$          (long_name='autocorrelation TFP')
+        phi         $\phi$          (long_name='irreversibility fraction of steady state investment')
+        psi         $\psi$          (long_name='capital adjustment cost')
+        ;
+
+beta=0.96;
+alpha=0.33;
+delta=0.10;
+sigma=2;
+rho = 0.9;
+phi = 0.975;
+psi = 5;
+
+
+model;
+// 1.
+[name='Euler', bind = 'INEG']
+-C^(-sigma)*(1+2*psi*(K/K(-1)-1)/K(-1))+ beta*C(+1)^(-sigma)*((1-delta)-2*psi*(K(+1)/K-1)*
+  (-K(+1)/K^2)+alpha*exp(A(+1))*K^(alpha-1))= -Lambda+beta*(1-delta)*Lambda(+1);
+
+[name='Euler', relax = 'INEG']
+-C^(-sigma) + beta*C(+1)^(-sigma)*(1-delta+alpha*exp(A(+1))*K^(alpha-1))= -Lambda+beta*(1-delta)*Lambda(+1);
+
+// 2.
+[name='Budget constraint',bind = 'INEG']
+C+K-(1-delta)*K(-1)+psi*(K/K(-1)-1)^2=exp(A)*K(-1)^(alpha);
+
+[name='Budget constraint',relax = 'INEG']
+C+K-(1-delta)*K(-1)=exp(A)*K(-1)^(alpha);
+
+// 3.
+[name='LOM capital']
+Invest = K-(1-delta)*K(-1);
+
+// 4.
+[name='investment',bind='IRR,INEG']
+(log_Invest - log(phi*steady_state(Invest))) = 0;
+[name='investment',relax='IRR']
+Lambda=0;
+[name='investment',bind='IRR',relax='INEG']
+(log_Invest - log(phi*steady_state(Invest))) = 0;
+
+// 5.
+[name='LOM TFP']
+A = rho*A(-1)+epsilon;
+
+// Definitions
+[name='Definition log capital']
+log_K=log(K);
+[name='Definition log consumption']
+log_C=log(C);
+[name='Definition log investment']
+log_Invest=log(Invest);
+end;
+
+occbin_constraints;
+name 'IRR'; bind log_Invest-log(steady_state(Invest))<log(phi); relax Lambda<0;
+name 'INEG'; bind log_Invest-log(steady_state(Invest))<-0.000001; %not exactly 0 for numerical reasons
+end;
+
+steady_state_model;
+K = ((1/beta-1+delta)/alpha)^(1/(alpha-1));
+C = -delta*K +K^alpha;
+Invest = delta*K;
+log_K = log(K);
+log_C = log(C);
+log_Invest = log(Invest);
+Lambda = 0;
+A=0;
+end;
+
+shocks;
+  var epsilon; stderr 0.015;
+end;
+
+steady;
+
+shocks(surprise);
+var epsilon;
+periods 1:9, 10, 50, 90, 130, 131:169;
+values -0.0001, -0.01,-0.02, 0.01, 0.02, 0;
+end;
+
+occbin_setup;
+occbin_solver(simul_periods=200,simul_check_ahead_periods=200);
+
+occbin_graph log_C epsilon Lambda log_K log_Invest A;

