114 lines
4.5 KiB
Matlab
114 lines
4.5 KiB
Matlab
function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R] = DsgeSmoother(xparam1,gend,Y)
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% Estimation of the smoothed variables and innovations.
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%
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% INPUTS
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% o xparam1 [double] (p*1) vector of (estimated) parameters.
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% o gend [integer] scalar specifying the number of observations ==> varargin{1}.
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% o data [double] (T*n) matrix of data.
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%
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% OUTPUTS
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% o alphahat [double] (m*T) matrix, smoothed endogenous variables.
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% o etahat [double] (r*T) matrix, smoothed structural shocks (r>n is the umber of shocks).
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% o epsilonhat [double] (n*T) matrix, smoothed measurement errors.
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% o ahat [double] (m*T) matrix, one step ahead filtered (endogenous) variables.
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% o SteadyState [double] (m*1) vector specifying the steady state level of each endogenous variable.
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% o trend_coeff [double] (n*1) vector, parameters specifying the slope of the trend associated to each observed variable.
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% o aK [double] (K,n,T+K) array, k (k=1,...,K) steps ahead filtered (endogenous) variables.
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% o T and R [double] Matrices defining the state equation (T is the (m*m) transition matrix).
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% ALGORITHM
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% Metropolis-Hastings.
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%
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% SPECIAL REQUIREMENTS
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% None.
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%
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%
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% part of DYNARE, copyright S. Adjemian, M. Juillard (2006)
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% Gnu Public License.
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global bayestopt_ M_ oo_ estim_params_ options_
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alphahat = [];
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epsilonhat = [];
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etahat = [];
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nobs = size(options_.varobs,1);
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smpl = size(Y,2);
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set_all_parameters(xparam1);
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%------------------------------------------------------------------------------
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% 2. call model setup & reduction program
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%------------------------------------------------------------------------------
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[T,R,SteadyState] = dynare_resolve;
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bayestopt_.mf = bayestopt_.mf2;
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if options_.loglinear == 1
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constant = log(SteadyState(bayestopt_.mfys));
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else
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constant = SteadyState(bayestopt_.mfys);
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end
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trend_coeff = zeros(nobs,1);
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if bayestopt_.with_trend == 1
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trend_coeff = zeros(nobs,1);
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nx1 = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn;
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for i=1:nobs
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trend_coeff(i) = evalin('base',bayestopt_.trend_coeff{i});
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end
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trend = constant*ones(1,gend)+trend_coeff*(1:gend);
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else
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trend = constant*ones(1,gend);
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end
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start = options_.presample+1;
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np = size(T,1);
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mf = bayestopt_.mf;
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% ------------------------------------------------------------------------------
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% 3. Initial condition of the Kalman filter
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% ------------------------------------------------------------------------------
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%
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% C'est ici qu'il faut d<>terminer Pinf et Pstar. Si le mod<6F>le est stationnaire,
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% alors il suffit de poser Pstar comme la solution de l'<27>uation de Lyapounov et
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% Pinf=[].
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%
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Q = M_.Sigma_e;
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H = M_.H;
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if options_.lik_init == 1 % Kalman filter
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Pstar = lyapunov_symm(T,R*Q*transpose(R));
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Pinf = [];
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elseif options_.lik_init == 2 % Old Diffuse Kalman filter
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Pstar = 10*eye(np);
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Pinf = [];
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elseif options_.lik_init == 3 % Diffuse Kalman filter
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Pstar = zeros(np,np);
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ivs = bayestopt_.var_list_stationary;
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Pstar(ivs,ivs) = lyapunov_symm(T(ivs,ivs),R(ivs,:)*Q* ...
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transpose(R(ivs,:)));
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% Pinf = bayestopt_.Pinf;
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% by M. Ratto
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RR=T(:,find(~ismember([1:np],ivs)));
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i=find(abs(RR)>1.e-10);
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R0=zeros(size(RR));
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R0(i)=sign(RR(i));
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Pinf=R0*R0';
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% by M. Ratto
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end
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% -----------------------------------------------------------------------------
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% 4. Kalman smoother
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% -----------------------------------------------------------------------------
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if any(any(H ~= 0)) % should be replaced by a flag
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if options_.kalman_algo == 1
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[alphahat,epsilonhat,etahat,ahat,aK] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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if all(alphahat(:)==0)
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[alphahat,epsilonhat,etahat,ahat,aK] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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elseif options_.kalman_algo == 3
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[alphahat,epsilonhat,etahat,ahat,aK] = DiffuseKalmanSmootherH3(T,R,Q,H,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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else
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if options_.kalman_algo == 1
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[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother1(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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if all(alphahat(:)==0)
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[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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elseif options_.kalman_algo == 3
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[alphahat,etahat,ahat,aK] = DiffuseKalmanSmoother3(T,R,Q,Pinf,Pstar,Y,trend,nobs,np,smpl,mf);
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end
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end
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