tests/estimation/method_of_moments/CET/cet_rwmh.mod

@@ -83,7 +83,7 @@ method_of_moments(mom_method = irf_matching
 %, mode_check_neighbourhood_size
 %, mode_check_number_of_points
 , mode_check_symmetric_plots = 0
-, mode_compute = 1
+, mode_compute = 4
 , mode_file = cet_original_mode
 %, nobs
 %, no_posterior_kernel_density

tests/estimation/method_of_moments/CET/cet_slice.mod

@@ -192,7 +192,7 @@ method_of_moments(mom_method = irf_matching
 , mh_replic=10
 , plot_priors = 0
 , nograph
-, mode_compute = 1
+, mode_compute = 5
 , posterior_sampling_method = 'slice'
 , posterior_sampler_options = ('rotated',1
 %                              ,'mode_files'

tests/estimation/method_of_moments/CET/cet_tarb.mod

@@ -156,14 +156,14 @@ method_of_moments(mom_method = irf_matching
 
 
 method_of_moments(mom_method = irf_matching
-, additional_optimizer_steps = [1]
+, additional_optimizer_steps = [4]
 , cova_compute=1
 , dirname=cet_tarb_results
 , irf_matching_file = cet_irf_matching_file
 , mh_conf_sig = 0.90
 , mh_replic=0
 %, mode_check
-, mode_compute = 4
+, mode_compute = 5
 , mode_file = 'cet_tarb_results/method_of_moments/cet_tarb_mh_mode'
 , plot_priors = 0
 , nograph

tests/moments/example1_hp_test.mod

@@ -77,12 +77,12 @@ send_endogenous_variables_to_workspace;
 options_.nomoments=0;
 oo_unfiltered_all_shocks=oo_;
 
-[junk, y_filtered]=sample_hp_filter(y',1600);
-[junk, c_filtered]=sample_hp_filter(c',1600);
-[junk, k_filtered]=sample_hp_filter(k',1600);
-[junk, a_filtered]=sample_hp_filter(a',1600);
-[junk, h_filtered]=sample_hp_filter(h',1600);
-[junk, b_filtered]=sample_hp_filter(b',1600);
+[junk, y_filtered]=sample_hp_filter(y,1600);
+[junk, c_filtered]=sample_hp_filter(c,1600);
+[junk, k_filtered]=sample_hp_filter(k,1600);
+[junk, a_filtered]=sample_hp_filter(a,1600);
+[junk, h_filtered]=sample_hp_filter(h,1600);
+[junk, b_filtered]=sample_hp_filter(b,1600);
 
 verbatim;
 total_std_all_shocks_filtered_sim=std([y_filtered c_filtered k_filtered a_filtered h_filtered b_filtered]);
@@ -112,12 +112,12 @@ stoch_simul(order=1,nofunctions,hp_filter=0,periods=2500000,nomoments);
 send_endogenous_variables_to_workspace;
 oo_unfiltered_one_shock=oo_;
 
-[junk, y_filtered]=sample_hp_filter(y',1600);
-[junk, c_filtered]=sample_hp_filter(c',1600);
-[junk, k_filtered]=sample_hp_filter(k',1600);
-[junk, a_filtered]=sample_hp_filter(a',1600);
-[junk, h_filtered]=sample_hp_filter(h',1600);
-[junk, b_filtered]=sample_hp_filter(b',1600);
+[junk, y_filtered]=sample_hp_filter(y,1600);
+[junk, c_filtered]=sample_hp_filter(c,1600);
+[junk, k_filtered]=sample_hp_filter(k,1600);
+[junk, a_filtered]=sample_hp_filter(a,1600);
+[junk, h_filtered]=sample_hp_filter(h,1600);
+[junk, b_filtered]=sample_hp_filter(b,1600);
 
 verbatim;
 total_std_one_shock_filtered_sim=std([y_filtered c_filtered k_filtered a_filtered h_filtered b_filtered]);