examples/Ramsey_Example.mod

+/*
+ * This file replicates the model studied in:
+ * Lawrence J. Christiano, Roberto Motto and Massimo Rostagno (2007):
+ * "Notes on Ramsey-Optimal Monetary Policy", Section 2
+ * The paper is available at http://faculty.wcas.northwestern.edu/~lchrist/d16/d1606/ramsey.pdf
+ * 
+ * Notes:
+ * - This mod-files allows to simulate a simple New Keynesian Model with Rotemberg price 
+ *      adjustment costs under three different monetary policy arrangements:
+ *      1. a Taylor rule with a fixed inflation feedback coefficient alpha
+ *          -> set the Optimal_policy switch to 0
+ *      2. a Taylor rule where the inflation feedback coefficient alpha is chosen 
+ *          optimally to minimize a quadratic loss function (optimal simple rule (OSR))
+ *          -> set the Optimal_policy switch to 1 and the Ramsey switch to 0
+ *      3. fully optimal monetary under commitment (Ramsey)
+ *          -> set the Optimal_policy switch to 1 and the Ramsey switch to 1
+ *
+ *  - The Efficent_steady_state switch can be used to switch from an distorted steady state 
+ *      due to a monopolistic distortion to one where a labor subsidy counteracts this 
+ *      distortion. Note that the purely quadratic loss function in the OSR case does not capture
+ *      the full welfare losses with a distorted steady state as there would be a linear term 
+ *      appearing.
+ *
+ *  - This files shows how to use a conditional steady state file in the Ramsey case. It takes
+ *      the value of the defined instrument R as given and then computes the rest of the steady 
+ *      state, including the steady state inflation rate, based on this value. The initial value 
+ *      of the instrument for steady state search must then be defined in an initval-block.
+ *
+ *  - The optim_weights in the OSR case are based on a second order approximation to the welfare function
+ *      as in Gali (2015). The relative weight between inflation and output gap volatility is essentially 
+ *      given by the slope of the New Keynesian Phillips Curve. Note that the linear terms that would be 
+ *      present in case of a distorted steady state need to be dropped for OSR. 
+ *
+ *  - Due to divine coincidence, the first best policy involves fully stabilizing inflation  
+ *      and thereby the output gap. As a consequence, the optimal inflation feedback coefficient 
+ *      in a Taylor rule would be infinity. The OSR command therefore estimates it to be at the 
+ *      upper bound defined via osr_params_bounds.
+ *
+ *  - The mod-file also allows to conduct estimation under Ramsey policy by setting the 
+ *      Estimation_under_Ramsey switch to 1.
+ *
+ * This implementation was written by Johannes Pfeifer.
+ *
+ * If you spot mistakes, email me at jpfeifer@gmx.de
+ *
+ * Please note that the following copyright notice only applies to this Dynare 
+ * implementation of the model.
+ */
+
+/*
+ * Copyright © 2019 Dynare Team
+ *
+ * This is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * It is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * For a copy of the GNU General Public License,
+ * see <https://www.gnu.org/licenses/>.
+ */
+
+//**********Define which monetary policy setup to use *********** 
+
+@#ifndef Optimal_policy
+    @#define Optimal_policy=1
+    @#ifndef Ramsey
+        @#define Ramsey=1
+    @#endif    
+@#endif
+
+//**********Define whether to use distorted steady state*********** 
+
+@#ifndef Efficent_steady_state
+    @#define Efficent_steady_state=0
+@#endif
+
+@#ifndef Estimation_under_Ramsey
+    @#define Estimation_under_Ramsey=0
+@#endif
+
+var C       $C$                 (long_name='Consumption')
+    pi      $\pi$               (long_name='Gross inflation')
+    h       $h$                 (long_name='hours worked')
+    Z       $Z$                 (long_name='TFP')
+    R       $R$                 (long_name='Net nominal interest rate')
+    log_C   ${\ln C}$           (long_name='Log Consumption')
+    log_h   ${\ln h}$           (long_name='Log hours worked')
+    pi_ann  ${\pi^{ann}}$       (long_name='Annualized net inflation')
+    R_ann   ${R^{ann}}$         (long_name='Annualized net nominal interest rate')
+    r_real  ${r^{ann,real}}$    (long_name='Annualized net real interest rate')
+    y_nat   ${y^{nat}}$         (long_name='Natural (flex price) output')
+    y_gap   ${r^{gap}}$         (long_name='Output gap')
+    @#if Optimal_policy==0
+        Welfare ${W}$           (long_name='Welfare')
+    @#endif
+;
+
+varexo epsilon ${\varepsilon}$     (long_name='TFP shock')
+    ;
+
+parameters beta     ${\beta}$       (long_name='discount factor')
+        theta       ${\theta}$      (long_name='substitution elasticity')
+        tau         ${\tau}$        (long_name='labor subsidy')
+        chi         ${\chi}$        (long_name='labor disutility')
+        phi         ${\phi}$        (long_name='price adjustment costs')
+        rho         ${\rho}$        (long_name='TFP autocorrelation')
+        @# if !defined(Ramsey) || Ramsey==0
+            pi_star     ${\pi^*}$   (long_name='steady state inflation')
+            alpha       ${\alpha}$  (long_name='inflation feedback Taylor rule')
+        @# endif
+        ;
+
+beta=0.99;
+theta=5;
+phi=100;
+rho=0.9;
+@# if !defined(Ramsey) || Ramsey==0
+    alpha=1.5;
+    pi_star=1;
+@# endif
+@# if Efficent_steady_state
+    tau=1/(theta-1);
+@# else
+    tau=0;
+@# endif
+chi=1;
+        
+model;
+    [name='Euler equation']
+    1/(1+R)=beta*C/(C(+1)*pi(+1));
+    [name='Firm FOC']
+    (tau-1/(theta-1))*(1-theta)+theta*(chi*h*C/(exp(Z))-1)=phi*(pi-1)*pi-beta*phi*(pi(+1)-1)*pi(+1);
+    [name='Resource constraint']
+    C*(1+phi/2*(pi-1)^2)=exp(Z)*h;
+    [name='TFP process']
+    Z=rho*Z(-1)+epsilon;
+    @#if !defined(Ramsey) || Ramsey==0
+        [name='Taylor rule']
+        R=pi_star/beta-1+alpha*(pi-pi_star);
+    @#endif
+    [name='Definition log consumption']
+    log_C=log(C);
+    [name='Definition log hours worked']
+    log_h=log(h);
+    [name='Definition annualized inflation rate']
+    pi_ann=4*log(pi);
+    [name='Definition annualized nominal interest rate']
+    R_ann=4*R;
+    [name='Definition annualized real interest rate']
+    r_real=4*log((1+R)/pi(+1));
+    [name='Definition natural output']
+    y_nat=exp(Z)*sqrt((theta-1)/theta*(1+tau)/chi);
+    [name='output gap']
+    y_gap=log_C-log(y_nat);
+    @#if Optimal_policy==0
+        [name='Definition Welfare']
+        Welfare=log(C)-chi/2*h^2+beta*Welfare(+1);
+        @#endif
+end;
+
+steady_state_model;
+    Z=0;
+    @# if !defined(Ramsey) || Ramsey==0
+        R=pi_star/beta-1; %only set this if not conditional steady state file for Ramsey
+    @# endif
+    pi=(R+1)*beta;
+    C=sqrt((1+1/theta*((1-beta)*(pi-1)*pi-(tau-1/(theta-1))*(1-theta)))/(chi*(1+phi/2*(pi-1)^2)));
+    h=C*(1+phi/2*(pi-1)^2);
+    log_C=log(C);
+    log_h=log(h);
+    pi_ann=4*log(pi);
+    R_ann=4*R;
+    r_real=4*log((1+R)/pi);
+    y_nat=sqrt((theta-1)/theta*(1+tau)/chi);
+    y_gap=log_C-log(y_nat);
+    @#if Optimal_policy==0
+        Welfare=1/(1-beta)*(log(C)-chi/2*h^2);
+    @#endif
+
+end;
+
+@# if defined(Ramsey) && Ramsey==1
+    //define initial value of instrument for Ramsey
+    initval;
+        R=1/beta-1;
+    end;
+@# endif
+
+shocks;
+    var epsilon = 0.01^2;
+end;
+
+@#if Optimal_policy==0
+    //use Taylor rule
+    stoch_simul(order=2) pi_ann log_h R_ann log_C Z r_real y_nat Welfare;
+@#else
+    @# if !defined(Ramsey) || Ramsey==0
+        //use OSR Taylor rule
+        
+        //set weights on (co-)variances for OSR
+        optim_weights;
+            pi theta/((theta-1)/phi);
+            y_gap 1;
+        end;
+
+        //define OSR parameters to be optimized
+        osr_params alpha;
+
+        //starting value for OSR parameter
+        alpha = 1.5;
+
+        //define bounds for OSR during optimization
+        osr_params_bounds;
+            alpha, 0, 100;
+        end;
+
+        //compute OSR and provide output
+        osr(opt_algo=9) pi_ann log_h R_ann log_C Z r_real;
+
+    @# else
+        //use Ramsey optimal policy
+        
+        //define planner objective, which corresponds to utility function of agents
+        planner_objective log(C)-chi/2*h^2;
+        
+        //set up Ramsey optimal policy problem with interest rate R as the instrument,...
+        // defining the discount factor in the planner objective to be the one of private agents        
+        ramsey_model(instruments=(R),planner_discount=beta,planner_discount_latex_name=$\beta$); 
+        
+        //conduct stochastic simulations of the Ramsey problem
+        stoch_simul(order=1,irf=20,periods=500) pi_ann log_h R_ann log_C Z r_real;
+        evaluate_planner_objective;
+        
+        @# if Estimation_under_Ramsey==1
+            datatomfile('ramsey_simulation',{'log_C'})
+        
+            estimated_params;
+                rho,0.5,uniform_pdf, , ,0,1;
+            end;
+            varobs log_C;
+                
+            estimation(datafile=ramsey_simulation,mode_compute=5);
+        @# endif        
+    @# endif
+@# endif
\ No newline at end of file

