Merge pull request #1295 from JohannesPfeifer/stochastic_singularity

Stochastic singularity

doc/dynare.texi

@@ -6075,6 +6075,15 @@ the option is equal to @code{0}, Dynare will not automatically change
 the filter, but rather use a penalty value for the likelihood when
 such a singularity is encountered. Default: @code{1}.
 
+@item keep_kalman_algo_if_singularity_is_detected
+@anchor{keep_kalman_algo_if_singularity_is_detected}
+With the default @ref{use_univariate_filters_if_singularity_is_detected}=1, Dynare will switch 
+to the univariate Kalman filter when it encounters a singular forecast error variance
+matrix during Kalman filtering. Upon encountering such a singularity for the first time, all subsequent
+parameter draws and computations will automatically rely on univariate filter, i.e. Dynare will never try 
+the multivariate filter again. Use the @code{keep_kalman_algo_if_singularity_is_detected} option to have the 
+@code{use_univariate_filters_if_singularity_is_detected} only affect the behavior for the current draw/computation.
+
 @item qz_zero_threshold = @var{DOUBLE}
 @xref{qz_zero_threshold}.
 

matlab/dsge_likelihood.m

@@ -894,8 +894,10 @@ if imag(fval)~=0
     return
 end
 
-% Update DynareOptions.kalman_algo.
-DynareOptions.kalman_algo = kalman_algo;
+if ~DynareOptions.kalman.keep_kalman_algo_if_singularity_is_detected
+    % Update DynareOptions.kalman_algo.
+    DynareOptions.kalman_algo = kalman_algo;
+end
 
 if analytic_derivation==0 && nargout>3,
     lik=lik(start:end,:);

matlab/initial_estimation_checks.m

@@ -130,7 +130,19 @@ ana_deriv = DynareOptions.analytic_derivation;
 DynareOptions.analytic_derivation=0;
 if ~isequal(DynareOptions.mode_compute,11) || ...
         (isequal(DynareOptions.mode_compute,11) && isequal(DynareOptions.order,1))
-  [fval,info] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
+    %shut off potentially automatic switch to diffuse filter for the
+    %purpose of checking stochastic singularity
+    use_univariate_filters_if_singularity_is_detected_old=DynareOptions.use_univariate_filters_if_singularity_is_detected;
+    DynareOptions.use_univariate_filters_if_singularity_is_detected=0;
+    [fval,info] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
+    if info(1)==50
+        fprintf('\ninitial_estimation_checks:: The forecast error variance in the multivariate Kalman filter became singular.\n')    
+        fprintf('initial_estimation_checks:: This is often a sign of stochastic singularity, but can also sometimes happen by chance\n')    
+        fprintf('initial_estimation_checks:: for a particular combination of parameters and data realizations.\n')    
+        error('initial_estimation_checks:: The forecast error variance in the multivariate Kalman filter became singular.')    
+    end
+    %reset options 
+    DynareOptions.use_univariate_filters_if_singularity_is_detected=use_univariate_filters_if_singularity_is_detected_old;
 else 
     info=0;
     fval = 0;

tests/Makefile.am

@@ -328,6 +328,7 @@ XFAIL_MODFILES = ramst_xfail.mod \
 	estim_param_in_shock_value_xfail.mod \
 	optimal_policy/Ramsey/ramsey_ex_wrong_ss_file_xfail.mod \
 	estimation/fs2000_mixed_ML_xfail.mod \
+	estimation/fs2000_stochastic_singularity_xfail.mod \
 	identification/ident_unit_root/ident_unit_root_xfail.mod \
 	steady_state/Linear_steady_state_xfail.mod
 

tests/estimation/fs2000_stochastic_singularity_xfail.mod

@@ -0,0 +1,76 @@
+// See fs2000.mod in the examples/ directory for details on the model
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m e_junk;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-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;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+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;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1) + e_junk;
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+steady_state_model;
+  dA = exp(gam);
+  gst = 1/dA;
+  m = mst;
+  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 );
+  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;
+  y  = k^alp*n^(1-alp)*gst^alp;
+  R  = mst/bet;
+  W  = l/n;
+  ist  = y-c;
+  q  = 1 - d;
+
+  e = 1;
+  
+  gp_obs = m/dA;
+  gy_obs = dA;
+end;
+
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+var e_junk; stderr 0.005;
+end;
+
+stoch_simul(order=1,periods=300,irf=0,nomoments) k y c;
+datatomfile('fsdat_simul_collinear', char('k','y','c'));
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+end;
+
+varobs k y c;
+
+
+estimation(order=1,datafile=fsdat_simul_collinear) y m;