tests/occbin/filter/NKM.mod

@@ -13,66 +13,66 @@
 
 // ----------------- Defintions -----------------------------------------//
 var        
-    c          //1  Consumption 
-    n          //2  Labor
-    y          //5  Output
-    yf         //6  Final goods       
-    yg         //11 Output growth gap
-    w          //12 Real wage rate
-    wf         //13 Flexible real wage
-    pigap      //15 Inflation rate -> pi(t)/pibar = pigap
-    inom    ${i^{nom}}$       //16 Nominal interest rate
-    inomnot    //17 Notional interest rate
-    mc         //19 Real marginal cost
-    lam  ${\lambda}$      //20 Inverse marginal utility of wealth  
-    g          //21 Growth shock       
-    s          //22 Risk premium shock
-    mp         //23 Monetary policy shock    
-    pi   ${\pi}$   //24 Observed inflation    
+    c       $c$         (long_name='Consumption')
+    n                   (long_name='Labor')
+    y       $y$         (long_name='Output')
+    yf                  (long_name='Final goods')
+    yg                  (long_name='Output growth gap')
+    w                   (long_name='Real wage rate')
+    wf                  (long_name='Flexible real wage')
+    pigap               (long_name='Inflation rate -> pi(t)/pibar = pigap')
+    inom    ${i^{nom}}$ (long_name='Nominal interest rate')
+    inomnot             (long_name='Notional interest rate')
+    mc                  (long_name='Real marginal cost')
+    lam  ${\lambda}$    (long_name='Inverse marginal utility of wealth')
+    g                   (long_name='Growth shock')
+    s                   (long_name='Risk premium shock')
+    mp                  (long_name='Monetary policy shock')
+    pi   ${\pi}$        (long_name='Observed inflation')
     @#if !(small_model)
-        x          //3  Investment
-        k          //4  Capital    
-        u          //7  Utilization cost
-        ups        //8  Utilization choice
-        wg         //9  Real wage growth gap    
-        xg         //10 Investment growth
-        rk         //14 Real rental rate
-        q          //18 Tobins q   
+        x               (long_name='Investment')
+        k               (long_name='Capital')
+        u               (long_name='Utilization cost')
+        ups             (long_name='Utilization choice')
+        wg              (long_name='Real wage growth gap')    
+        xg              (long_name='Investment growth')
+        rk              (long_name='Real rental rate')
+        q               (long_name='Tobins q')
     @#endif
 ;
 varexo          
-    epsg    ${\varepsilon_g}$   // Productivity growth shock
-    epsi       // Notional interest rate shock
-    epss       // Risk premium shock
+    epsg    ${\varepsilon_g}$   (long_name='Productivity growth shock')
+    epsi                        (long_name='Notional interest rate shock')
+    epss                        (long_name='Risk premium shock')
 ;        
 parameters
     // Calibrated Parameters    
-    beta    $\beta$    // Discount factor
-    chi         // Labor disutility scale
-    thetap      // Elasticity of subs. between intermediate goods
-    thetaw      // Elasticity of subs. between labor types
-    nbar        // Steady state labor
-    eta         // Inverse frish elasticity of labor supply
-    delta       // Depreciation
-    alpha       // Capital share
-    gbar        // Mean growth rate
-    pibar       // Inflation target
-    inombar     // Steady gross nom interest rate    
-    inomlb      // Effective lower bound on gross nominal interest rate    
-    sbar        // Average risk premium
+    beta    $\beta$    (long_name='Discount factor')
+    chi                 (long_name='Labor disutility scale')
+    thetap              (long_name='Elasticity of subs. between intermediate goods')
+    thetaw              (long_name='Elasticity of subs. between labor types')
+    nbar                (long_name='Steady state labor')
+    eta                 (long_name='Inverse frish elasticity of labor supply')
+    delta               (long_name='Depreciation')
+    alpha               (long_name='Capital share')
+    gbar                (long_name='Mean growth rate')
+    pibar               (long_name='Inflation target')
+    inombar             (long_name='Steady gross nom interest rate')
+    inomlb              (long_name='Effective lower bound on gross nominal interest rate')
+    sbar                (long_name='Average risk premium')
     // Parameters for DGP and Estimated parameters
-    varphip     // Rotemberg price adjustment cost
-    varphiw     // Rotemberg wage adjustment cost
-    h          	// Habit persistence
-    rhos        // Persistence
-    rhoi    	// Persistence
-    sigz        // Standard deviation technology
-    sigs        // Standard deviation risk premia
-    sigi        // Standard deviation mon pol
-    phipi       // Inflation responsiveness
-    phiy        // Output responsiveness
-    nu          // Investment adjustment cost
-    sigups      // Utilization    
+    varphip             (long_name='Rotemberg price adjustment cost')
+    varphiw             (long_name='Rotemberg wage adjustment cost')
+    h          	        (long_name='Habit persistence')
+    rhos                (long_name='Persistence')
+    rhoi    	        (long_name='Persistence')
+    sigz                (long_name='Standard deviation technology')
+    sigs                (long_name='Standard deviation risk premia')
+    sigi                (long_name='Standard deviation mon pol')
+    phipi               (long_name='Inflation responsiveness')
+    phiy                (long_name='Output responsiveness')
+    nu                  (long_name='Investment adjustment cost')
+    sigups              (long_name='Utilization')
  ;
 