examples/Ramsey_steady_file.mod

+/*
+ * This file replicates the model studied in:
+ * Lawrence J. Christiano, Roberto Motto and Massimo Rostagno (2007):
+ * "Notes on Ramsey-Optimal Monetary Policy", Section 2
+ * The paper is available at http://faculty.wcas.northwestern.edu/~lchrist/d16/d1606/ramsey.pdf
+ * 
+ * Notes:
+ * - This mod-files allows to simulate a simple New Keynesian Model with Rotemberg price 
+ *      adjustment costs under fully optimal monetary under commitment (Ramsey)
+ *
+ *  - This files shows how to use a user-defined conditional steady state file in the Ramsey case. It takes
+ *      the value of the defined instrument R as given and then computes the rest of the steady 
+ *      state, including the steady state inflation rate, based on this value. The initial value 
+ *      of the instrument for steady state search must then be defined in an initval-block.
+ *
+ * This implementation was written by Johannes Pfeifer.
+ *
+ * If you spot mistakes, email me at jpfeifer@gmx.de
+ *
+ * Please note that the following copyright notice only applies to this Dynare 
+ * implementation of the model.
+ */
+
+/*
+ * Copyright © 2019-2022 Dynare Team
+ *
+ * This is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * It is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * For a copy of the GNU General Public License,
+ * see <https://www.gnu.org/licenses/>.
+ */
+
+
+var C       $C$                 (long_name='Consumption')
+    pi      $\pi$               (long_name='Gross inflation')
+    h       $h$                 (long_name='hours worked')
+    Z       $Z$                 (long_name='TFP')
+    R       $R$                 (long_name='Net nominal interest rate')
+    log_C   ${\ln C}$           (long_name='Log Consumption')
+    log_h   ${\ln h}$           (long_name='Log hours worked')
+    pi_ann  ${\pi^{ann}}$       (long_name='Annualized net inflation')
+    R_ann   ${R^{ann}}$         (long_name='Annualized net nominal interest rate')
+    r_real  ${r^{ann,real}}$    (long_name='Annualized net real interest rate')
+    y_nat   ${y^{nat}}$         (long_name='Natural (flex price) output')
+    y_gap   ${r^{gap}}$         (long_name='Output gap')
+;
+
+varexo epsilon ${\varepsilon}$     (long_name='TFP shock')
+    ;
+
+parameters beta     ${\beta}$       (long_name='discount factor')
+        theta       ${\theta}$      (long_name='substitution elasticity')
+        tau         ${\tau}$        (long_name='labor subsidy')
+        chi         ${\chi}$        (long_name='labor disutility')
+        phi         ${\phi}$        (long_name='price adjustment costs')
+        rho         ${\rho}$        (long_name='TFP autocorrelation')
+        ;
+
+beta=0.99;
+theta=5;
+phi=100;
+rho=0.9;
+tau=0;
+chi=1;
+        
+model;
+    [name='Euler equation']
+    1/(1+R)=beta*C/(C(+1)*pi(+1));
+    [name='Firm FOC']
+    (tau-1/(theta-1))*(1-theta)+theta*(chi*h*C/(exp(Z))-1)=phi*(pi-1)*pi-beta*phi*(pi(+1)-1)*pi(+1);
+    [name='Resource constraint']
+    C*(1+phi/2*(pi-1)^2)=exp(Z)*h;
+    [name='TFP process']
+    Z=rho*Z(-1)+epsilon;
+    [name='Definition log consumption']
+    log_C=log(C);
+    [name='Definition log hours worked']
+    log_h=log(h);
+    [name='Definition annualized inflation rate']
+    pi_ann=4*log(pi);
+    [name='Definition annualized nominal interest rate']
+    R_ann=4*R;
+    [name='Definition annualized real interest rate']
+    r_real=4*log((1+R)/pi(+1));
+    [name='Definition natural output']
+    y_nat=exp(Z)*sqrt((theta-1)/theta*(1+tau)/chi);
+    [name='output gap']
+    y_gap=log_C-log(y_nat);
+end;
+
+initval;
+    R=1/beta-1;
+end;
+
+shocks;
+    var epsilon = 0.01^2;
+end;
+
+//use Ramsey optimal policy
+
+//define planner objective, which corresponds to utility function of agents
+planner_objective log(C)-chi/2*h^2;
+
+//set up Ramsey optimal policy problem with interest rate R as the instrument,...
+// defining the discount factor in the planner objective to be the one of private agents        
+ramsey_model(instruments=(R),planner_discount=beta,planner_discount_latex_name=$\beta$); 
+
+//conduct stochastic simulations of the Ramsey problem
+stoch_simul(order=1,irf=20,periods=500) pi_ann log_h R_ann log_C Z r_real;
+evaluate_planner_objective;
+

