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function [alphahat,epsilonhat,etahat,a, aK] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)
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% function [alphahat,epsilonhat,etahat,a, aK] = DiffuseKalmanSmootherH1(T,R,Q,H,Pinf1,Pstar1,Y,trend,pp,mm,smpl,mf)
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% Computes the diffuse kalman smoother with measurement error, in the case of a non-singular var-cov matrix
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%
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% INPUTS
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% T: mm*mm matrix
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% R: mm*rr matrix
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% Q: rr*rr matrix
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% Pinf1: mm*mm diagonal matrix with with q ones and m-q zeros
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% Pstar1: mm*mm variance-covariance matrix with stationary variables
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% Y: pp*1 vector
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% trend
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% pp: number of observed variables
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% mm: number of state variables
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% smpl: sample size
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% mf: observed variables index in the state vector
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%
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% OUTPUTS
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% alphahat: smoothed state variables
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% epsilonhat:smoothed measurement errors
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% etahat: smoothed shocks
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% a: matrix of one step ahead filtered state variables
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% aK: 3D array of k step ahead filtered state variables
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% SPECIAL REQUIREMENTS
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% See "Filtering and Smoothing of State Vector for Diffuse State Space
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% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
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% Analysis, vol. 24(1), pp. 85-98).
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%
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% part of DYNARE, copyright Dynare Team (2004-2008)
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% Gnu Public License.
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% modified by M. Ratto:
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% new output argument aK (1-step to k-step predictions)
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% new options_.nk: the max step ahed prediction in aK (default is 4)
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% new crit1 value for rank of Pinf
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% it is assured that P is symmetric
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%
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% stephane.adjemian@cepremap.cnrs.fr [09-16-2004]
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%
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% See "Filtering and Smoothing of State Vector for Diffuse State Space
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% Models", S.J. Koopman and J. Durbin (2003, in Journal of Time Series
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% Analysis, vol. 24(1), pp. 85-98).
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global options_
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