 
@@ -316,36 +316,34 @@ varobs yg inom pi;
 
     // forecast starting from period 42, zero shocks (default)
     smoother2histval(period=42);
-    [oo, error_flag] = occbin.forecast(options_,M_,oo_,8);
+    [forecast, error_flag] = occbin.forecast(options_,M_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state,8);
     // forecast with stochastic shocks
     options_.occbin.forecast.qmc=true;
     options_.occbin.forecast.replic=127;
-    [oo1, error_flag] = occbin.forecast(options_,M_,oo_,8);
+    [forecast1, error_flag] = occbin.forecast(options_,M_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state,8);
+
+    figure('Name','OccBin: Forecasts')
+    subplot(2,1,1)
+    plot(1:8,forecast.piecewise.Mean.inom,'b-',1:8,forecast1.piecewise.Mean.inom,'r--')
+    subplot(2,1,2)
+    plot(1:8,forecast.piecewise.Mean.y,'b-',1:8,forecast1.piecewise.Mean.y,'r--')
 
     // GIRF given states in 42 and shocks in 43
     t0=42;
     options_.occbin.irf.exo_names=M_.exo_names;
     options_.occbin.irf.t0=t0;
-    oo_ = occbin.irf(M_,oo_,options_);
-
-    vars_irf = {
-    'c', 'consumption'    
-    'n', 'labor'    
-    'y', 'output'    
-    'pigap', 'inflation rate'
-    'inom', 'interest rate'  
-    'inomnot', 'shadow rate'
-    };
-
-    options_.occbin.plot_irf.exo_names = M_.exo_names;
-    options_.occbin.plot_irf.endo_names = vars_irf(:,1);
-    options_.occbin.plot_irf.endo_names_long = vars_irf(:,2);
+    oo_.occbin.irfs = occbin.irf(M_,oo_,options_);
+
+    var_list_ = {'c','n','y','pigap','inom','inomnot'};
+
     // if you want to scale ...
     // options_occbin_.plot_irf.endo_scaling_factor = vars_irf(:,3);
     options_.occbin.plot_irf.simulname = ['t0_' int2str(t0)];
-    options_.occbin.plot_irf.tplot = min(40,options_.irf);
-    occbin.plot_irfs(M_,oo_,options_);
-
+    options_.irf=40;
+    occbin.plot_irfs(M_,oo_.occbin.irfs,options_,var_list_);
+    var_list_={};
+    options_.occbin.plot_irf.simulname = ['t0_' int2str(t0) '_full'];
+    occbin.plot_irfs(M_,oo_.occbin.irfs,options_,var_list_);
     oo0=oo_;
     // use smoother_redux
     estimation(
@@ -374,7 +372,7 @@ varobs yg inom pi;
             consider_all_endogenous,heteroskedastic_filter,filter_step_ahead=[1],smoothed_state_uncertainty);
             
     // show initial condition effect of IF
-    figure,
+    figure('Name','OccBin: Smoothed shocks')
     subplot(221)
     plot([oo0.SmoothedShocks.epsg oo_.SmoothedShocks.epsg]), title('epsg')
     subplot(222)
@@ -382,7 +380,7 @@ varobs yg inom pi;
     subplot(223)
     plot([oo0.SmoothedShocks.epss oo_.SmoothedShocks.epss]), title('epss')
     legend('PKF','IF')
-    figure,
+    figure('Name','OccBin: Smoothed Variables')
     subplot(221)
     plot([oo0.SmoothedVariables.inom oo_.SmoothedVariables.inom]), title('inom')
     subplot(222)