examples/Ramsey_steady_file_steadystate.m

+function [ys,params,check] = Ramsey_steady_file_steadystate(ys,exo,M_,options_)
+% function [ys,params,check] = Ramsey_steady_file_steadystate(ys,exo,M_,options_)
+% computes the steady state for the Ramsey_steady_file.mod, conditional on
+% the instrument value provided
+%
+% Inputs: 
+%   - ys        [vector] vector of initial values for the steady state of
+%                   the endogenous variables
+%   - exo       [vector] vector of values for the exogenous variables
+%   - M_        [structure] Dynare model structure
+%   - options   [structure] Dynare options structure
+%
+% Output: 
+%   - ys        [vector] vector of steady state values for the the endogenous variables
+%   - params    [vector] vector of parameter values
+%   - check     [scalar] set to 0 if steady state computation worked and to
+%                    1 of not (allows to impose restrictions on parameters)
+
+% Copyright © 2020 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
+
+% read out parameters to access them with their name
+beta=NaN; %make parameter known to Matlab function, prevents crashes due to Matlab function with same name;
+          %will be overwritten next
+          
+NumberOfParameters = M_.param_nbr;
+for ii = 1:NumberOfParameters
+  paramname = M_.param_names{ii};
+  eval([ paramname ' = M_.params(' int2str(ii) ');']);
+end
+
+% read in instrument values
+for ii = 1:size(options_.instruments,1)
+  eval([options_.instruments{ii} ' = ys(strmatch(options_.instruments{ii},M_.endo_names,''exact'')) ;']);
+end
+% initialize indicator
+check = 0;
+
+
+%% Enter model equations here
+
+    Z=0;
+    pi=(R+1)*beta;
+    C=sqrt((1+1/theta*((1-beta)*(pi-1)*pi-(tau-1/(theta-1))*(1-theta)))/(chi*(1+phi/2*(pi-1)^2)));
+    h=C*(1+phi/2*(pi-1)^2);
+    log_C=log(C);
+    log_h=log(h);
+    pi_ann=4*log(pi);
+    R_ann=4*R;
+    r_real=4*log((1+R)/pi);
+    y_nat=sqrt((theta-1)/theta*(1+tau)/chi);
+    y_gap=log_C-log(y_nat);
+
+%% end own model equations
+
+params=NaN(NumberOfParameters,1);
+for iter = 1:length(M_.params) %update parameters set in the file
+  eval([ 'params(' num2str(iter) ') = ' M_.param_names{iter} ';' ])
+end
+
+NumberOfEndogenousVariables = M_.orig_endo_nbr; %auxiliary variables are set automatically
+for ii = 1:NumberOfEndogenousVariables
+  varname = M_.endo_names{ii};
+  eval(['ys(' int2str(ii) ') = ' varname ';']);
+end

examples/agtrend.mod

 /*
  * This file replicates the model studied in:
- * Aguiar, Mark and Gopinath, Gita (2004): "Emerging Market Business Cycles:
- * The Cycle is the Trend" (NBER WP 10734). It is different from version published 
+ * Aguiar, Mark and Gopinath, Gita (2004): “Emerging Market Business Cycles:
+ * The Cycle is the Trend” (NBER WP 10734). It is different from version published 
  * in the Journal of Political Economy.
  * 
  * This model file is intended to show the capabilities of the Dynare macro 
@@ -15,13 +15,13 @@
  * The mod-file reproduces Figure 4 of the working paper, which displays the
  * model response to 1 percent shock to trend and cyclical TFP.
  *
- * This implementation was written by Sbastien Villemot and Johannes Pfeifer. 
+ * This implementation was written by Sébastien Villemot and Johannes Pfeifer. 
  * Please note that the following copyright notice only applies to this Dynare 
  * implementation of the model.
  */
 
