Force the weights to sum up to one, and not to the number of particles as in...

Force the weights to sum up to one, and not to the number of particles as in WK (Econometrica 2010).

matlab/particle/sequential_importance_particle_filter.m

@@ -115,7 +115,7 @@ lik  = NaN(sample_size,1);
 
 % Initialization of the weights across particles.
 nb_obs_resamp = 0 ;
-weights = ones(1,number_of_particles) ;
+weights = ones(1,number_of_particles)/number_of_particles ;
 StateVectors = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_rank,number_of_particles),StateVectorMean);
 for t=1:sample_size
     yhat = bsxfun(@minus,StateVectors,state_variables_steady_state);
@@ -134,10 +134,9 @@ for t=1:sample_size
     if (Neff<.5*sample_size && strcmpi(DynareOptions.particle.resampling.status,'generic')) || strcmpi(DynareOptions.particle.resampling.status,'systematic')
         nb_obs_resamp = nb_obs_resamp+1 ;
         StateVectors = tmp(mf0,resample(weights,DynareOptions.particle.resampling.method1,DynareOptions.particle.resampling.method2));
-        weights = ones(1,number_of_particles) ;
+        weights = ones(1,number_of_particles)/number_of_particles ;
     elseif strcmpi(DynareOptions.particle_filter.resampling.status,'none')
         StateVectors = tmp(mf0,:);
-        weights = number_of_particles*weights;
     end
 end