tests/optimal_policy/neo_ramsey_det_bytecode.mod

+//MODEL:
+//test on Dynare to find the lagrangean multipliers.
+//We consider a standard NK model. We use the FOCS of the competitive economy and we aim at calculating the Ramsey optimal problem.
+
+//------------------------------------------------------------------------------------------------------------------------
+//1. Variable declaration
+//------------------------------------------------------------------------------------------------------------------------
+
+var pai, c, n, r, a;
+
+//4 variables + 1 shock
+
+varexo u;
+
+
+//------------------------------------------------------------------------------------------------------------------------
+// 2. Parameter declaration and calibration
+//-------------------------------------------------------------------------------------------------------------------------
+
+parameters beta, rho, epsilon, omega, phi, gamma;
+
+beta=0.99;
+gamma=3;       //Frish elasticity
+omega=17;        //price stickyness
+epsilon=8;      //elasticity for each variety of consumption
+phi=1;           //coefficient associated to labor effort disutility
+
+rho=0.95;  //coefficient associated to productivity shock
+
+//-----------------------------------------------------------------------------------------------------------------------
+// 3. The model
+//-----------------------------------------------------------------------------------------------------------------------
+
+model(bytecode);
+a=rho*(a(-1))+u;
+1/c=beta*(1/(c(+1)))*(r/(pai(+1)));               //euler
+omega*pai*(pai-1)=beta*omega*(c/(c(+1)))*(pai(+1))*(pai(+1)-1)+epsilon*exp(a)*n*(c/exp(a)*phi*n^gamma-(epsilon-1)/epsilon);  //NK pc
+//pai*(pai-1)/c = beta*pai(+1)*(pai(+1)-1)/c(+1)+epsilon*phi*n^(gamma+1)/omega-exp(a)*n*(epsilon-1)/(omega*c);  //NK pc
+(exp(a))*n=c+(omega/2)*((pai-1)^2);
+end;
+
+//--------------------------------------------------------------------------------------------------------------------------
+// 4. Steady state
+//---------------------------------------------------------------------------------------------------------------------------
+
+initval;
+pai=1;
+r=1/beta;
+c=0.9671684882;
+n=0.9671684882;
+a=0;
+end;
+
+
+//---------------------------------------------------------------------------------------------------------------------------
+// 5. shocks
+//---------------------------------------------------------------------------------------------------------------------------
+
+shocks;
+var u;
+periods 1;
+values 0.008;
+end;
+
+//--------------------------------------------------------------------------------------------------------------------------
+// 6. Ramsey problem
+//--------------------------------------------------------------------------------------------------------------------------
+
+planner_objective(ln(c)-phi*((n^(1+gamma))/(1+gamma)));
+
+write_latex_static_model;
+
+ramsey_model(planner_discount=0.99);
+steady;
+
+perfect_foresight_setup(periods=200);
+perfect_foresight_solver(maxit=20);
+
+rplot r;

tests/riccatiupdate.m

-source_dir = getenv('source_root');
-addpath([source_dir filesep 'matlab']);
-
-dynare_config;
-
-testFailed = 0;
-
-skipline()
-disp('***  TESTING: riccatiupdate.m ***');
-
-t0 = clock;
-
-% Set the number of experiments for time measurement
-N = 5000;
-% Set the dimension of the problem to be solved.
-r = 50;
-n = 100;
-tol = 1e-15;
-% Set the input arguments
-% P, Q: use the fact that for any real matrix A, A'*A is positive semidefinite
-P = rand(n,r);
-P = P'*P;
-Q = rand(n,r);
-Q = Q'*Q;
-K = rand(r,n);
-Z = rand(n,r);
-T = rand(r,r);
-% Computing an upperbound for the norm the updated variance-covariance matrix
-ub = norm(T,1)^2*norm(P,1)*(1+norm(K*Z,1))+norm(Q,1);
-% Weighting the P and Q matrices to keep the norm of the variance-covariance matrix below 1
-P = 0.5*P/ub;
-Q = 0.5*Q/ub;
-
-% 1. Update the state vairance-covariance matrix with Matlab
-tElapsed1 = 0.;
-tic;
-for i=1:N
-   Ptmp_matlab = T*(P-K*Z*P)*transpose(T)+Q;
-end
-tElapsed1 = toc;
-disp(['Elapsed time for the Matlab Riccati update is: ' num2str(tElapsed1) ' (N=' int2str(N) ').'])
-
-% 2. Update the state varance-covariance matrix with the mex routine
-tElapsed2 = 0.;
-Ptmp_fortran = P;
-try
-   tic;
-   for i=1:N
-      Ptmp_fortran = riccati_update(P, T, K, Z, Q);
-   end
-   tElapsed2 = toc;
-   disp(['Elapsed time for the Fortran Riccati update is: ' num2str(tElapsed2) ' (N=' int2str(N) ').'])
-   R = norm(Ptmp_fortran-Ptmp_matlab,1);
-   if (R > tol)
-      testFailed = testFailed+1;
-      dprintf('The Fortran Riccati update is wrong')
-   end
-catch
-   testFailed = testFailed+1;
-   dprintf('Fortran Riccati update failed')
-end
-
-% Compare the Fortran and Matlab execution time
-if tElapsed1<tElapsed2
-    skipline()
-    dprintf('Matlab Riccati update is %5.2f times faster than its Fortran counterpart.', tElapsed2/tElapsed1)
-    skipline()
-else
-    skipline()
-    dprintf('Fortran Riccati update is %5.2f times faster than its Matlab counterpart.', tElapsed1/tElapsed2)
-    skipline()
-end
-
-% Compare results after multiple calls
-N = 50;
-disp(['After 1 update using the Riccati formula, the norm-1 discrepancy is ' num2str(norm(Ptmp_fortran-Ptmp_matlab,1)) '.']);
-for i=2:N
-   Ptmp_matlab = T*(Ptmp_matlab-K*Z*Ptmp_matlab)*transpose(T)+Q;
-   Ptmp_fortran = riccati_update(Ptmp_fortran, T, K, Z, Q);
-   disp(['After ' int2str(i) ' updates using the Riccati formula, the norm-1 discrepancy is ' num2str(norm(Ptmp_fortran-Ptmp_matlab,1)) '.'])
-end
-
-t1 = clock;
-
-fprintf('\n*** Elapsed time (in seconds): %.1f\n\n', etime(t1, t0));
-
-quit(testFailed > 0)