 /*
- * Copyright (C) 2012-13 Dynare Team
+ * Copyright © 2012-13 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -36,7 +36,7 @@
  * GNU General Public License for more details.
  *
  * You should have received a copy of the GNU General Public License
- * along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+ * along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
  */
 
 

examples/bkk.mod

@@ -27,7 +27,7 @@
  */
 
 /*
- * Copyright (C) 2010 Dynare Team
+ * Copyright © 2010 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -42,7 +42,7 @@
  * GNU General Public License for more details.
  *
  * You should have received a copy of the GNU General Public License
- * along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+ * along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
  */
 
 @#define countries = [ "H", "F" ]

examples/dynare++/README.txt

-Assuming that the dynare++ binary is in your PATH, you can run the example by using the following command
-in a Command Prompt Window:
-
-... > dynare++ example1.mod
-
-Please, read the manual (doc\dynare++\dynare++-tutorial.pdf) for a description of the generated output.

examples/dynare++/example1.mod

-/*
- * This Dynare++ mod-file implements the RBC model with time-to-build
- * described in Kamenik (2011): "DSGE Models with Dynare++. A Tutorial."
- * Note that Dynare++ uses the same stock-at-the-end-of-period timing convention
- * as the regular Dynare
-*/
-
-var Y, C, K, A, H, B;
-
-varexo EPS, NU;
-
-parameters beta, rho, alpha, delta, theta, psi, tau;
-
-alpha = 0.36;
-rho = 0.95;
-tau = 0.025;
-beta = 1/(1.03^0.25);
-delta = 0.025;
-psi = 0;
-theta = 2.95;
-
-model;
-  C*theta*H^(1+psi) = (1-alpha)*Y;
-  beta*exp(B)*C/exp(B(1))/C(1)*
-  (exp(B(1))*alpha*Y(1)/K(1)+1-delta) = 1;
-  Y = exp(A)*K^alpha*H^(1-alpha);
-  K = exp(B(-1))*(Y(-1)-C(-1)) + (1-delta)*K(-1);
-  A = rho*A(-1) + tau*B(-1) + EPS;
-  B = tau*A(-1) + rho*B(-1) + NU;
-end;
-
-initval;
-  A = 0;
-  B = 0;
-  H = ((1-alpha)/(theta*(1-(delta*alpha)/(1/beta-1+delta))))^(1/(1+psi));
-  Y = (alpha/(1/beta-1+delta))^(alpha/(1-alpha))*H;
-  K = alpha/(1/beta-1+delta)*Y;
-  C = Y - delta*K;
-end;
-
-vcov = [0.0002 0.00005;
-0.00005 0.0001
-];
-
-order = 7;
\ No newline at end of file

examples/example1.mod

@@ -4,7 +4,7 @@
  */
 
 /*
- * Copyright (C) 2001-2010 Dynare Team
+ * Copyright © 2001-2010 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -19,7 +19,7 @@
  * GNU General Public License for more details.
  *
  * You should have received a copy of the GNU General Public License
- * along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+ * along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
  */
 
 

examples/example1_reporting.mod

@@ -4,7 +4,7 @@
  */
 
 /*
- * Copyright (C) 2001-2015 Dynare Team
+ * Copyright © 2001-2015 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -19,7 +19,7 @@
  * GNU General Public License for more details.
  *
  * You should have received a copy of the GNU General Public License
- * along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+ * along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
  */
 
 

examples/example2.mod

@@ -4,7 +4,7 @@
  */
 
 /*
- * Copyright (C) 2001-2010 Dynare Team
+ * Copyright © 2001-2010 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -19,7 +19,7 @@
  * GNU General Public License for more details.
  *
  * You should have received a copy of the GNU General Public License
- * along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+ * along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
  */
 
 

examples/example3.mod

@@ -6,7 +6,7 @@
  * To do so, the equations of the model have been transformed into a non-linear equation in 
  * labor h. Within the steady_state_model-block, a helper function is called that uses fsolve
  * to solve this non-linear equation. The use of the helper function is necessary to avoid 
- * interference of the Matlab syntax with Dynare's preprocessor. A more complicated alternative 
+ * interference of the MATLAB syntax with Dynare's preprocessor. A more complicated alternative 
  * that provides more flexibility in the type of commands executed and functions called is the use 
  * of an explicit steady state file. See the NK_baseline.mod in the Examples Folder.
  * 
@@ -16,7 +16,7 @@
  */
 
 /*
- * Copyright (C) 2013 Dynare Team
+ * Copyright © 2013 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -31,7 +31,7 @@
  * GNU General Public License for more details.
  *
  * You should have received a copy of the GNU General Public License
- * along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+ * along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
  */
 
