Add capacities for rolling window estimation

doc/dynare.texi

@@ -4788,13 +4788,25 @@ the file after @code{first_obs}
 Runs a recursive estimation and forecast for samples of size ranging
 of @var{INTEGER1} to @var{INTEGER2}. Option @code{forecast} must
 also be specified. The forecasts are stored in the
-@code{RecursiveForecast} field of the results structure (@pxref{RecursiveForecast}). The respective results structures @code{oo_} are saved in @code{oo_recursive_} (@pxref{oo_recursive_}).
+@code{RecursiveForecast} field of the results structure (@pxref{RecursiveForecast}). 
+The respective results structures @code{oo_} are saved in @code{oo_recursive_} (@pxref{oo_recursive_})
+and are indexed with the respective sample length.
 
 @item first_obs = @var{INTEGER}
 @anchor{first_obs}
 The number of the first observation to be used. In case of estimating a DSGE-VAR, 
 @code{first_obs} needs to be larger than the number of lags.  Default: @code{1}
 
+@item first_obs = [@var{INTEGER1}:@var{INTEGER2}]
+@anchor{first_obs1}
+Runs a rolling window estimation and forecast for samples of fixed size @code{nobs} starting with the 
+first observation ranging from @var{INTEGER1} to @var{INTEGER2}. Option @code{forecast} 
+must also be specified. This option is incompatible with requesting recursive forecasts using an 
+expanding window (@pxref{nobs1,,nobs}). The respective results structures @code{oo_} 
+are saved in @code{oo_recursive_} (@pxref{oo_recursive_}) and are indexed with the respective
+first observation of the rolling window.
+
+
 @item prefilter = @var{INTEGER}
 @anchor{prefilter}
 A value of @code{1} means that the estimation procedure will demean

matlab/dynare_estimation.m

@@ -52,7 +52,15 @@ else
     nobs = [];
 end
 
+first_obs = sort(options_.first_obs); 
+
 nnobs = length(nobs);
+nfirstobs = length(first_obs);
+
+if nnobs~=1 && nfirstobs~=1
+    error('You cannot simultaneously do rolling window and recursive estimation')
+end
+    
 horizon = options_.forecast;
 
 if nargin<2 || ~exist(dname) || isempty(dname)
@@ -73,10 +81,15 @@ if options_.logged_steady_state
 end
 
 
-if nnobs > 1
-    for i=1:nnobs
-        options_.nobs = nobs(i);
-        M_.dname = [dname '_' int2str(nobs(i))];
+if nnobs>1 || nfirstobs > 1
+    for i=1:max(nnobs,nfirstobs)
+        if nnobs>1
+            options_.nobs = nobs(i);
+            M_.dname = [dname '_' int2str(nobs(i))];
+        elseif nfirstobs>1;
+            options_.first_obs=first_obs(i);            
+            M_.dname = [dname '_' int2str(first_obs(i))];
+        end
         dynare_estimation_1(var_list,M_.dname);
         if isequal(i,1)
             options_.mode_file = [M_.fname '_mode'];
@@ -86,7 +99,11 @@ if nnobs > 1
                 dynare_estimation_1(var_list,M_.dname);
             end
         end
-        oo_recursive_{nobs(i)} = oo_;
+        if nnobs>1
+            oo_recursive_{nobs(i)} = oo_;
+        elseif nfirstobs>1;
+            oo_recursive_{first_obs(i)} = oo_;
+        end
     end
 else
     dynare_estimation_1(var_list,dname);

tests/Makefile.am

@@ -214,6 +214,7 @@ MODFILES = \
 	stochastic_purely_forward/stochastic_purely_forward.mod \
 	stochastic_purely_forward/stochastic_purely_forward_with_static.mod \
 	forecast/Hansen_exo_det_forecast.mod \
+	forecast/ls2003_rolling_window_forecast.mod \
 	gradient/fs2000_numgrad_13.mod \
 	gradient/fs2000_numgrad_15.mod \
 	gradient/fs2000_numgrad_2.mod \

tests/forecast/ls2003_rolling_window_forecast.mod

@@ -0,0 +1,64 @@
+var y y_s R pie dq pie_s de A y_obs pie_obs R_obs;
+varexo e_R e_q e_ys e_pies e_A;
+
+parameters psi1 psi2 psi3 rho_R tau alpha rr k rho_q rho_A rho_ys rho_pies;
+
+psi1 = 1.54;
+psi2 = 0.25;
+psi3 = 0.25;
+rho_R = 0.5;
+alpha = 0.3;
+rr = 2.51;
+k = 0.5;
+tau = 0.5;
+rho_q = 0.4;
+rho_A = 0.2;
+rho_ys = 0.9;
+rho_pies = 0.7;
+
+
+model(linear);
+y = y(+1) - (tau +alpha*(2-alpha)*(1-tau))*(R-pie(+1))-alpha*(tau +alpha*(2-alpha)*(1-tau))*dq(+1) + alpha*(2-alpha)*((1-tau)/tau)*(y_s-y_s(+1))-A(+1);
+pie = exp(-rr/400)*pie(+1)+alpha*exp(-rr/400)*dq(+1)-alpha*dq+(k/(tau+alpha*(2-alpha)*(1-tau)))*y+alpha*(2-alpha)*(1-tau)/(tau*(tau+alpha*(2-alpha)*(1-tau)))*y_s;
+pie = de+(1-alpha)*dq+pie_s;
+R = rho_R*R(-1)+(1-rho_R)*(psi1*pie+psi2*(y+alpha*(2-alpha)*((1-tau)/tau)*y_s)+psi3*de)+e_R;
+dq = rho_q*dq(-1)+e_q;
+y_s = rho_ys*y_s(-1)+e_ys;
+pie_s = rho_pies*pie_s(-1)+e_pies;
+A = rho_A*A(-1)+e_A;
+y_obs = y-y(-1)+A;
+pie_obs = 4*pie;
+R_obs = 4*R;
+end;
+
+shocks;
+var e_R = 1.25^2;
+var e_q = 2.5^2;
+var e_A = 1.89;
+var e_ys = 1.89;
+var e_pies = 1.89;
+end;
+
+varobs y_obs R_obs pie_obs dq de;
+
+estimated_params;
+psi1 , gamma_pdf,1.5,0.5;
+psi2 , gamma_pdf,0.25,0.125;
+psi3 , gamma_pdf,0.25,0.125;
+rho_R ,beta_pdf,0.5,0.2;
+alpha ,beta_pdf,0.3,0.1;
+rr ,gamma_pdf,2.5,1;
+k , gamma_pdf,0.5,0.25;
+tau ,gamma_pdf,0.5,0.2;
+rho_q ,beta_pdf,0.4,0.2;
+rho_A ,beta_pdf,0.5,0.2;
+rho_ys ,beta_pdf,0.8,0.1;
+rho_pies,beta_pdf,0.7,0.15;
+stderr e_R,inv_gamma_pdf,1.2533,0.6551;
+stderr e_q,inv_gamma_pdf,2.5066,1.3103;
+stderr e_A,inv_gamma_pdf,1.2533,0.6551;
+stderr e_ys,inv_gamma_pdf,1.2533,0.6551;
+stderr e_pies,inv_gamma_pdf,1.88,0.9827;
+end;
+options_.plot_priors=0;
+estimation(silent_optimizer,datafile='../recursive/data_ca1',first_obs=[8 11],nobs=[76],mh_nblocks=1,prefilter=1,mh_jscale=0.5,mh_replic=2000,forecast=8) y_obs R_obs pie_obs dq de;