windows/build.sh

@@ -5,7 +5,7 @@
 # The binaries are cross compiled for Windows (64-bit), Octave and MATLAB
 # (all supported versions).
 
-# Copyright © 2017-2023 Dynare Team
+# Copyright © 2017-2024 Dynare Team
 #
 # This file is part of Dynare.
 #
@@ -127,8 +127,8 @@ mkdir "$ZIPDIR"/preprocessor
 cp -p build-win-matlab/preprocessor/src/dynare-preprocessor.exe "$ZIPDIR"/preprocessor
 cp -pr matlab "$ZIPDIR"
 cp -p build-win-matlab/dynare_version.m "$ZIPDIR"/matlab
-mkdir -p "$ZIPDIR"/matlab/modules/dseries/externals/x13/windows/64
-cp -p windows/deps/lib64/x13as/x13as.exe "$ZIPDIR"/matlab/modules/dseries/externals/x13/windows/64
+mkdir -p "$ZIPDIR"/matlab/dseries/externals/x13/windows/64
+cp -p windows/deps/lib64/x13as/x13as.exe "$ZIPDIR"/matlab/dseries/externals/x13/windows/64
 cp -pr examples "$ZIPDIR"
 mkdir -p "$ZIPDIR"/scripts
 cp -p scripts/dynare.el "$ZIPDIR"/scripts

windows/deps/Makefile

@@ -157,7 +157,8 @@ mingw64: tarballs/mingw-w64-x86_64-gcc-$(MINGW64_GCC_VERSION)-any.pkg.tar.zst ta
 	touch $@
 
 tarballs/mingw-w64-x86_64-%-any.pkg.tar.zst:
-	wget $(WGET_OPTIONS) -O $@ http://repo.msys2.org/mingw/x86_64/$(notdir $@)
+	mkdir -p tarballs
+	wget $(WGET_OPTIONS) -O $@ https://www.dynare.org/windows-pkg-build/msys2/6.x/$(notdir $@)
 
 clean-msys2:
 	rm -rf lib64-msys2

windows/dynare.nsi

@@ -52,7 +52,7 @@ Section "Dynare core (preprocessor and M-files)"
  File README.txt ..\NEWS.md ..\license.txt
 
  SetOutPath $INSTDIR\matlab
- File /r ..\matlab\*.m ..\build-win-matlab\dynare_version.m
+ File /r ..\matlab\*.m ..\matlab\*.json ..\build-win-matlab\dynare_version.m
 
  SetOutPath $INSTDIR\preprocessor
  File ..\build-win-matlab\preprocessor\src\dynare-preprocessor.exe
@@ -60,7 +60,7 @@ Section "Dynare core (preprocessor and M-files)"
  SetOutPath $INSTDIR\matlab\preprocessor64
  File ..\matlab\preprocessor64\dynare_m.exe
 
- SetOutPath $INSTDIR\matlab\modules\dseries\externals\x13\windows\64
+ SetOutPath $INSTDIR\matlab\dseries\externals\x13\windows\64
  File deps\lib64\x13as\x13as.exe
 
  SetOutPath $INSTDIR\contrib