 

examples/fs2000.mod

@@ -3,20 +3,20 @@
  * in the paper) described in Frank Schorfheide (2000): "Loss function-based
  * evaluation of DSGE models", Journal of Applied Econometrics, 15(6), 645-670.
  *
- * The data are in file "fsdat_simul.m", and have been artificially generated.
- * They are therefore different from the original dataset used by Schorfheide.
+ * The data are taken from the replication package at
+ * http://dx.doi.org/10.15456/jae.2022314.0708799949
  *
  * The prior distribution follows the one originally specified in Schorfheide's
- * paper, except for parameter rho. In the paper, the elicited beta prior for rho
+ * paper. Note that the elicited beta prior for rho in the paper
  * implies an asymptote and corresponding prior mode at 0. It is generally
- * recommended to avoid this extreme type of prior. Some optimizers, for instance
- * mode_compute=12 (Mathworks' particleswarm algorithm) may find a posterior mode
- * with rho equal to zero. We lowered the value of the prior standard deviation
- * (changing .223 to .100) to remove the asymptote.
+ * recommended to avoid this extreme type of prior.
+ *
+ * Because the data are already logged and we use the loglinear option to conduct
+ * a full log-linearization, we need to use the logdata option.
  *
  * The equations are taken from J. Nason and T. Cogley (1994): "Testing the
  * implications of long-run neutrality for monetary business cycle models",
- * Journal of Applied Econometrics, 9, S37-S70.
+ * Journal of Applied Econometrics, 9, S37-S70, NC in the following.
  * Note that there is an initial minus sign missing in equation (A1), p. S63.
  *
  * This implementation was originally written by Michel Juillard. Please note that the
@@ -25,7 +25,7 @@
  */
 
 /*
- * Copyright (C) 2004-2017 Dynare Team
+ * Copyright © 2004-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -40,36 +40,74 @@
  * GNU General Public License for more details.
  *
  * You should have received a copy of the GNU General Public License
- * along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+ * along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
  */
 
-var m P c e W R k d n l gy_obs gp_obs y dA;
-varexo e_a e_m;
-
-parameters alp bet gam mst rho psi del;
-
+var m       ${m}$           (long_name='money growth')
+    P       ${P}$           (long_name='Price level')
+    c       ${c}$           (long_name='consumption')
+    e       ${e}$           (long_name='capital stock')
+    W       ${W}$           (long_name='Wage rate')
+    R       ${R}$           (long_name='interest rate')
+    k       ${k}$           (long_name='capital stock')
+    d       ${d}$           (long_name='dividends')
+    n       ${n}$           (long_name='labor')
+    l       ${l}$           (long_name='loans')
+    gy_obs  ${\Delta \ln GDP}$  (long_name='detrended capital stock')
+    gp_obs  ${\Delta \ln P}$    (long_name='detrended capital stock')
+    y       ${y}$           (long_name='detrended output')
+    dA      ${\Delta A}$    (long_name='TFP growth')
+    ;
+varexo e_a  ${\epsilon_A}$      (long_name='TFP shock')
+    e_m     ${\epsilon_M}$      (long_name='Money growth shock')
+    ;
+
+parameters alp  ${\alpha}$       (long_name='capital share')
+    bet         ${\beta}$        (long_name='discount factor')
+    gam         ${\gamma}$       (long_name='long-run TFP growth')
+    logmst         ${\log(m^*)}$ (long_name='long-run money growth')
+    rho         ${\rho}$         (long_name='autocorrelation money growth')
+    phi         ${\phi}$         (long_name='labor weight in consumption')
+    del         ${\delta}$       (long_name='depreciation rate')
+    ;
+
+% roughly picked values to allow simulating the model before estimation
 alp = 0.33;
 bet = 0.99;
 gam = 0.003;
-mst = 1.011;
+logmst = log(1.011);
 rho = 0.7;
-psi = 0.787;
+phi = 0.787;
 del = 0.02;
 
 model;
+[name='NC before eq. (1), TFP growth equation']
 dA = exp(gam+e_a);
-log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+[name='NC eq. (2), money growth rate']
+log(m) = (1-rho)*logmst + rho*log(m(-1))+e_m;
+[name='NC eq. (A1), Euler equation']
 -P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+[name='NC below eq. (A1), firm borrowing constraint']
 W = l/n;
--(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+[name='NC eq. (A2), intratemporal labour market condition']
+-(phi/(1-phi))*(c*P/(1-n))+l/n = 0;
+[name='NC below eq. (A2), credit market clearing']
 R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+[name='NC eq. (A3), credit market optimality']
 1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+[name='NC eq. (18), aggregate resource constraint']
 c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+[name='NC eq. (19), money market condition']
 P*c = m;
+[name='NC eq. (20), credit market equilibrium condition']
 m-1+d = l;
+[name='Definition TFP shock']
 e = exp(e_a);
+[name='Implied by NC eq. (18), production function']
 y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+[name='Observation equation GDP growth']
 gy_obs = dA*y/y(-1);
+[name='Observation equation price level']
 gp_obs = (P/P(-1))*m(-1)/dA;
 end;
 
@@ -81,19 +119,19 @@ end;
 steady_state_model;
   dA = exp(gam);
   gst = 1/dA;
-  m = mst;
+  m = exp(logmst);
   khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
-  xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
-  nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
+  xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/m )^(-1);
+  nust = phi*m^2/( (1-alp)*(1-phi)*bet*gst^alp*khst^alp );
   n  = xist/(nust+xist);
   P  = xist + nust;
   k  = khst*n;
 
