### Deleted trailing white spaces.

parent 859335b3
 ... @@ -101,7 +101,7 @@ function [fval,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,Dynar ... @@ -101,7 +101,7 @@ function [fval,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,Dynar %! @end deftypefn %! @end deftypefn %@eod: %@eod: % Copyright (C) 2010-2011 Dynare Team % Copyright (C) 2010, 2011, 2012 Dynare Team % % % This file is part of Dynare. % This file is part of Dynare. % % ... @@ -197,8 +197,8 @@ if EstimatedParameters_.ncx ... @@ -197,8 +197,8 @@ if EstimatedParameters_.ncx end end % Try to compute the cholesky decomposition of Q (possible iff Q is positive definite) % Try to compute the cholesky decomposition of Q (possible iff Q is positive definite) [CholQ,testQ] = chol(Q); [CholQ,testQ] = chol(Q); if testQ if testQ % The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty. % The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty. a = diag(eig(Q)); a = diag(eig(Q)); k = find(a < 0); k = find(a < 0); if k > 0 if k > 0 ... @@ -212,7 +212,7 @@ if EstimatedParameters_.ncx ... @@ -212,7 +212,7 @@ if EstimatedParameters_.ncx end end % Get the off-diagonal elements of the covariance matrix for the measurement errors. Test if H is positive definite. % Get the off-diagonal elements of the covariance matrix for the measurement errors. Test if H is positive definite. if EstimatedParameters_.ncn if EstimatedParameters_.ncn for i=1:EstimatedParameters_.ncn for i=1:EstimatedParameters_.ncn k1 = DynareOptions.lgyidx2varobs(EstimatedParameters_.corrn(i,1)); k1 = DynareOptions.lgyidx2varobs(EstimatedParameters_.corrn(i,1)); k2 = DynareOptions.lgyidx2varobs(EstimatedParameters_.corrn(i,2)); k2 = DynareOptions.lgyidx2varobs(EstimatedParameters_.corrn(i,2)); ... @@ -266,8 +266,8 @@ BayesInfo.mf = BayesInfo.mf1; ... @@ -266,8 +266,8 @@ BayesInfo.mf = BayesInfo.mf1; % Define the deterministic linear trend of the measurement equation. % Define the deterministic linear trend of the measurement equation. if DynareOptions.noconstant if DynareOptions.noconstant constant = zeros(nvobs,1); constant = zeros(nvobs,1); else else if DynareOptions.loglinear if DynareOptions.loglinear constant = log(SteadyState(BayesInfo.mfys)); constant = log(SteadyState(BayesInfo.mfys)); else else ... @@ -332,7 +332,7 @@ ReducedForm.mf1 = mf1; ... @@ -332,7 +332,7 @@ ReducedForm.mf1 = mf1; % Set initial condition. % Set initial condition. switch DynareOptions.particle.initialization switch DynareOptions.particle.initialization case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model. case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model. StateVectorMean = ReducedForm.constant(mf0); StateVectorMean = ReducedForm.constant(mf0); StateVectorVariance = lyapunov_symm(ReducedForm.ghx(mf0,:),ReducedForm.ghu(mf0,:)*ReducedForm.Q*ReducedForm.ghu(mf0,:)',1e-12,1e-12); StateVectorVariance = lyapunov_symm(ReducedForm.ghx(mf0,:),ReducedForm.ghu(mf0,:)*ReducedForm.Q*ReducedForm.ghu(mf0,:)',1e-12,1e-12); case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model). case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model). ... ...