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when filter is NOT diffuse, allow likelihood to handle violation of constraints in period 1.

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Marco Ratto 2022-11-18 17:36:29 +01:00
parent 6598d615d1
commit a346639020
1 changed files with 77 additions and 49 deletions
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34
matlab/kalman/likelihood/missing_observations_kalman_filter.m
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@ -105,7 +105,11 @@ if occbin_.status
occbin_options=occbin_.info{4};
opts_regime.regime_history = occbin_options.opts_simul.init_regime;
opts_regime.binding_indicator = occbin_options.opts_simul.init_binding_indicator;
if t>1
first_period_occbin_update = max(t+1,options_.occbin.likelihood.first_period_occbin_update);
else
first_period_occbin_update = options_.occbin.likelihood.first_period_occbin_update;
end
if isempty(opts_regime.binding_indicator) && isempty(opts_regime.regime_history)
opts_regime.binding_indicator=zeros(last+2,M_.occbin.constraint_nbr);
end
@ -142,6 +146,10 @@ while notsteady && t<=last
QQ = R*Q*transpose(R); % Variance of R times the vector of structural innovations.
end
end
if t==1
Pinit = P1(:,:,1);
ainit = a1(:,1);
end
s = t-start+1;
d_index = data_index{t};
if isqvec
@ -178,7 +186,6 @@ while notsteady && t<=last
% badly_conditioned_F = true;
end
end
if ~occbin_.status || (occbin_.status && (options_.occbin.likelihood.use_updated_regime==0 || t<first_period_occbin_update))
if badly_conditioned_F && (~occbin_.status || (occbin_.status && t<first_period_occbin_update))
if ~all(abs(F(:))<kalman_tol)
% Use univariate filter.
@ -189,6 +196,8 @@ while notsteady && t<=last
end
else
F_singular = false;
end
if ~occbin_.status || (occbin_.status && (options_.occbin.likelihood.use_updated_regime==0 || t<first_period_occbin_update))
if rescale_prediction_error_covariance
log_dF = log(det(F./(sig*sig')))+2*sum(log(sig));
iF = inv(F./(sig*sig'))./(sig*sig');
@ -225,14 +234,33 @@ while notsteady && t<=last
end
end
end
end
if occbin_.status && t>=first_period_occbin_update
occbin_options.opts_simul.waitbar=0;
if t==1
if isqvec
Qt = cat(3,Q,Qvec(:,:,t:t+1));
end
a00 = ainit;
a10 = [a00 a(:,1)];
P00 = Pinit;
P10 = P1(:,:,[1 1]);
data_index0{1}=[];
data_index0(2)=data_index(1);
v0(:,2)=vv(:,1);
Y0(:,2)=Y(:,1);
Y0(:,1)=nan;
TT01 = cat(3,T,TT(:,:,1));
RR01 = cat(3,R,RR(:,:,1));
CC01 = zeros(size(CC,1),2);
CC01(:,2) = CC(:,1);
[ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regimes_(t:t+2), info, M_, likx] = occbin.kalman_update_algo_1(a00, a10, P00, P10, data_index0, Z, v0, Y0, H, Qt, T0, R0, TT01, RR01, CC01, regimes_(t:t+1), M_, oo_, options_, occbin_options);
else
if isqvec
Qt = Qvec(:,:,t-1:t+1);
end
occbin_options.opts_simul.waitbar=0;
[ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regimes_(t:t+2), info, M_, likx] = occbin.kalman_update_algo_1(a0(:,t-1),a1(:,t-1:t),P0(:,:,t-1),P1(:,:,t-1:t),data_index(t-1:t),Z,vv(:,t-1:t),Y(:,t-1:t),H,Qt,T0,R0,TT(:,:,t-1:t),RR(:,:,t-1:t),CC(:,t-1:t),regimes_(t:t+1),M_,oo_,options_,occbin_options);
end
if info
if options_.debug
fprintf('\nmissing_observations_kalman_filter:PKF failed with: %s\n', get_error_message(info,options_));