-  l  = psi*mst*n/( (1-psi)*(1-n) );
-  c  = mst/P;
-  d  = l - mst + 1;
+  l  = phi*m*n/( (1-phi)*(1-n) );
+  c  = m/P;
+  d  = l - m + 1;
   y  = k^alp*n^(1-alp)*gst^alp;
-  R  = mst/bet;
+  R  = m/bet;
   W  = l/n;
   ist  = y-c;
   q  = 1 - d;
@@ -104,17 +142,18 @@ steady_state_model;
   gy_obs = dA;
 end;
 
-steady;
 
+steady;
 check;
 
+% Table 1 of Schorfheide (2000)
 estimated_params;
 alp, beta_pdf, 0.356, 0.02;
 bet, beta_pdf, 0.993, 0.002;
 gam, normal_pdf, 0.0085, 0.003;
-mst, normal_pdf, 1.0002, 0.007;
-rho, beta_pdf, 0.129, 0.100;
-psi, beta_pdf, 0.65, 0.05;
+logmst, normal_pdf, 0.0002, 0.007;
+rho, beta_pdf, 0.129, 0.223;
+phi, beta_pdf, 0.65, 0.05;
 del, beta_pdf, 0.01, 0.005;
 stderr e_a, inv_gamma_pdf, 0.035449, inf;
 stderr e_m, inv_gamma_pdf, 0.008862, inf;
@@ -122,14 +161,8 @@ end;
 
 varobs gp_obs gy_obs;
 
-estimation(order=1, datafile=fsdat_simul, nobs=192, loglinear, mh_replic=2000, mh_nblocks=2, mh_jscale=0.8);
-
-
-/*
- * The following lines were used to generate the data file. If you want to
- * generate another random data file, comment the "estimation" line and uncomment
- * the following lines.
- */
+estimation(order=1, datafile=fs2000_data, loglinear,logdata, mode_compute=4, mh_replic=20000, nodiagnostic, mh_nblocks=2, mh_jscale=0.8, mode_check);
 
-//stoch_simul(periods=200, order=1);
-//datatomfile('fsdat_simul', char('gy_obs', 'gp_obs'));
+%uncomment the following lines to generate LaTeX-code of the model equations
+%write_latex_original_model(write_equation_tags);
+%collect_latex_files;

examples/fs2000_data.m

+%This file is a direct Matlab implementation of the loaddata.g and data.prn files
+%of Schorfheide, Frank (2000): Loss function-based evaluation of DSGE models
+%(replication data). Version: 1. Journal of Applied Econometrics. Dataset.
+%http://dx.doi.org/10.15456/jae.2022314.0708799949
+% Copyright: 2000-2022 Frank Schorfheide
+% Copyright: 2023 Dynare Team
+
+% License: CC BY 4.0
+% (https://creativecommons.org/licenses/by/4.0/legalcode)
+
+% Time series, extracted 05/04/00
+% columms are quarterly data from 1949:IV to 1997:IV
+% 1: GDPD = GROSS DOMESTIC PRODUCT:IMPLICIT PRICE DEFLATOR (INDEX,92=100)(T7.1)
+% 2: GDPQ = GROSS DOMESTIC PRODUCT
+% 3: GPOP = POPULATION, NIPA basis (THOUS.,NSA)
+
+data_q=[18.02 1474.5 150.2
+17.94 1538.2 150.9
+18.01 1584.5 151.4
+18.42 1644.1 152
+18.73 1678.6 152.7
+19.46 1693.1 153.3
+19.55 1724   153.9
+19.56 1758.2 154.7
+19.79 1760.6 155.4
+19.77 1779.2 156
+19.82 1778.8 156.6
+20.03 1790.9 157.3
+20.12 1846   158
+20.1  1882.6 158.6
+20.14 1897.3 159.2
+20.22 1887.4 160
+20.27 1858.2 160.7
+20.34 1849.9 161.4
+20.39 1848.5 162
+20.42 1868.9 162.8
+20.47 1905.6 163.6
+20.56 1959.6 164.3
+20.62 1994.4 164.9
+20.78 2020.1 165.7
+21    2030.5 166.5
+21.2  2023.6 167.2
+21.33 2037.7 167.9
+21.62 2033.4 168.7
+21.71 2066.2 169.5
+22.01 2077.5 170.2
+22.15 2071.9 170.9
+22.27 2094   171.7
+22.29 2070.8 172.5
+22.56 2012.6 173.1
+22.64 2024.7 173.8
+22.77 2072.3 174.5
+22.88 2120.6 175.3
+22.92 2165   176.045
+22.91 2223.3  176.727
+22.94 2221.4  177.481
+23.03 2230.95 178.268
+23.13 2279.22 179.694
+23.22 2265.48 180.335
+23.32 2268.29 181.094
+23.4  2238.57 181.915
+23.45 2251.68 182.634
+23.51 2292.02 183.337
+23.56 2332.61 184.103
+23.63 2381.01 184.894
+23.75 2422.59 185.553
+23.81 2448.01 186.203
+23.87 2471.86 186.926
+23.94 2476.67 187.68
+24    2508.7  188.299
+24.07 2538.05 188.906
+24.12 2586.26 189.631
+24.29 2604.62 190.362
+24.35 2666.69 190.954
+24.41 2697.54 191.56
+24.52 2729.63 192.256
+24.64 2739.75 192.938
+24.77 2808.88 193.467
+24.88 2846.34 193.994
+25.01 2898.79 194.647
+25.17 2970.48 195.279
+25.32 3042.35 195.763
+25.53 3055.53 196.277
+25.79 3076.51 196.877
+26.02 3102.36 197.481
+26.14 3127.15 197.967
+26.31 3129.53 198.455
+26.6  3154.19 199.012
+26.9  3177.98 199.572
+27.21 3236.18 199.995
+27.49 3292.07 200.452
+27.75 3316.11 200.997
+28.12 3331.22 201.538
+28.39 3381.86 201.955
+28.73 3390.23 202.419
+29.14 3409.65 202.986
+29.51 3392.6  203.584
+29.94 3386.49 204.086
+30.36 3391.61 204.721
+30.61 3422.95 205.419
+31.02 3389.36 206.13
+31.5  3481.4  206.763
+31.93 3500.95 207.362
+32.27 3523.8  208
+32.54 3533.79 208.642
+33.02 3604.73 209.142
+33.2  3687.9  209.637
+33.49 3726.18 210.181
+33.95 3790.44 210.737
+34.36 3892.22 211.192
+34.94 3919.01 211.663
+35.61 3907.08 212.191
+36.29 3947.11 212.708
+37.01 3908.15 213.144
+37.79 3922.57 213.602
+38.96 3879.98 214.147
+40.13 3854.13 214.7
+41.05 3800.93 215.135
+41.66 3835.21 215.652
+42.41 3907.02 216.289
+43.19 3952.48 216.848
+43.69 4044.59 217.314
+44.15 4072.19 217.776
+44.77 4088.49 218.338
+45.57 4126.39 218.917
+46.32 4176.28 219.427
+47.07 4260.08 219.956
+47.66 4329.46 220.573
+48.63 4328.33 221.201
+49.42 4345.51 221.719
+50.41 4510.73 222.281
+51.27 4552.14 222.933
+52.35 4603.65 223.583
+53.51 4605.65 224.152
+54.65 4615.64 224.737
+55.82 4644.93 225.418
+56.92 4656.23 226.117
+58.18 4678.96 226.754
+59.55 4566.62 227.389
+61.01 4562.25 228.07
+62.59 4651.86 228.689
+64.15 4739.16 229.155
+65.37 4696.82 229.674
+66.65 4753.02 230.301
+67.87 4693.76 230.903
+68.86 4615.89 231.395
+69.72 4634.88 231.906
+70.66 4612.08 232.498
+71.44 4618.26 233.074
+72.08 4662.97 233.546
+72.83 4763.57 234.028
+73.48 4849    234.603
+74.19 4939.23 235.153
+75.02 5053.56 235.605
+75.58 5132.87 236.082
+76.25 5170.34 236.657
+76.81 5203.68 237.232
+77.63 5257.26 237.673
+78.25 5283.73 238.176
+78.76 5359.6  238.789
+79.45 5393.57 239.387
+79.81 5460.83 239.861
+80.22 5466.95 240.368
+80.84 5496.29 240.962
+81.45 5526.77 241.539
+82.09 5561.8  242.009
+82.68 5618    242.52
+83.33 5667.39 243.12
+84.09 5750.57 243.721
+84.67 5785.29 244.208
+85.56 5844.05 244.716
+86.66 5878.7  245.354
+87.44 5952.83 245.966
+88.45 6010.96 246.46
+89.39 6055.61 247.017
+90.13 6087.96 247.698
+90.88 6093.51 248.374
+92    6152.59 248.928
+93.18 6171.57 249.564
+94.14 6142.1  250.299
+95.11 6078.96 251.031
+96.27 6047.49 251.65
+97    6074.66 252.295
+97.7  6090.14 253.033
+98.31 6105.25 253.743
+99.13 6175.69 254.338
+99.79 6214.22 255.032
+100.17 6260.74 255.815
+100.88 6327.12 256.543
+101.84 6327.93 257.151
+102.35 6359.9  257.785
+102.83 6393.5  258.516
+103.51 6476.86 259.191
+104.13 6524.5  259.738
+104.71 6600.31 260.351
+105.39 6629.47 261.04
+106.09 6688.61 261.692
+106.75 6717.46 262.236
+107.24 6724.2  262.847
+107.75 6779.53 263.527
+108.29 6825.8  264.169
+108.91 6882    264.681
+109.24 6983.91 265.258
+109.74 7020    265.887
+110.23 7093.12 266.491
+111    7166.68 266.987
+111.43 7236.5  267.545
+111.76 7311.24 268.171
+112.08 7364.63 268.815];
+
+%Compute growth rates: from 1950:I to 1997:IV
+gy_obs=1000*data_q(:,2)./data_q(:,3); %real GDP per capita
+gy_obs=diff(log(gy_obs));
+
+gp_obs = diff(log(data_q(:,1))); %GDP deflator inflation

examples/fs2000_nonstationary.mod

@@ -8,7 +8,7 @@
  */
 
 /*
- * Copyright (C) 2004-2015 Dynare Team
+ * Copyright © 2004-2015 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -23,7 +23,7 @@
  * GNU General Public License for more details.
  *
  * You should have received a copy of the GNU General Public License
- * along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+ * along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
  */
 
 var gM gA;